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face-api/dist/face-api.esm.js

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t==="string"?Wd(e):Or([e],t)}function JD(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function Or(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=te(e)),oe().getBool("DEBUG")&&Nr(e,t),JD(e,t))return e;if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"){const n=new Uint8Array(e.length);for(let s=0;s<n.length;++s)Math.round(e[s])!==0&&(n[s]=1);return n}else throw new Error(`Unknown data type ${t}`)}function jn(){return oe().platform.now()}function nT(e,t){return oe().platform.fetch(e,t)}function Wd(e,t="utf-8"){return t=t||"utf-8",oe().platform.encode(e,t)}function Kl(e,t="utf-8"){return t=t||"utf-8",oe().platform.decode(e,t)}var ZD=Object.freeze({__proto__:null,createScalarValue:tT,toTypedArray:Or,now:jn,fetch:nT,encodeString:Wd,decodeString:Kl,shuffle:I,clamp:S,nearestLargerEven:T,sum:C,randUniform:D,distSquared:_,assert:A,assertShapesMatch:B,assertNonNull:ne,flatten:te,sizeFromShape:P,isScalarShape:ge,arraysEqual:ae,isInt:Le,tanh:ve,sizeToSquarishShape:Ve,createShuffledIndices:at,rightPad:pt,repeatedTry:$t,inferFromImplicitShape:Vt,parseAxisParam:qe,squeezeShape:ln,getTypedArrayFromDType:bt,getArrayFromDType:ws,checkConversionForErrors:Nr,isValidDtype:Cr,hasEncodingLoss:ba,isTypedArray:hn,bytesPerElement:Bg,bytesFromStringArray:Ix,isString:Yi,isBoolean:xx,isNumber:Qu,inferDtype:wa,isFunction:Rr,nearestDivisor:ed,computeStrides:je,toNestedArray:Ls,makeOnesTypedArray:Mg,makeZerosTypedArray:La,makeZerosNestedTypedArray:Pg,assertNonNegativeIntegerDimensions:zg,locToIndex:_s,indexToLoc:yo,isPromise:bo});class QD{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new tk)}profileKernel(e,t,n){let s;const i=()=>{s=n()},o=this.backendTimer.time(i);for(let c=0;c<s.length;c++){const h=s[c];h.data().then(d=>{ek(d,h.dtype,e)})}const a={kernelName:e,outputs:s,inputs:t,timeMs:o.then(c=>c.kernelMs),extraInfo:o.then(c=>c.getExtraProfileInfo!=null?c.getExtraProfileInfo():"")};return a}logKernelProfile(e){const{kernelName:t,outputs:n,timeMs:s,inputs:i,extraInfo:o}=e;n.forEach(a=>{Promise.all([a.data(),s,o]).then(c=>{this.logger.logKernelProfile(t,a,c[0],c[1],i,c[2])})})}}function ek(e,t,n){if(t!=="float32")return!1;for(let s=0;s<e.length;s++){const i=e[s];if(isNaN(i)||!isFinite(i))return console.warn(`Found ${i} in the result of '${n}'`),!0}return!1}class tk{logKernelProfile(e,t,n,s,i,o){const a=typeof s=="number"?pt(`${s}ms`,9):s.error,c=pt(e,25),h=t.rank,d=t.size,m=pt(t.shape.toString(),14);let f="";for(const b in i){const w=i[b];if(w!=null){const L=w.shape||t.shape,x=L.length;f+=`${b}: ${x}D ${x>0?L:""} `}}console.log(`%c${c} %c${a} %c${h}D ${m} %c${d} %c${f} %c${o}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}}function nk(e,t,n){const s={},i={};for(let h=0;h<t.length;h++)s[t[h].id]=!0;for(let h=0;h<e.length;h++){const d=e[h],m=d.inputs;for(const f in m){const b=m[f];let w=!1;for(let L=0;L<t.length;L++)if(s[b.id]){d.outputs.forEach(x=>s[x.id]=!0),w=!0,i[d.id]=!0;break}if(w)break}}const o={};o[n.id]=!0;const a={};for(let h=e.length-1;h>=0;h--){const d=e[h],m=d.inputs;for(let f=0;f<d.outputs.length;f++)if(o[d.outputs[f].id]){for(const b in m)o[m[b].id]=!0,a[d.id]=!0;break}}const c=[];for(let h=0;h<e.length;h++){const d=e[h];if(i[d.id]&&a[d.id]){const m={};for(const b in d.inputs){const w=d.inputs[b];s[w.id]&&(m[b]=w)}const f=Object.assign({},d);f.inputs=m,f.outputs=d.outputs,c.push(f)}}return c}function sk(e,t,n,s){for(let i=t.length-1;i>=0;i--){const o=t[i],a=[];if(o.outputs.forEach(h=>{const d=e[h.id];d!=null?a.push(d):a.push(null)}),o.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${o.kernelName}.`);const c=o.gradient(a);for(const h in o.inputs){if(!(h in c))throw new Error(`Cannot backprop through input ${h}. Available gradients found: ${Object.keys(c)}.`);const d=n(()=>c[h]());if(d.dtype!=="float32")throw new Error(`Error in gradient for op ${o.kernelName}. The gradient of input ${h} must have 'float32' dtype, but has '${d.dtype}'`);const m=o.inputs[h];if(!ae(d.shape,m.shape))throw new Error(`Error in gradient for op ${o.kernelName}. The gradient of input '${h}' has shape '${d.shape}', which does not match the shape of the input '${m.shape}'`);if(e[m.id]==null)e[m.id]=d;else{const f=e[m.id];e[m.id]=s(f,d),f.dispose()}}}}const sT=20,Xl=3,ky=7;function ik(e,t,n,s){const i=je(t),o=rk(e,t,n,i),a=t.length,c=$d(e,t,n,i,o),h=["Tensor"];return s&&(h.push(` dtype: ${n}`),h.push(` rank: ${a}`),h.push(` shape: [${t}]`),h.push(" values:")),h.push(c.map(d=>" "+d).join(`
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`;for(let x=2;x<h;x++)L+=`
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Buffer shape=${this.shape}`;throw new Error(i)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Si().makeTensor(this.values,this.shape,this.dtype)}}let Si=null,Oa=null,rT=null;function ok(e){Si=e}function ak(e){Oa=e}function ck(e){rT=e}class ee{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=P(e),this.strides=je(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return Oa.buffer(this.shape,this.dtype,e)}bufferSync(){return Oa.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return Ls(this.shape,e)}arraySync(){return Ls(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=Si().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(n=>Kl(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=Si().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Kl(t))}catch(t){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 Si().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;Si().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Oa.print(this,e)}clone(){return this.throwIfDisposed(),Oa.clone(this)}toString(e=!1){const t=this.dataSync();return ik(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Oa.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Si().makeVariable(this,e,t,n)}}Object.defineProperty(ee,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class Ql extends ee{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ae(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Si().disposeTensor(this),this.dataId=e.dataId,Si().incRef(this,null)}dispose(){Si().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Ql,Symbol.hasInstance,{value:e=>e instanceof ee&&e.assign!=null&&e.assign instanceof Function});(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r.Rank||(r.Rank={}));var Fy;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Fy||(Fy={}));var _y;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(_y||(_y={}));var Wy;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Wy||(Wy={}));var $y;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})($y||($y={}));const lk={float32:Wy,int32:Fy,bool:_y,complex64:$y};function $n(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return lk[e][t]}function Ud(e){return $n(e,"int32")}function Gt(e,t){if(e.dtype===t.dtype)return[e,t];const n=$n(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function oT(e,t){A(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Bd(e,t){return t.some(n=>n.id===e.id)}function Hi(e){const t=[],n=new Set;return aT(e,t,n),t}function aT(e,t,n){if(e==null)return;if(e instanceof ee){t.push(e);return}if(!hk(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),aT(o,t,n))}}function hk(e){return Array.isArray(e)||typeof e=="object"}var uk=Object.freeze({__proto__:null,makeTypesMatch:Gt,assertTypesMatch:oT,isTensorInList:Bd,getTensorsInContainer:Hi});class cT{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}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class eh{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new cT}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 n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);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,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),s=n?await t:t;if(!s)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new QD(this.backendInstance),!0}setupRegisteredKernels(){const e=Fd(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=Fd(e);t.forEach(n=>{n.disposeFunc!=null&&n.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 n=t.factory();if(n&&!(n instanceof y)&&typeof n.then=="function"){const s=++this.pendingBackendInitId,i=n.then(o=>s<this.pendingBackendInitId?!1:(this.registry[e]=o,this.pendingBackendInit=null,!0)).catch(o=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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 n=e[t],{success:s,asyncInit:i}=this.initializeBackend(n);if(i||s)return{name:n,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,i=this.readSync(t);s.disposeData(t),n.backend=e,e.move(t,i,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=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");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{const s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return eh.nextTensorId++}nextVariableId(){return eh.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},h={dtype:a};return G.runKernelFunc(d=>d.cast(o,a),c,null,Sa,h)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,s,i,o,a){let c,h=[];const d=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,f=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=Oy(s,this.backendName);let L;if(w!=null)b=()=>{const v=this.backend.numDataIds();L=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const N=Array.isArray(L)?L:[L];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,v,N);const O=N.map(({dataId:E,shape:k,dtype:F})=>this.makeTensorFromDataId(E,k,F));if(d){let E=this.getTensorsForGradient(s,t,O);if(E==null){a==null&&(a=[]);const k=O.filter((F,U)=>a[U]);E=(o||[]).slice().concat(k)}h=this.saveTensorsForBackwardMode(E)}return O};else{const v=N=>{if(!d)return;h=N.map(O=>this.keep(this.clone(O)))};b=()=>{const N=this.backend.numDataIds();L=this.tidy(()=>e(this.backend,v));const O=Array.isArray(L)?L:[L];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,O),O}}let x;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(x=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(x),c=x.outputs)}),d&&this.addTapeNode(s,t,c,n,h,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-f,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(v=>t[v]!=null?t[v].shape:null),outputShapes:c.map(v=>v.shape),kernelTimeMs:x.timeMs,extraInfo:x.extraInfo}),Array.isArray(L)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=Ey(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(h=>t[h])):a=i.map(h=>t[h]);const c=n.filter((h,d)=>o[d]);return a.concat(c)}return null}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let i=e;n==="string"&&Yi(e[0])&&(i=e.map(c=>Wd(c)));const o=s.write(i,t,n),a=new ee(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),h=Ix(i);this.state.numBytes+=h-c.bytes,c.bytes=h}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new ee(t,n,e,this.nextTensorId());return this.incRef(i,s),i}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const i=new Ql(e,t,n,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let s=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(s=e.size*Bg(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Ql||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}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,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;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,n,s,i,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:i},c=Ey(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=h=>(h=h.map((d,m)=>{if(d==null){const f=n[m],b=La(f.size,f.dtype);return this.makeTensor(b,f.shape,f.dtype)}return d}),s(h.length>1?h:h[0],i,o))),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 t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=Hi(e),n=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const o=this.state.activeScope.track[i];!o.kept&&!n.has(o.id)&&o.dispose()}const s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===s.id&&this.track(i)})}gradients(e,t,n,s=!1){if(A(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));A(i instanceof ee,()=>"The result y returned by f() must be a tensor.");const o=nk(this.state.activeTape,t,i);if(!s&&o.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{const a={};a[i.id]=n==null?dk(i.shape):n,sk(a,o,h=>this.tidy(h),pk);const c=t.map(h=>a[h.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(h=>{for(const d of h.saved)d.dispose()}),this.state.activeTape=null),{value:i,grads:c}})}customGrad(e){return A(Rr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{A(t.every(i=>i instanceof ee),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n;const s={};return t.forEach((i,o)=>{s[o]=i}),this.runKernelFunc((i,o)=>(n=e(...t,o),A(n.value instanceof ee,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),A(Rr(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s,(i,o)=>{const a=n.gradFunc(i,o),c=Array.isArray(a)?a:[a];A(c.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),A(c.every(d=>d instanceof ee),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");const h={};return c.forEach((d,m)=>{h[m]=()=>d}),h})}}readSync(e){const t=this.state.tensorInfo.get(e);return t.backend.readSync(e)}read(e){const t=this.state.tensorInfo.get(e);return t.backend.read(e)}async time(e){const t=jn(),n=await this.backend.time(e);return n.wallMs=jn()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new cT;for(const e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}eh.nextTensorId=0,eh.nextVariableId=0;function dk(e){const t=Mg(P(e),"float32");return G.makeTensor(t,e,"float32")}function lT(){const e=Nx();if(e._tfengine==null){const t=new vx(e);e._tfengine=new eh(t)}return YD(e._tfengine.ENV),ok(()=>e._tfengine),e._tfengine}const G=lT();function pk(e,t){const n={a:e,b:t};return G.runKernelFunc((s,i)=>{const o=s.add(e,t);return i([e,t]),o},n,null,wo)}function mk(){return typeof navigator!="undefined"&&navigator!=null}function hT(){if(mk()){const e=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}return!1}function Uy(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var fk=Object.freeze({__proto__:null,isMobile:hT,isBrowser:Uy});const qi=oe();qi.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")}),qi.registerFlag("IS_BROWSER",()=>Uy()),qi.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined"),qi.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)),qi.registerFlag("PROD",()=>!1),qi.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>qi.getBool("DEBUG")),qi.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0),qi.registerFlag("IS_TEST",()=>!1);function Ii(e,t){let n=e;if(hn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];const s=[];for(;Array.isArray(n)||hn(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&oe().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&uT(e,s,[]),s}function uT(e,t,n){if(n=n||[],!Array.isArray(e)&&!hn(e)){A(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}A(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),A(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);const s=t.slice(1);for(let i=0;i<e.length;++i)uT(e[i],s,n.concat(i))}function dT(e,t,n,s){if(e==null)return;if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}function W(e,t,n,s="numeric"){if(e instanceof ee)return dT(s,e.dtype,t,n),e;let i=wa(e);if(i!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(i=s),dT(s,i,t,n),e==null||!hn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){const h=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${h}'`)}const o=Ii(e,i);!hn(e)&&!Array.isArray(e)&&(e=[e]);const a=!0,c=i!=="string"?Or(e,i):te(e,[],a);return G.makeTensor(c,o,i)}function th(e,t,n,s="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);const i=e;return i.map((o,a)=>W(o,`${t}[${a}]`,n),s)}const pT="__op";function z(e){const t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let n=t[0];const s=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+pT;const i=(...o)=>{G.startScope(n);try{const a=s(...o);return bo(a)&&console.error("Cannot return a Promise inside of tidy."),G.endScope(a),a}catch(a){throw G.endScope(null),a}};return Object.defineProperty(i,"name",{value:n,configurable:!0}),i}function gk(e,t){const n=W(e,"real","complex"),s=W(t,"imag","complex");B(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);const i=a=>a.complex(n,s),o={real:n,imag:s};return G.runKernelFunc(i,o,null,rd)}const ji=z({complex_:gk});function Er(e,t,n,s){if(s==null&&(s=wa(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!hn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){zg(t);const i=P(t),o=P(n);A(i===o,()=>`Based on the provided shape, [${t}], the tensor should have ${i} values but has ${o}`);for(let a=0;a<n.length;++a){const c=n[a],h=a===n.length-1?c!==P(t.slice(a)):!0;A(n[a]===t[a]||!h,()=>`Error creating a new Tensor. 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Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=By[f.dtype],w=e.slice(i,i+d*b),L=f.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(f.dtype==="uint8"||f.dtype==="uint16"){m=new Float32Array(L.length);for(let x=0;x<L.length;x++){const v=L[x];m[x]=v*f.scale+f.min}}else if(f.dtype==="float16")s===void 0&&(s=xk()),m=s(L);else throw new Error(`Unsupported quantization type ${f.dtype} for weight type float32.`);else if(c==="int32"){if(f.dtype!=="uint8"&&f.dtype!=="uint16")throw new Error(`Unsupported quantization type ${f.dtype} for weight type int32.`);m=new Int32Array(L.length);for(let x=0;x<L.length;x++){const v=L[x];m[x]=Math.round(v*f.scale+f.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*b}else if(c==="string"){const f=P(o.shape);m=[];for(let b=0;b<f;b++){const w=new Uint32Array(e.slice(i,i+Md))[0];i+=Md;const L=new Uint8Array(e.slice(i,i+w));m.push(L),i+=w}}else{const f=By[c],b=e.slice(i,i+d*f);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),L=new Float32Array(m.length/2);for(let N=0;N<w.length;N++)w[N]=m[N*2],L[N]=m[N*2+1];const x=sn(w,h,"float32"),v=sn(L,h,"float32");n[a]=ji(x,v),x.dispose(),v.dispose()}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*f}c!=="complex64"&&(n[a]=sn(m,h,c))}return n}function yk(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0;const n=[];e.forEach(o=>{if(t+=o.byteLength,n.push(o.byteLength===o.buffer.byteLength?o:new o.constructor(o)),!(o instanceof Float32Array||o instanceof Int32Array||o instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${o.constructor.name}`)});const s=new Uint8Array(t);let i=0;return n.forEach(o=>{s.set(new Uint8Array(o.buffer),i),i+=o.byteLength}),s.buffer}const Py=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function mT(e){return Py?Buffer.byteLength(e):new Blob([e]).size}function bk(e){if(Py)return Buffer.from(e).toString("base64");const t=new Uint8Array(e);let n="";for(let s=0,i=t.length;s<i;s++)n+=String.fromCharCode(t[s]);return btoa(n)}function wk(e){if(Py){const s=Buffer.from(e,"base64");return s.buffer.slice(s.byteOffset,s.byteOffset+s.byteLength)}const t=atob(e),n=new Uint8Array(t.length);for(let s=0;s<t.length;++s)n.set([t.charCodeAt(s)],s);return n.buffer}function zd(e){if(e.length===1)return e[0];let t=0;e.forEach(i=>{t+=i.byteLength});const n=new Uint8Array(t);let s=0;return e.forEach(i=>{n.set(new Uint8Array(i),s),s+=i.byteLength}),n.buffer}function fT(e){const t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);const n=e.split(t);return n[n.length-1]}function nh(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:e.modelTopology==null?0:mT(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:mT(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function Lk(){const e=n=>{let s=n<<13,i=0;for(;(s&8388608)===0;)i-=8388608,s<<=1;return s&=~8388608,i+=947912704,s|i},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let n=1024;n<2048;n++)t[n]=939524096+(n-1024<<13);return t}function Sk(){const e=new Uint32Array(64);e[0]=0,e[31]=1199570944,e[32]=2147483648,e[63]=3347054592;for(let t=1;t<31;t++)e[t]=t<<23;for(let t=33;t<63;t++)e[t]=2147483648+(t-32<<23);return e}function Ik(){const e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function xk(){const e=Lk(),t=Sk(),n=Ik();return s=>{const i=new ArrayBuffer(4*s.length),o=new Uint32Array(i);for(let a=0;a<s.length;a++){const c=s[a],h=e[n[c>>10]+(c&1023)]+t[c>>10];o[a]=h}return new Float32Array(i)}}class en{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return en.instance==null&&(en.instance=new en),en.instance}static registerSaveRouter(e){en.getInstance().saveRouters.push(e)}static registerLoadRouter(e){en.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return en.getHandlers(e,"save")}static getLoadHandlers(e,t){return en.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?en.getInstance().loadRouters:en.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const Tk=e=>en.registerSaveRouter(e),Ak=e=>en.registerLoadRouter(e),zy=e=>en.getSaveHandlers(e),Vy=(e,t)=>en.getLoadHandlers(e,t);const Vd="tensorflowjs",Gy=1,Lo="models_store",Dr="model_info_store";async function bee(){const e=Yy();return new Promise((t,n)=>{const s=e.deleteDatabase(Vd);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function Yy(){if(!oe().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function Hy(e){const t=e.result;t.createObjectStore(Lo,{keyPath:"modelPath"}),t.createObjectStore(Dr,{keyPath:"modelPath"})}class So{constructor(e){if(this.indexedDB=Yy(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,s)=>{const i=this.indexedDB.open(Vd,Gy);i.onupgradeneeded=()=>Hy(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(Lo,"readonly"),c=a.objectStore(Lo),h=c.get(this.modelPath);h.onsuccess=()=>{if(h.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(h.result.modelArtifacts)},h.onerror=d=>(o.close(),s(h.error)),a.oncomplete=()=>o.close()}else{const a=nh(t),c=o.transaction(Dr,"readwrite");let h=c.objectStore(Dr);const d=h.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;d.onsuccess=()=>{m=o.transaction(Lo,"readwrite");const f=m.objectStore(Lo),b=f.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{h=c.objectStore(Dr);const L=h.delete(this.modelPath);L.onsuccess=()=>(o.close(),s(b.error)),L.onerror=x=>(o.close(),s(b.error))}},d.onerror=f=>(o.close(),s(d.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}So.URL_SCHEME="indexeddb://";const gT=e=>oe().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(So.URL_SCHEME))?vk(e.slice(So.URL_SCHEME.length)):null;en.registerSaveRouter(gT),en.registerLoadRouter(gT);function vk(e){return new So(e)}function Nk(e){return e.startsWith(So.URL_SCHEME)?e.slice(So.URL_SCHEME.length):e}class Ck{constructor(){this.indexedDB=Yy()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(Vd,Gy);n.onupgradeneeded=()=>Hy(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(Dr,"readonly"),o=i.objectStore(Dr),a=o.getAll();a.onsuccess=()=>{const c={};for(const h of a.result)c[h.modelPath]=h.modelArtifactsInfo;e(c)},a.onerror=c=>(s.close(),t(a.error)),i.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async removeModel(e){return e=Nk(e),new Promise((t,n)=>{const s=this.indexedDB.open(Vd,Gy);s.onupgradeneeded=()=>Hy(s),s.onsuccess=()=>{const i=s.result,o=i.transaction(Dr,"readwrite"),a=o.objectStore(Dr),c=a.get(e);let h;c.onsuccess=()=>{if(c.result==null)return i.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const d=a.delete(e),m=()=>{h=i.transaction(Lo,"readwrite");const f=h.objectStore(Lo),b=f.delete(e);b.onsuccess=()=>t(c.result.modelArtifactsInfo),b.onerror=w=>n(c.error)};d.onsuccess=m,d.onerror=f=>(m(),i.close(),n(c.error))}},c.onerror=d=>(i.close(),n(c.error)),o.oncomplete=()=>{h==null?i.close():h.oncomplete=()=>i.close()}},s.onerror=i=>n(s.error)})}}const xi="/",Io="tensorflowjs_models",yT="info",Rk="model_topology",Ok="weight_specs",Ek="weight_data",Dk="model_metadata";function wee(){if(!oe().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("purgeLocalStorageModels() cannot proceed because local storage is unavailable in the current environment.");const e=window.localStorage,t=[];for(let n=0;n<e.length;++n){const s=e.key(n),i=Io+xi;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=wT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function bT(e){return{info:[Io,e,yT].join(xi),topology:[Io,e,Rk].join(xi),weightSpecs:[Io,e,Ok].join(xi),weightData:[Io,e,Ek].join(xi),modelMetadata:[Io,e,Dk].join(xi)}}function wT(e){const t=e.split(xi);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(xi)}function kk(e){return e.startsWith(xo.URL_SCHEME)?e.slice(xo.URL_SCHEME.length):e}class xo{constructor(e){if(!oe().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=bT(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=nh(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,bk(e.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,userDefinedMetadata:e.userDefinedMetadata})),{modelArtifactsInfo:s}}catch(i){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){const e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");const t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;const s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;const i=this.LS.getItem(this.keys.modelMetadata);if(i!=null){const a=JSON.parse(i);t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy,t.userDefinedMetadata=a.userDefinedMetadata}const o=this.LS.getItem(this.keys.weightData);if(o==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=wk(o),t}}xo.URL_SCHEME="localstorage://";const LT=e=>oe().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(xo.URL_SCHEME))?Fk(e.slice(xo.URL_SCHEME.length)):null;en.registerSaveRouter(LT),en.registerLoadRouter(LT);function Fk(e){return new xo(e)}class _k{constructor(){A(oe().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),A(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=Io+xi,n=xi+yT;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=wT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=kk(e);const t=bT(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);const n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}}const Ea="://";class Ss{constructor(){this.managers={}}static getInstance(){return Ss.instance==null&&(Ss.instance=new Ss),Ss.instance}static registerManager(e,t){A(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Ea)&&(e=e.slice(0,e.indexOf(Ea))),A(e.length>0,()=>"scheme must not be an empty string.");const n=Ss.getInstance();A(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function Gd(e){if(e.indexOf(Ea)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Ss.getSchemes().join(",")}`);return{scheme:e.split(Ea)[0],path:e.split(Ea)[1]}}async function ST(e,t,n=!1){A(e!==t,()=>`Old path and new path are the same: '${e}'`);const s=en.getLoadHandlers(e);A(s.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),A(s.length<2,()=>`Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);const i=s[0],o=en.getSaveHandlers(t);A(o.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),A(o.length<2,()=>`Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);const a=o[0],c=Gd(e).scheme,h=Gd(e).path,d=c===Gd(e).scheme,m=await i.load();n&&d&&await Ss.getManager(c).removeModel(h);const f=await a.save(m);return n&&!d&&await Ss.getManager(c).removeModel(h),f.modelArtifactsInfo}async function Wk(){const e=Ss.getSchemes(),t={};for(const n of e){const s=await Ss.getManager(n).listModels();for(const i in s){const o=n+Ea+i;t[o]=s[i]}}return t}async function $k(e){const t=Gd(e),n=Ss.getManager(t.scheme);return n.removeModel(t.path)}async function Uk(e,t){const n=!1;return ST(e,t,n)}async function Bk(e,t){const n=!0;return ST(e,t,n)}class Mk{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}}if(oe().get("IS_BROWSER")){oe().setPlatform("browser",new Mk);try{Ss.registerManager(xo.URL_SCHEME,new _k)}catch(e){}try{Ss.registerManager(So.URL_SCHEME,new Ck)}catch(e){}}const Pk={importFetch:()=>ZC()};let Da;function Lee(){Da=null}function See(e){Da=e}function Iee(){return Da}class zk{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return 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Please use float32 or int32 tensors.`);const a=await n.data(),c=n.dtype==="float32"?255:1,h=new Uint8ClampedArray(i*s*4);for(let d=0;d<s*i;++d){const m=[0,0,0,255];for(let b=0;b<o;b++){const w=a[d*o+b];if(n.dtype==="float32"){if(w<0||w>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${w}.`)}else if(n.dtype==="int32"&&(w<0||w>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${w}.`);o===1?(m[0]=w*c,m[1]=w*c,m[2]=w*c):m[b]=w*c}const f=d*4;h[f+0]=Math.round(m[0]),h[f+1]=Math.round(m[1]),h[f+2]=Math.round(m[2]),h[f+3]=Math.round(m[3])}if(t!=null){t.width=i,t.height=s;const d=t.getContext("2d"),m=new ImageData(h,i,s);d.putImageData(m,0,0)}return n!==e&&n.dispose(),h}const OT=z({fromPixels_:pF});var fF=Object.freeze({__proto__:null,toPixels:mF,fromPixels:OT});function Hd(e,t){if(e.rank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${e.rank}.`);if(t.rank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[t.rank-1]>e.rank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[t.rank-1]} vs. ${e.rank}`);if(e.size===0)throw new Error(`Requested more than 0 entries, but input is empty. 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aM={fft:Lh,ifft:qa,rfft:Sh,irfft:xp},cM={hammingWindow:rB,hannWindow:UA,frame:BA,stft:lB},zr={flipLeftRight:pB,resizeNearestNeighbor:zA,resizeBilinear:PA,rotateWithOffset:fB,cropAndResize:uB,nonMaxSuppression:yB,nonMaxSuppressionAsync:AB,nonMaxSuppressionWithScore:NB,nonMaxSuppressionWithScoreAsync:RB,nonMaxSuppressionPadded:EB,nonMaxSuppressionPaddedAsync:kB},GA={bandPart:$B,gramSchmidt:BB,qr:PB},lM={absoluteDifference:GB,computeWeightedLoss:Qi,cosineDistance:HB,hingeLoss:jB,huberLoss:XB,logLoss:ZB,meanSquaredError:eM,sigmoidCrossEntropy:sM,softmaxCrossEntropy:oM};class er extends Ao{minimize(e,t=!1,n){const{value:s,grads:i}=this.computeGradients(e,n);if(n!=null){const o=n.map(a=>({name:a.name,tensor:i[a.name]}));this.applyGradients(o)}else this.applyGradients(i);return He(i),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 Cb(e,t)}dispose(){this.iterations_!=null&&He(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ce(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(er,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class Th extends er{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=G.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=G.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:Q(()=>et(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:Q(()=>et(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,h=this.accumulatedUpdates[s].variable;Q(()=>{const d=be(X(c,this.rho),X(At(a),1-this.rho)),m=X(We(Nn(be(h,this.epsilon)),Nn(be(c,this.epsilon))),a),f=be(X(h,this.rho),X(At(m),1-this.rho));c.assign(d),h.assign(f);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(He(this.accumulatedGrads.map(e=>e.variable)),He(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}Th.className="Adadelta",fe(Th);class Ah extends er{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=G.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:Q(()=>Ba(i.shape,this.initialAccumulatorValue).variable(c))}}const o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;const a=this.accumulatedGrads[s].variable;Q(()=>{const c=be(a,At(o));a.assign(c);const h=be(X(We(o,Nn(be(c,G.backend.epsilon()))),-this.learningRate),i);i.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&He(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(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}Ah.className="Adagrad",fe(Ah);class vh extends er{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Q(()=>{this.accBeta1=Ce(t).variable(),this.accBeta2=Ce(n).variable()}),s==null&&(this.epsilon=G.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);Q(()=>{const n=Re(1,this.accBeta1),s=Re(1,this.accBeta2);t.forEach((i,o)=>{const a=G.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:Q(()=>et(a).variable(c))}),this.accumulatedSecondMoment[o]==null&&(this.accumulatedSecondMoment[o]={originalName:`${i}/v`,variable:Q(()=>et(a).variable(c))});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedSecondMoment[o].variable,f=be(X(d,this.beta1),X(h,1-this.beta1)),b=be(X(m,this.beta2),X(At(h),1-this.beta2)),w=We(f,n),L=We(b,s);d.assign(f),m.assign(b);const x=be(X(We(w,be(Nn(L),this.epsilon)),-this.learningRate),a);a.assign(x)}),this.accBeta1.assign(X(this.accBeta1,this.beta1)),this.accBeta2.assign(X(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&He(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),Q(()=>{this.accBeta1.assign(Zs(this.beta1,this.iterations_+1)),this.accBeta2.assign(Zs(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}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)}}vh.className="Adam",fe(vh);class Nh extends er{constructor(e,t,n,s=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Q(()=>{this.iteration=Ce(0).variable(),this.accBeta1=Ce(t).variable()}),s==null&&(this.epsilon=G.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);Q(()=>{const n=Re(1,this.accBeta1),s=We(-this.learningRate,be(X(this.iteration,this.decay),1));t.forEach((i,o)=>{const a=G.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:et(a).variable(c)}),this.accumulatedWeightedInfNorm[o]==null&&(this.accumulatedWeightedInfNorm[o]={originalName:`${i}/v`,variable:et(a).variable(c)});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedWeightedInfNorm[o].variable,f=be(X(d,this.beta1),X(h,1-this.beta1)),b=X(m,this.beta2),w=dn(h),L=$s(b,w);d.assign(f),m.assign(L);const x=be(X(We(s,n),We(f,be(L,this.epsilon))),a);a.assign(x)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(X(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&He(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)}}Nh.className="Adamax",fe(Nh);class Ja extends er{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;const o=G.registeredVariables[n];Q(()=>{const a=be(X(this.c,i),o);o.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=bn(Ce(-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)}}Ja.className="SGD",fe(Ja);class Ch extends Ja{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ce(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=G.registeredVariables[n];if(this.accumulations[s]==null){const c=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:Q(()=>et(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;Q(()=>{let c;const h=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(h,this.m))),i):c=be(X(this.c,h),i),o.assign(h),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&He(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(n=>({originalName:n.name,variable:n.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)}}Ch.className="Momentum",fe(Ch);class Rh extends er{constructor(e,t=.9,n=0,s=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,s==null&&(this.epsilon=G.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=G.registeredVariables[n],o=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:Q(()=>et(i).variable(o))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:Q(()=>et(i).variable(o))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:Q(()=>et(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedMeanSquares[s].variable,h=this.accumulatedMoments[s].variable;Q(()=>{const d=be(X(c,this.decay),X(At(a),1-this.decay));if(this.centered){const m=this.accumulatedMeanGrads[s].variable,f=be(X(m,this.decay),X(a,1-this.decay)),b=We(X(a,this.learningRate),Nn(Re(d,be(At(f),this.epsilon)))),w=be(X(h,this.momentum),b);c.assign(d),m.assign(f),h.assign(w);const L=Re(i,w);i.assign(L)}else{const m=be(X(c,this.decay),X(At(a),1-this.decay)),f=be(X(h,this.momentum),We(X(a,this.learningRate),Nn(be(m,this.epsilon))));c.assign(m),h.assign(f);const b=Re(i,f);i.assign(b)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&He(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&He(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&He(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,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}Rh.className="RMSProp",fe(Rh);class _o{static sgd(e){return new Ja(e)}static momentum(e,t,n=!1){return new Ch(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,i=!1){return new Rh(e,t,n,s,i)}static adam(e=.001,t=.9,n=.999,s=null){return new vh(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new Th(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,i=0){return new Nh(e,t,n,s,i)}static adagrad(e,t=.1){return new Ah(e,t)}}const Wo={sgd:_o.sgd,momentum:_o.momentum,adadelta:_o.adadelta,adagrad:_o.adagrad,rmsprop:_o.rmsprop,adamax:_o.adamax,adam:_o.adam};const hM=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function _p(){return new Promise(e=>hM(()=>e()))}function Jb(e,t,n){const s=n*(typeof e=="number"?e:e[0]),i=t*(typeof e=="number"?e:e[1]);return[s,i]}function Oh(e,t,n,s=!0){let i=[];if(s)i=i.concat(t.slice(0)),i.push(e[0]/n),i=i.concat(e.slice(1));else{i=i.concat(e[0]);const o=t.length;for(let a=0;a<o;++a)i=i.concat([e[a+1]/t[a],t[a]]);i=i.concat(e.slice(o+1))}return i}function Eh(e,t,n=!0){const s=[];if(n){s.push(t);for(let i=t+1;i<e;++i)i<=2*t?(s.push(i),s.push(i-(t+1))):s.push(i)}else{const i=[],o=[];for(let a=1;a<e;++a)a>=t*2+1||a%2===1?o.push(a):i.push(a);s.push(...i),s.push(0),s.push(...o)}return s}function Dh(e,t,n,s=!0){const i=[];s?i.push(e[0]/n):i.push(e[0]*n);for(let o=1;o<e.length;++o)o<=t.length?s?i.push(t[o-1]*e[o]):i.push(e[o]/t[o-1]):i.push(e[o]);return i}function Zb(e,t){const n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function Qb(e,t,n){const s=e.slice(0,1);for(let i=0;i<n;++i)s.push(e[i+1]-t[i][0]-t[i][1]);return s}const Wp=1.7580993408473768,$p=1.0507009873554805;const ew=.3275911,tw=.254829592,nw=-.284496736,sw=1.421413741,iw=-1.453152027,rw=1.061405429;function Za(...e){oe().getBool("IS_TEST")||console.warn(...e)}function uM(...e){oe().getBool("IS_TEST")||console.log(...e)}function tr(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);const n=new Float32Array(e.length*2);for(let s=0;s<n.length;s+=2)n[s]=e[s/2],n[s+1]=t[s/2];return n}function YA(e){const t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let s=0;s<e.length;s+=2)t[s/2]=e[s],n[s/2]=e[s+1];return{real:t,imag:n}}function HA(e){const t=Math.ceil(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let i=0;i<e.length;i+=4)n[Math.floor(i/4)]=e[i],s[Math.floor(i/4)]=e[i+1];return{real:n,imag:s}}function qA(e){const t=Math.floor(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let i=2;i<e.length;i+=4)n[Math.floor(i/4)]=e[i],s[Math.floor(i/4)]=e[i+1];return{real:n,imag:s}}function ow(e,t){const n=e[t*2],s=e[t*2+1];return{real:n,imag:s}}function jA(e,t,n,s){e[s*2]=t,e[s*2+1]=n}function KA(e,t){const n=new Float32Array(e/2),s=new Float32Array(e/2);for(let i=0;i<Math.ceil(e/2);i++){const o=(t?2:-2)*Math.PI*(i/e);n[i]=Math.cos(o),s[i]=Math.sin(o)}return{real:n,imag:s}}function XA(e,t,n){const s=(n?2:-2)*Math.PI*(e/t),i=Math.cos(s),o=Math.sin(s);return{real:i,imag:o}}function dM(e,t,n){if(t==="complex64"){if(e.dtype==="complex64")return e.clone();const s=dt(e.shape),i=Ae(e,"float32"),o=n.complex(i,s);return s.dispose(),i.dispose(),o}if(!ba(e.dtype,t))return G.makeTensorFromDataId(e.dataId,e.shape,t);if(e.dtype==="complex64"){const s=n.real(e),i=Ae(s,t);return s.dispose(),i}if(t==="int32")return n.int(e);if(t==="bool"){const s=Ce(0,e.dtype),i=n.notEqual(e,s);return s.dispose(),i}else throw new Error(`Error in Cast: failed to cast ${e.dtype} to ${t}`)}function JA(e,t){return G.makeTensorFromDataId(e.dataId,t,e.dtype)}function aw(e,t,n){const s=(t-e)/(n-1),i=La(n,"float32");i[0]=e;for(let o=1;o<i.length;o++)i[o]=i[o-1]+s;return ls(i,"float32")}var 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expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return s.length===1?t.reshape([1,s[0],1,1,1]):t.reshape([1,s[3],s[0],s[1],s[2]]);if(n==="channelsLast")return s.length===1?t.reshape([1,1,1,1,s[0]]):t.reshape([1].concat(s))}else if(e===4){if(n==="channelsFirst")return s.length===1?t.reshape([1,s[0],1,1]):t.reshape([1,s[2],s[0],s[1]]);if(n==="channelsLast")return s.length===1?t.reshape([1,1,1,s[0]]):t.reshape([1].concat(s))}else if(e===3){if(n==="channelsFirst")return s.length===1?t.reshape([1,s[0],1]):t.reshape([1,s[1],s[0]]);if(n==="channelsLast")return s.length===1?t.reshape([1,1,s[0]]):t.reshape([1].concat(s))}else if(e<3)return t;throw new q(`Unsupported input rank by biasAdd: ${t.rank}`)}function Ri(e,t,n){return Q(()=>(n==null&&(n=ei()),jt(n),e.add(xw(e.rank,t,n))))}function Dz(e,t=1){if(t!==1)throw new Pe(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Ua(e)}function kz(e){return Q(()=>We(e,dn(e).add(1)))}function wv(e,t,n,s){return Q(()=>kA(e,t,n,s))}function Fz(e){return Q(()=>{const t=be(.5,X(.2,e));return Jn(t,0,1)})}function Bh(e,t,n=!1){return n?e():t()}const _z=["fanIn","fanOut","fanAvg"],Wz=["normal","uniform","truncatedNormal"],Vee=["Zeros","Ones","Constant","RandomNormal","RandomUniform","TruncatedNormal","VarianceScaling","Orthogonal","Identity"];function $z(e){Qa(_z,"FanMode",e)}function Uz(e){Qa(Wz,"Distribution",e)}class Ms extends Ao{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}}class Tw extends Ms{apply(e,t){return dt(e,t)}}Tw.className="Zeros",fe(Tw);class Vp extends Ms{apply(e,t){return Js(e,t)}}Vp.className="Ones",fe(Vp);class Aw extends Ms{constructor(e){super();if(typeof e!="object")throw new q(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new q(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return Q(()=>X(Ce(this.value),Js(e,t)))}getConfig(){return{value:this.value}}}Aw.className="Constant",fe(Aw);class vw extends Ms{constructor(e){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=e.minval||this.DEFAULT_MINVAL,this.maxval=e.maxval||this.DEFAULT_MAXVAL,this.seed=e.seed}apply(e,t){return ko(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}}vw.className="RandomUniform",fe(vw);class Nw extends Ms{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Pe(`randomNormal does not support dType ${t}.`);return zp(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}}Nw.className="RandomNormal",fe(Nw);class Cw extends Ms{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Pe(`truncatedNormal does not support dType ${t}.`);return xh(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}}Cw.className="TruncatedNormal",fe(Cw);class Rw extends Ms{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return Q(()=>{if(e.length!==2||e[0]!==e[1])throw new q("Identity matrix initializer can only be used for 2D square matrices.");return X(this.gain,cp(e[0]))})}getConfig(){return{gain:this.gain}}}Rw.className="Identity",fe(Rw);function Bz(e,t="channelsLast"){let n,s;if(jt(t),e.length===2)n=e[0],s=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){const i=Gr(e,2);n=e[1]*i,s=e[0]*i}else if(t==="channelsLast"){const i=Gr(e,0,e.length-2);n=e[e.length-2]*i,s=e[e.length-1]*i}}else{const i=Gr(e);n=Math.sqrt(i),s=Math.sqrt(i)}return[n,s]}class ns extends Ms{constructor(e){super();if(e.scale<0)throw new q(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,$z(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,Uz(this.distribution),this.seed=e.seed}apply(e,t){const n=Bz(e),s=n[0],i=n[1];let o=this.scale;if(this.mode==="fanIn"?o/=Math.max(1,s):this.mode==="fanOut"?o/=Math.max(1,i):o/=Math.max(1,(s+i)/2),this.distribution==="normal"){const a=Math.sqrt(o);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Pe(`${this.getClassName()} does not support dType ${t}.`);return xh(e,0,a,t,this.seed)}else{const a=Math.sqrt(3*o);return ko(e,-a,a,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}}ns.className="VarianceScaling",fe(ns);class Gp extends ns{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ns.className}}Gp.className="GlorotUniform",fe(Gp);class Yp extends ns{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ns.className}}Yp.className="GlorotNormal",fe(Yp);class Hp extends ns{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ns.className}}Hp.className="HeNormal",fe(Hp);class qp extends ns{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ns.className}}qp.className="HeUniform",fe(qp);class jp extends ns{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return ns.className}}jp.className="LeCunNormal",fe(jp);class Kp extends ns{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return ns.className}}Kp.className="LeCunNormal",fe(Kp);class Ow extends Ms{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Pe("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return Q(()=>{if(e.length<2)throw new Pe("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);const n=e[0]>e[1]?[e[1],e[0]]:e,s=zp(n,0,1,"float32");let i=GA.gramSchmidt(s);return e[0]>e[1]&&(i=i.transpose()),X(this.gain,i)})}getConfig(){return{gain:this.gain,seed:this.seed}}}Ow.className="Orthogonal",fe(Ow);const Lv={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function Sv(e,t={}){return kh(e,Ws.getMap().classNameMap,t,"initializer")}function Kt(e){return dw(e)}function Pt(e){if(typeof e=="string"){const t=e in Lv?Lv[e]:e;if(t==="GlorotNormal")return new Yp;if(t==="GlorotUniform")return new Gp;if(t==="HeNormal")return new Hp;if(t==="HeUniform")return new qp;if(t==="LeCunNormal")return new jp;if(t==="LeCunUniform")return new Kp;{const n={};return n.className=t,n.config={},Sv(n)}}else return e instanceof Ms?e:Sv(e)}function Mz(){return new Tw}function Pz(){return new Vp}function zz(e){return new Aw(e)}function Vz(e){return new vw(e)}function Gz(e){return new Nw(e)}function Yz(e){return new Cw(e)}function Hz(e){return new Rw(e)}function qz(e){return new ns(e)}function jz(e){return new Gp(e)}function Kz(e){return new Yp(e)}function Xz(e){return new Hp(e)}function Jz(e){return new qp(e)}function Zz(e){return new jp(e)}function Qz(e){return new Kp(e)}function e3(e){return new Ow(e)}var t3=Object.freeze({__proto__:null,zeros:Mz,ones:Pz,constant:zz,randomUniform:Vz,randomNormal:Gz,truncatedNormal:Yz,identity:Hz,varianceScaling:qz,glorotUniform:jz,glorotNormal:Kz,heNormal:Xz,heUniform:Jz,leCunNormal:Zz,leCunUniform:Qz,orthogonal:e3});let n3=0;function Iv(){return n3++}const Xp={};function Jp(e=""){return e in Xp||(Xp[e]=0),Xp[e]+=1,e+Xp[e].toString()}function Ew(e){return Array.isArray(e)&&Array.isArray(e[0])}function Zp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Xe(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new q(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function Nt(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new q(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Qp(e){let t=0;for(const n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((s,i)=>s*i);return t}const xv="Variable";class si{constructor(e,t="float32",n=xv,s=!0,i=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=Iv(),n=n==null?xv:n,this.originalName=pv(n),this.name=mv(this.originalName),this.trainable_=s,this.constraint=i,this.val=mA(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),s3(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function s3(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function Gee(e,t,n,s){return new si(e,t,n,!0,s)}function Yee(e,t,n){return new si(dt(e),t,n)}function Hee(e,t,n){return new si(et(e),t,n)}function qee(e,t,n){const s=Js(e);return new si(s,t,n)}function jee(e,t,n){const s=Fn(e);return new si(s,t,n)}function Kee(e,t,n){return new si(cp(e),t,n)}function Xee(e,t,n,s,i,o="randomUniform"){return new si(ko(e,t,n,s),s,o)}function Jee(e,t=0,n=1,s,i,o="truncatedNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new Pe(`randomNormal does not support dType ${s}.`);return new si(xh(e,t,n,s,i),s,o)}function Zee(e,t=0,n=1,s,i,o="randomNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new Pe(`randomNormalVariable does not support dType ${s}.`);return new si(Fb(e,t,n,s,i),s,o)}function Qee(e,t){return e.write(t)}function ete(e,t){return e.write(be(e.read(),t))}function tte(e,t){return e.write(Re(e.read(),t))}function Dw(e){return e.map(t=>t.read())}function kw(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function nte(e,t){const n=t.map(i=>i.read()),s=Cb(e,n);return t.map(i=>s.grads[i.name])}class Ln{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class ii{constructor(e,t,n,s,i,o,a){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=i,this.outputTensorIndex=a,this.id=Iv(),o!=null&&(this.originalName=pv(o),this.name=mv(this.originalName)),this.rank=t.length}}let i3=0;class em{constructor(e,t){this.callArgs=t,this.id=i3++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let r3=0;class lt extends Ao{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=r3++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const n=this.getClassName();t=sr(n)+"_"+Jp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let i=null;e.batchSize!=null&&(i=e.batchSize),n=[i].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new ti(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new q(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return ts(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return ts(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new nr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new nr(`Layer ${this.name} is not connected, no input to return.`);return ts(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new nr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new nr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return ts(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Et(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=Et(this.inputSpec);if(e.length!==t.length)throw new q(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){const s=e[n],i=t[n];if(i==null)continue;const o=s.rank;if(i.ndim!=null&&o!==i.ndim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${o}`);if(i.maxNDim!=null&&o>i.maxNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${o}`);if(i.minNDim!=null&&o<i.minNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${o}.`);if(i.dtype!=null&&s.dtype!==i.dtype)throw new q(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${s.dtype}.`);if(i.axes){const a=s.shape;for(const c in i.axes){const h=Number(c),d=i.axes[c],m=h>=0?a[h]:a[a.length+h];if(d!=null&&[d,null].indexOf(m)===-1)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected axis ${h} of input shape to have value ${d} but got shape ${a}.`)}}if(i.shape!=null)for(let a=0;a<i.shape.length;++a){const c=i.shape[a],h=s.shape[a];if(c!=null&&h!=null&&c!==h)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${s.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 n=Et(e);let s=!0;for(const o of n)if(!(o instanceof ii)){s=!1;break}let i=!0;for(const o of n)if(o instanceof ii){i=!1;break}if(s===i)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return Bo(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of Et(e))o.push(a.shape);this.build(ts(o)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&i&&(this._refCount=1)}if(this.assertInputCompatibility(e),i){let o=this.call(e,t);const a=Et(o),c=[];for(let h of a)n.indexOf(h)!==-1&&(h=h.clone()),c.push(h);if(o=ts(c),this.activityRegularizer!=null)throw new Pe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}else{const o=o3(e),a=this.computeOutputShape(o);let c;const h=a3(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?o[0]:o),a!=null&&a.length>0&&Array.isArray(a[0])?c=a.map((d,m)=>new ii(h,d,this,Et(e),t,this.name,m)):c=new ii(h,a,this,Et(e),t,this.name),this.addInboundNode(e,c,null,null,o,a,t),this._refCount++,this.activityRegularizer!=null)throw new Pe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape==null)return;if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new nr(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}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 nr(`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 ti(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Qp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Dw(e?this.trainableWeights:this.weights)}setWeights(e){Q(()=>{const t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;const n=[],s=Dw(t);for(let i=0;i<s.length;++i){const o=s[i],a=t[i],c=e[i];if(!ae(o.shape,c.shape))throw new q(`Layer weight shape ${o.shape} not compatible with provided weight shape ${c.shape}`);n.push([a,c])}kw(n)})}addWeight(e,t,n,s,i,o,a){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Pt("zeros"));const c=s.apply(t,n),h=new si(c,n,e,o,a);return c.dispose(),i!=null&&this.addLoss(()=>i.apply(h.read())),o==null&&(o=!0),o?this._trainableWeights.push(h):this._nonTrainableWeights.push(h),h}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){if(e==null||Array.isArray(e)&&e.length===0)return;e=Et(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e)}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=Et(e);t=Et(t),n=Et(n),s=Et(s),i=Zp(i),o=Zp(o);const h=[],d=[],m=[];for(const f of c)h.push(f.sourceLayer),d.push(f.nodeIndex),m.push(f.tensorIndex);new em({outboundLayer:this,inboundLayers:h,nodeIndices:d,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let f=0;f<t.length;f++)t[f].sourceLayer=this,t[f].nodeIndex=this.inboundNodes.length-1,t[f].tensorIndex=f}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function o3(e){e=Et(e);const t=[];for(const n of e)t.push(n.shape);return ts(t)}function a3(e){return"float32"}function Tv(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{const s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{const i=[];for(let o=0;o<s.inboundLayers.length;o++){const a=s.inputTensors[o],c=s.inboundLayers[o],h=s.nodeIndices[o],d=Tv(a,c,h);for(const m of d)i.indexOf(m)===-1&&i.push(m)}return i}}}class nc extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Jp("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new q("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const s=new ii(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new em({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}nc.className="InputLayer",fe(nc);function Av(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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s=0;s<e.sourceLayer.inboundNodes.length;++s)for(const i of e.sourceLayer.inboundNodes[s].outputTensors)if(i.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}class Oi extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=Jp(N)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Vr(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(N=>N.name)}`);Vr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(N=>N.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const N of this.outputs){const O=N.sourceLayer,E=N.nodeIndex,k=N.tensorIndex;this.outputLayers.push(O),this.outputLayersNodeIndices.push(E),this.outputLayersTensorIndices.push(k)}for(const N of this.inputs){const O=N.sourceLayer,E=N.nodeIndex,k=N.tensorIndex;As(E===0,"input layer has >1 nodes"),As(k===0,"input layer has >1 tensors"),this.inputLayers.push(O),this.inputLayersNodeIndices.push(E),this.inputLayersTensorIndices.push(k)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let N=0;N<this.inputLayers.length;N++){const O=this.inputLayers[N];if(!(O instanceof nc))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${N} (0-based) originates from layer type ${O.getClassName()}.`);this.inputNames.push(O.name),this.feedInputShapes.push(O.batchInputShape),this.feedInputNames.push(O.name)}for(const N of this.outputLayers)this.outputNames.push(N.name);this.internalInputShapes=this.inputs.map(N=>N.shape),this.internalOutputShapes=this.outputs.map(N=>N.shape);const t={},n={},s={},i={},o={},a=[],c=(N,O,E,k,F,U)=>{(k==null||F==null||U==null)&&(k=N.sourceLayer,F=N.nodeIndex,U=N.tensorIndex);const $=k.inboundNodes[F];if(E.indexOf($)!==-1)throw new ti(`The tensor ${N.name} at layer "${k.name}" is part of a cycle.`);if(O.indexOf($)!==-1)return;this.containerNodes.add(Oi.nodeKey(k,F)),k.id in o||(o[k.id]=Object.keys(o).length),E.indexOf($)===-1&&E.push($);const Y=$.inboundLayers.length;for(let j=0;j<Y;j++){const Z=$.inputTensors[j],ie=$.inboundLayers[j],de=$.nodeIndices[j],he=$.tensorIndices[j];c(Z,O,E,ie,de,he)}for(O.push($);E.indexOf($)>=0;)E.splice(E.indexOf($),1);a.push($)},h=[],d=[];for(const N of this.outputs)c(N,h,d);const m=a.slice().reverse();for(const N of m){n[N.id]=N,N.id in t||(t[N.id]=0);let O=t[N.id];const E=s[N.outboundLayer.id]==null?0:s[N.outboundLayer.id];O=Math.max(O,E),s[N.outboundLayer.id]=O,i[N.outboundLayer.id]=N.outboundLayer,t[N.id]=O;for(let k=0;k<N.inboundLayers.length;k++){const F=N.inboundLayers[k],U=N.nodeIndices[k],$=F.inboundNodes[U],Y=t[$.id]==null?0:t[$.id];t[$.id]=Math.max(O+1,Y),n[$.id]=$}}const f={};for(const N in t){const O=t[N];O in f||(f[O]=[]),f[O].push(n[N])}const b={};for(const N in s){const O=s[N];O in b||(b[O]=[]),b[O].push(i[N])}let w=Object.keys(b).map(N=>parseInt(N,10)).sort(Bp);this.layers=[];for(const N of w){const O=b[N];O.sort((E,k)=>{const F=o[E.id],U=o[k.id];return F<U?-1:F>U?1:0});for(const E of O)E instanceof Oi&&this.internalContainerRefs.push(E),this.layers.push(E)}this.layersByDepth=b,w=Object.keys(f).map(N=>parseInt(N,10)).sort(Bp);const L=this.inputs.slice(),x=[];for(const N of w)for(const O of f[N]){const E=O.outboundLayer;if(E!=null){for(const k of O.inputTensors)if(L.indexOf(k)===-1)throw new ti(`Graph disconnected: cannot obtain value for tensor ${k} at layer "${E.name}". The following previous layers were accessed without issue: ${x}`);for(const k of O.outputTensors)L.push(k);x.push(E.name)}}this.nodesByDepth=f;const v=this.layers.map(N=>N.name);for(const N of v){const O=v.filter(E=>E===N).length;if(O!==1)throw new ti(`The name "${N}" is used ${O} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(v))}this.outboundNodes=[],this.inboundNodes=[],new em({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let s=0;for(const o of this.layers)for(const a of o.weights){if(n[a.originalName]!=null)throw new q(`Duplicate weight name: ${a.originalName}`);n[a.originalName]=a,s++}const i=[];for(const o in e){let a=o;if(n[o]==null){const c=o.split("/"),h=c.slice(0,-2).concat([c[c.length-1]]);a=h.join("/")}if(n[a]!=null)i.push([n[a],e[o]]);else if(t)throw new q(`Provided weight data has no target variable: ${o}`);delete n[a]}if(t){const o=[];for(const a in n)o.push(a);if(o.length>0)throw new q(`${o.length} of ${s} weights are not set: ${o}`)}kw(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${lm}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=Pw(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return Q(()=>{e=Et(e);const n=new Po;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return zh(this.outputs,n,t)})}computeMask(e,t){return Q(()=>{e=Et(e);let n;return t==null?n=$o(null,e.length):n=Et(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=Zp(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],h=t[a],d=c.name+"_0_0";n[d]=h}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(Bp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const h of c){const d=h.outboundLayer;if(this.inputLayers.map(L=>L.id).indexOf(d.id)!==-1)continue;const m=[];for(let L=0;L<h.inboundLayers.length;L++){const x=h.inboundLayers[L],v=h.nodeIndices[L],N=h.tensorIndices[L],O=`${x.name}_${v}_${N}`,E=n[O];m.push(E)}const f=d.computeOutputShape(ts(m)),b=Zp(f),w=d.inboundNodes.indexOf(h);for(let L=0;L<b.length;L++){const x=`${d.name}_${w}_${L}`;n[x]=b[L]}}}const i=[],o=[];for(let a=0;a<this.outputLayers.length;a++){const c=this.outputLayers[a],h=this.outputLayersNodeIndices[a],d=this.outputLayersTensorIndices[a],m=`${c.name}_${h}_${d}`;o.push(m)}for(let a=0;a<o.length;a++){const c=o[a];As(c in n),i.push(n[c])}return ts(i)}runInternalGraph(e,t){t==null&&(t=$o(null,e.length));const n={};for(let c=0;c<this.inputs.length;++c){const h=this.inputs[c],d=e[c],m=t[c];n[h.id]=[d,m]}const s=Object.keys(this.nodesByDepth).map(c=>parseInt(c,10)).sort(Bp);for(const c of s){const h=this.nodesByDepth[c];for(const d of h){const m=d.outboundLayer,f=d.inputTensors,b=d.outputTensors,w=new Array;for(const L of f)L.id in n&&w.push(n[L.id]);if(w.length===f.length){let L={},x,v,N,O;if(d.callArgs!=null&&(L=d.callArgs),w.length===1){const[E,k]=w[0];L.mask==null&&(L.mask=k),N=Et(m.call(E,L)),O=Et(m.computeMask(E,k)),x=[E],v=[k]}else x=w.map(E=>E[0]),v=w.map(E=>E[1]),L.mask==null&&(L.mask=v),N=Et(m.call(x,L)),O=Et(m.computeMask(x,v));if(m.activityRegularizer)throw new Pe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let E=0;E<b.length;++E){const k=b[E],F=N[E],U=O[E];n[k.id]=[F,U]}}}}const i=[],o=[],a=[];for(const c of this.outputs){As(c.id in n,`Could not compute output ${c.name} : ${c.id}`);const[h,d]=n[c.id];a.push(h.shape),i.push(h),o.push(d)}return[i,o,a]}buildNodeConversionMap(e){const t={};let n;for(const s of this.layers){n=s instanceof Oi?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=Oi.nodeKey(s,i);this.containerNodes.has(o)&&(t[o]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(const n of this.layers)if(n.name===e)return n;throw new q(`No such layer: ${e}`)}calculateLosses(){return Q(()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=Oi.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const o of this.layers){const a=o.getClassName(),c=o.getConfig(),h=[];for(let m=0;m<o.inboundNodes.length;m++){const f=o.inboundNodes[m],b=Oi.nodeKey(o,m);let w={};if(this.containerNodes.has(b)){if(f.callArgs)try{JSON.stringify(f.callArgs),w=f.callArgs}catch(L){console.warn(`Layer ${o.name} was passed non-serializable keyword arguments: ${f.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),w={}}if(f.inboundLayers.length>0){const L=[];for(let x=0;x<f.inboundLayers.length;x++){const v=f.inboundLayers[x],N=f.nodeIndices[x],O=f.tensorIndices[x],E=Oi.nodeKey(v,N);let k=t[E];k==null&&(k=0),L.push([v.name,k,O,w])}h.push(L)}}}const d={};d.name=o.name,d.className=a,d.config=c,d.inboundNodes=h,n.push(d)}e.layers=n;const s=[];for(let o=0;o<this.inputLayers.length;o++){const a=this.inputLayers[o],c=this.inputLayersNodeIndices[o],h=Oi.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.inputLayersTensorIndices[o];s.push([a.name,d,m])}e.inputLayers=s;const i=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],c=this.outputLayersNodeIndices[o],h=Oi.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.outputLayersTensorIndices[o];i.push([a.name,d,m])}return e.outputLayers=i,e}static fromConfig(e,t,n={},s=!1){const i={},o={};function a(x,v){x.name in o?o[x.name].push(v):o[x.name]=[v]}function c(x,v){const N=[];let O;for(const E of v){const k=E[0],F=E[1],U=E[2];if(O=E[3]==null?{}:E[3],!(k in i)){a(x,v);return}const $=i[k];if($.inboundNodes.length<=F){a(x,v);return}const Y=$.inboundNodes[F];N.push(Y.outputTensors[U])}N.length>0&&x.apply(ts(N),O)}function h(x){const v=x.name,N=ri(x,t.customObjects!=null?t.customObjects:{});N.setFastWeightInitDuringBuild(s),i[v]=N;const O=x.inboundNodes;O.forEach(E=>{if(!(E instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${E}`);a(N,E)})}const d=t.name,m=t.layers;for(const x of m)h(x);for(;!pz(o);)for(const x of m){const v=i[x.name];if(v.name in o){const N=o[v.name];delete o[v.name];for(const O of N)c(v,O)}}const f=[],b=[],w=t.inputLayers;for(const x of w){const v=x[0],N=x[1],O=x[2];As(v in i);const E=i[v],k=E.inboundNodes[N].outputTensors;f.push(k[O])}const L=t.outputLayers;for(const x of L){const v=x[0],N=x[1],O=x[2];As(v in i);const E=i[v],k=E.inboundNodes[N].outputTensors;b.push(k[O])}return new e({inputs:f,outputs:b,name:d})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){Q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function Vv(e,t,n){const s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(i=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){const i=[];return t.forEach(o=>{o in e?i.push(e[o]):i.push(null)}),i}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function Gv(e,t){return Vv(e,t,"classWeight")}function gte(e,t){return Vv(e,t,"sampleWeight")}async function Yv(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){const i=Q(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const c=1;return e.argMax(c)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),o=Array.from(await i.data());He(i);const a=[];return o.forEach(c=>{if(n[c]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${c} exists in the data but not in classWeight`);a.push(n[c])}),ls(a,"float32")}else return null}function $3(e,t){return X(e,t)}const U3=32;function Hv(e,t){let n,s;const i=t;n=i.xs,s=i.ys,A(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);const o=qv("input",e.inputNames,n),a=qv("output",e.outputNames,s),c=o[0].shape[0];A(o.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${o.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),A(a.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${a.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let h=0;h<o.length;h++)A(o[h].shape[0]===c,()=>`Batch size mismatch: input ${e.inputNames[h]} has ${o[h].shape[0]}; expected ${c} based on input ${e.inputNames[0]}.`);for(let h=0;h<a.length;h++)A(a[h].shape[0]===c,()=>`Batch size mismatch: output ${e.outputNames[h]} has ${a[h].shape[0]}; expected ${c} based on input ${e.inputNames[0]}.`);return{xs:o,ys:a}}function qv(e,t,n){if(n instanceof ee)return[n];if(Array.isArray(n))return A(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{const s=[];for(const i of t){if(n[i]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);s.push(n[i])}return s}}function B3(e){if(e.length===3)throw new Pe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function M3(e,t,n){const s=n.batchesPerEpoch!=null;if(A(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),A(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),A(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),A(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),A(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{const i=n.validationData!=null;let o,a;if(i)if(jv(n.validationData))A(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{const v=B3(n.validationData);o=v.xs,a=v.ys}const c=e.makeTrainFunction(),h=e.getDedupedMetricsNames();let d;i?d=h.slice().concat(h.map(v=>"val_"+v)):d=h.slice();const m=Ev(n.callbacks,n.yieldEvery),f=n.verbose==null?1:n.verbose,{callbackList:b,history:w}=Dv(m,f,n.epochs,null,null,P3(t,n),null,i,d);b.setModel(e),e.history=w,await b.onTrainBegin(),e.stopTraining_=!1;let L=n.initialEpoch==null?0:n.initialEpoch,x=await t.iterator();for(;L<n.epochs;){const v={};await b.onEpochBegin(L);let N=0,O=0;for(s||(x=await t.iterator());s?N<n.batchesPerEpoch:!0;){const E=await x.next();if(s&&E.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${N} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(E.value!=null){const{xs:k,ys:F}=Hv(e,E.value),U={};U.batch=O,U.size=k[0].shape[0],await b.onBatchBegin(O,U);const $=[];if(n.classWeight!=null){const Z=Gv(n.classWeight,e.outputNames);for(let ie=0;ie<Z.length;++ie)$.push(await Yv(F[ie],null,Z[ie]))}const Y=k.concat(F).concat($),j=c(Y);He(Y);for(let Z=0;Z<h.length;++Z){const ie=h[Z],de=j[Z];U[ie]=de,bn(de)}await b.onBatchEnd(O,U),vv(U),O++,N++}if(s?N>=n.batchesPerEpoch:E.done){if(i){let k;jv(n.validationData)?k=Et(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):k=Et(e.evaluate(o,a,{batchSize:n.validationBatchSize==null?U3:n.validationBatchSize,verbose:0}));for(let F=0;F<e.metricsNames.length;++F)v[`val_${e.metricsNames[F]}`]=k[F]}break}if(e.stopTraining_)break}if(await b.onEpochEnd(L,v),L++,e.stopTraining_)break}return await b.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function P3(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function jv(e){return typeof e.iterator=="function"}function z3(e){return typeof e.next=="function"}async function V3(e,t,n){n=n||{};const s=n.batches!=null,i=e.testFunction;let o=[];if(n.verbose>0)throw new Pe("Verbose mode is not implemented yet.");A(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);const a=z3(t)?t:await t.iterator();let c=0,h=0;for(;s?h<n.batches:!0;){const d=await a.next();if(o=Q(()=>{if(d.value){const{xs:m,ys:f}=Hv(e,d.value),b=m.concat(f),w=Q(()=>i(b));if(He(b),h===0)for(let x=0;x<w.length;++x)o.push(Ce(0));const L=b[0].shape[0];for(let x=0;x<w.length;++x){const v=w[x],N=o[x];o[x]=Q(()=>be(o[x],X(L,v))),h>0&&He(N)}He(w),c+=L,++h}return o}),d.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). 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Use LayersModel.compile(modelCompileArgs).");const i=[];for(let o=0;o<this.feedOutputShapes.length;++o){const a=this.feedOutputShapes[o],c=this.feedLossFns[o];c===nm?i.push(a.slice(0,a.length-1).concat([1])):i.push(a)}if(e=Jv(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Jv(t,this.feedOutputNames,i,!1,"target"),q3(e,t,null),j3(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new q(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,i=!0,o){const[a,c]=this.standardizeUserDataXY(e,t,i,o);if(n!=null)throw new Error("sample weight is not supported yet.");let h=null;if(s!=null){const d=Gv(s,this.outputNames);h=[];for(let m=0;m<d.length;++m)h.push(await Yv(c[m],null,d[m]))}return[a,c,h]}testLoop(e,t,n,s=0,i){return Q(()=>{const o=this.checkNumSamples(t,n,i,"steps"),a=[];if(s>0)throw new Pe("Verbose mode is not implemented yet.");if(i!=null)throw new Pe("steps mode in testLoop() is not implemented yet");{const c=Yw(o,n),h=ls(ni(0,o));for(let d=0;d<c.length;++d){const m=c[d][0],f=c[d][1],b=Mo(h,m,f-m),w=Gw(t,b),L=e(w);if(d===0)for(let x=0;x<L.length;++x)a.push(Ce(0));for(let x=0;x<L.length;++x){const v=L[x];a[x]=be(a[x],X(f-m,v))}}for(let d=0;d<a.length;++d)a[d]=We(a[d],o)}return a})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let i=s;if(ov(e,s)>1){const o=ov(e.slice(0,n),s);i+=`_${o}`}t.push(i)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),o=[],a=()=>{const m=[];for(let L=0;L<this.inputs.length;++L)m.push({key:this.inputs[L],value:n[L]});const f=new Po(m),b=zh(this.outputs,f,{training:!0});let w;for(let L=0;L<this.lossFunctions.length;++L){const x=this.lossFunctions[L];let v=x(s[L],b[L]);i[L]!=null&&(v=$3(v,i[L]));const N=qt(v);t.push(N),L===0?w=v:w=be(w,v)}for(let L=0;L<this.metricsTensors.length;++L){let x;if(this.outputs.length>1&&L<this.outputs.length)x=t[L];else{const v=this.metricsTensors[L][0],N=this.metricsTensors[L][1];x=qt(v(s[N],b[N]))}bn(x),o.push(x)}return w=qt(w),this.calculateLosses().forEach(L=>{w=be(w,L)}),w},c=this.collectedTrainableWeights.map(m=>m.read()),h=!0,d=this.optimizer_.minimize(a,h,c);return[d].concat(o)}}makeTestFunction(){this.testFunction=e=>Q(()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),o=[];for(let h=0;h<this.inputs.length;++h)o.push({key:this.inputs[h],value:s[h]});const a=new Po(o),c=zh(this.outputs,a);for(let h=0;h<this.lossFunctions.length;++h){const d=this.lossFunctions[h],m=qt(d(i[h],c[h]));h===0?n=m:n=be(n,m),t.push(n)}for(let h=0;h<this.metricsTensors.length;++h){const d=this.metricsTensors[h][0],m=this.metricsTensors[h][1],f=qt(d(i[m],c[m]));t.push(f)}return t})}async fit(e,t,n={}){return Y3(this,e,t,n)}async fitDataset(e,t){return M3(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),s=n[0],i=n[1],o=this.makeTrainFunction(),a=o(s.concat(i)),c=[];for(const h of a){const d=await h.data();c.push(d[0])}return He(a),ts(c)}getNamedWeights(e){const t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,i=this.getWeights(n);for(let o=0;o<s.length;++o){if(n&&!s[o].trainable)continue;t.push({name:s[o].originalName,tensor:i[o]})}return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const t=Jd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Jd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=sr(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>sr(t))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const s of t)if(typeof n[s]=="string")e[s]=sr(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[sr(am(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>sr(am(e)));{const e={};for(const t in this.metrics)e[t]=sr(am(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=Ph(e.optimizer_config),n=ri(t);let s;if(typeof e.loss=="string")s=Uo(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>Uo(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=Uo(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>Uo(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=Uo(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const h=zy(e);if(h.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(h.length>1)throw new q(`Found more than one (${h.length}) save handlers for URL '${e}'`);e=h[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await My(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:X3,generatedBy:`TensorFlow.js tfjs-layers v${lm}`,convertedBy:null},c=t==null?!1:t.includeOptimizer;if(c&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();const h="optimizer",{data:d,specs:m}=await My(await this.optimizer.getWeights(),h);n.specs.push(...m),n.data=zd([n.data,d])}if(this.userDefinedMetadata!=null){const h=!0;Bv(this.userDefinedMetadata,this.name,h),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Bv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}rr.className="Model",fe(rr);class Qv extends rr{}Qv.className="Functional",fe(Qv);async function J3(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=Ph(n),i=ri(s,t);if(e.weightsManifest!=null){const o=await vT(e.weightsManifest,e.pathPrefix,i.weights.map(c=>c.originalName)),a={};for(const c of i.weights)a[c.originalName]=o[c.originalName];i.loadWeights(a),He(o)}return i}async function Z3(e,t){if(t==null&&(t={}),typeof e=="string"){const n=Vy(e,t);if(n.length===0)n.push(Yd(e,t));else if(n.length>1)throw new q(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Q3(e,void 0,t)}async function Q3(e,t,n){if(n==null&&(n={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const s=await e.load();let i=s.modelTopology;i.model_config!=null&&(i=i.model_config);const o=n.strict==null?!0:n.strict,a=s.weightData!=null&&s.weightSpecs!=null&&o,c=ri(Ph(i),t,a),h=s.trainingConfig;if(h!=null&&c.loadTrainingConfig(h),s.userDefinedMetadata!=null&&c.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:d,optimizerWeights:m}=eV(s.weightData,s.weightSpecs);c.loadWeights(d,o),c.optimizer!=null&&m.length>0&&await c.optimizer.setWeights(m),He(d),He(m.map(f=>f.tensor))}return c}function eV(e,t){const n=Pd(e,t),s={},i=[];return t.forEach(o=>{o.group==="optimizer"?i.push({name:o.name,tensor:n[o.name]}):s[o.name]=n[o.name]}),{modelWeights:s,optimizerWeights:i}}class rc extends rr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Jp("sequential_"),e.layers!=null)for(const t of e.layers)this.add(t)}checkShape(e){const t=e.inboundNodes[0].outputTensors[0].shape;if(t.some(n=>n<0))throw new q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof rc||e instanceof rr;let n;if(t){if(n=e,n.outputs.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new q("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const s=Av({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new q(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=Tv(this.outputs[0])}this.inboundNodes=[],new em({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:$o(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{const s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(Nt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new rr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new ti("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new ti("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new ti("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new ti("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let i,o={};if(t instanceof Array){if(!(t[0].className!=null)||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");i=t}else A(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),i=t.layers,delete t.layers,o=t;const a=new e(o);if(!(a instanceof rc))throw new Pe(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const h=void 0,d=ri(c,h,s);s&&d.setFastWeightInitDuringBuild(!0),a.add(d)}return a}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}rc.className="Sequential",fe(rc);function tV(e){return new rr(e)}function nV(e){return new rc(e)}function sV(e,t){return t==null&&(t={}),Z3(e,t)}function eN(e){return Av(e)}function iV(e,t){Ps.registerCallbackConstructor(e,t)}class us extends Ao{getConfig(){return{}}}class tN extends us{apply(e,t=1){return Dz(e,t)}}tN.className="elu",fe(tN);class nN extends us{apply(e){return bp(e)}}nN.className="selu",fe(nN);class sN extends us{apply(e){return Ni(e)}}sN.className="relu",fe(sN);class iN extends us{apply(e){return Q(()=>Oo(6,Ni(e)))}}iN.className="relu6",fe(iN);class rN extends us{apply(e){return e}}rN.className="linear",fe(rN);class oN extends us{apply(e){return Ti(e)}}oN.className="sigmoid",fe(oN);class aN extends us{apply(e){return Fz(e)}}aN.className="hardSigmoid",fe(aN);class cN extends us{apply(e){return za(e)}}cN.className="softplus",fe(cN);class lN extends us{apply(e){return kz(e)}}lN.className="softsign",fe(lN);class hN extends us{apply(e){return $a(e)}}hN.className="tanh",fe(hN);class qw extends us{apply(e,t=-1){return Fo(e,t)}}qw.className="softmax",fe(qw);class uN extends us{apply(e,t=-1){return dp(e,t)}}uN.className="logSoftmax",fe(uN);class dN extends us{apply(e,t=1){return Q(()=>Ti(e.mul(t)).mul(e))}}dN.className="swish",fe(dN);function jr(e){return e.getClassName()}function jw(e,t={}){return kh(e,Ws.getMap().classNameMap,t,"activation")}function Kr(e){if(e==null){const t={};return t.className="linear",t.config={},jw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},jw(t)}else return e instanceof us?e:jw(e)}function Kw(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class pN extends Ao{}class Gh extends pN{constructor(e){super();Kw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return Q(()=>{let t=dt([1]);return this.hasL1&&(t=be(t,$e(X(this.l1,dn(e))))),this.hasL2&&(t=be(t,$e(X(this.l2,Uh(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Gh.className="L1L2",fe(Gh);function rV(e){return Kw(e),new Gh({l1:e!=null?e.l1:null,l2:0})}function oV(e){return Kw(e),new Gh({l2:e!=null?e.l2:null,l1:0})}const mN={l1l2:"L1L2"};function Ct(e){return dw(e)}function fN(e,t={}){return kh(e,Ws.getMap().classNameMap,t,"regularizer")}function zt(e){if(e==null)return null;if(typeof e=="string"){const t=e in mN?mN[e]:e,n={className:t,config:{}};return fN(n)}else return e instanceof pN?e:fN(e)}class Xw extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Xe(e);let n=Ni(e);return this.maxValue!=null&&(n=Jn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Xw.className="ReLU",fe(Xw);class Jw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Xe(e);return lp(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Jw.className="LeakyReLU",fe(Jw);class Zw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Pt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=zt(e.alphaRegularizer),this.alphaConstraint=gn(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Nt(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Ln({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Xe(e),yh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Kt(this.alphaInitializer),alphaRegularizer:Ct(this.alphaRegularizer),alphaConstraint:fn(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}Zw.className="PReLU",fe(Zw);class Qw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Pe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Xe(e);return Ua(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Qw.className="ELU",fe(Qw);class eL extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){const n=Xe(e);return n.mul(Wh(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}eL.className="ThresholdedReLU",fe(eL);class tL extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new qw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=Xe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}tL.className="Softmax",fe(tL);function oc(e,t,n){if(typeof e=="number")return $o(e,t);if(e.length!==t)throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){const i=e[s];if(!vz(i))throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${i}`)}return e}function oi(e,t,n,s,i=1){if(e==null)return e;const o=t+(t-1)*(i-1);let a;return n==="same"?a=e:a=e-o+1,Math.floor((a+s-1)/s)}function hm(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Yr([n-t,0]);else if(s==="same")e=e*t;else throw new q(`Unsupport padding mode: ${s}.`);return e}function nL(e,t){return Q(()=>(jt(t),t==="channelsFirst"?Ye(e,[0,2,3,1]):e))}function gN(e,t){return Q(()=>(jt(t),t==="channelsFirst"?Ye(e,[0,2,3,4,1]):e))}function yN(e,t,n,s=1,i="valid",o,a=1){return Q(()=>{if(o==null&&(o=ei()),jt(o),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(o==="channelsFirst"&&(e=Ye(e,[0,2,1])),i==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=ip(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=Ri(c,n)),c})}function yte(e,t,n=1,s="valid",i,o=1){return Q(()=>(jt(i),yN(e,t,null,n,s,i,o)))}function bte(e,t,n=[1,1],s="valid",i,o){return Q(()=>(jt(i),sL(e,t,null,n,s,i,o)))}function sL(e,t,n,s=[1,1],i="valid",o,a,c=null){return Q(()=>{if(o==null&&(o=ei()),jt(o),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let h=nL(e,o);if(i==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return h=Kb({x:h,filter:t,strides:s,pad:i==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:c}),o==="channelsFirst"&&(h=Ye(h,[0,3,1,2])),h})}function wte(e,t,n=[1,1,1],s="valid",i,o){return Q(()=>(jt(i),bN(e,t,null,n,s,i,o)))}function bN(e,t,n,s=[1,1,1],i="valid",o,a){return Q(()=>{if(o==null&&(o=ei()),jt(o),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let c=gN(e,o);if(i==="causal")throw new Pe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=Lb(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=Ri(c,n)),o==="channelsFirst"&&(c=Ye(c,[0,4,1,2,3])),c})}class iL extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",iL.verifyArgs(t),this.rank=e,wn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Pe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=oc(t.kernelSize,e,"kernelSize"),this.strides=oc(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,vs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,jt(this.dataFormat),this.activation=Kr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=gn(t.biasConstraint),this.biasRegularizer=zt(t.biasRegularizer),this.activityRegularizer=zt(t.activityRegularizer),this.dilationRate=oc(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new q(`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 q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(As("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!mw(e.kernelSize,"number",1,3))throw new q(`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:jr(this.activation),useBias:this.useBias,biasInitializer:Kt(this.biasInitializer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),biasConstraint:fn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Yh extends iL{constructor(e,t){super(e,t);this.kernel=null,Yh.verifyArgs(t),this.filters=t.filters,wn(this.filters,"filters"),this.kernelInitializer=Pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=gn(t.kernelConstraint),this.kernelRegularizer=zt(t.kernelRegularizer)}build(e){e=Nt(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return Q(()=>{e=Xe(e);let n;const s=this.bias==null?null:this.bias.read(),i=cv(this.activation.getClassName());if(i!=null&&this.rank===2)n=sL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=yN(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=sL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=bN(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Pe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Nt(e);const t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let i=0;i<n.length;++i){const o=oi(n[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);t.push(o)}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:Kt(this.kernelInitializer),kernelRegularizer:Ct(this.kernelRegularizer),kernelConstraint:fn(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 q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Hh extends Yh{constructor(e){super(2,e);Hh.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!mw(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Hh.className="Conv2D",fe(Hh);class um extends Yh{constructor(e){super(3,e);um.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 q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}um.className="Conv3D",fe(um);class rL extends Hh{constructor(e){super(e);if(this.inputSpec=[new Ln({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Nt(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ln({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return Q(()=>{let n=Xe(e);if(n.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);const s=n.shape,i=s[0];let o,a;this.dataFormat==="channelsFirst"?(o=2,a=3):(o=1,a=2);const c=s[o],h=s[a],d=this.kernelSize[0],m=this.kernelSize[1],f=this.strides[0],b=this.strides[1],w=hm(c,f,d,this.padding),L=hm(h,b,m,this.padding),x=[i,w,L,this.filters];this.dataFormat!=="channelsLast"&&(n=Ye(n,[0,2,3,1]));let v=rp(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Ye(v,[0,3,1,2])),this.bias!=null&&(v=Ri(v,this.bias.read(),this.dataFormat)),this.activation!=null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=Nt(e);const t=e.slice();let n,s,i;this.dataFormat==="channelsFirst"?(n=1,s=2,i=3):(n=3,s=1,i=2);const o=this.kernelSize[0],a=this.kernelSize[1],c=this.strides[0],h=this.strides[1];return t[n]=this.filters,t[s]=hm(t[s],c,o,this.padding),t[i]=hm(t[i],h,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}rL.className="Conv2DTranspose",fe(rL);class wN extends Yh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("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 q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=zt(t.depthwiseRegularizer),this.depthwiseConstraint=gn(t.depthwiseConstraint),this.pointwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=zt(t.pointwiseRegularizer),this.pointwiseConstraint=gn(t.pointwiseConstraint)}build(e){if(e=Nt(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${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 q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),i=[];for(let a=0;a<this.rank;++a)i.push(1);i.push(n*this.depthMultiplier,this.filters);const o=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,o,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",i,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,o,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,o,this.biasConstraint):this.bias=null,this.inputSpec=[new Ln({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return Q(()=>{e=Xe(e);let n;if(this.rank===1)throw new Pe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ye(e,[0,2,3,1])),n=Ub(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ri(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ye(n,[0,3,1,2])),n})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Kt(this.depthwiseInitializer),e.pointwiseInitializer=Kt(this.pointwiseInitializer),e.depthwiseRegularizer=Ct(this.depthwiseRegularizer),e.pointwiseRegularizer=Ct(this.pointwiseRegularizer),e.depthwiseConstraint=fn(this.depthwiseConstraint),e.pointwiseConstraint=fn(this.pointwiseConstraint),e}}wN.className="SeparableConv";class oL extends wN{constructor(e){super(2,e)}}oL.className="SeparableConv2D",fe(oL);class dm extends Yh{constructor(e){super(1,e);dm.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"&&!mw(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}dm.className="Conv1D",fe(dm);class aL extends lt{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 Q(()=>{if(e=Xe(e),this.dataFormat==="channelsLast"){const n=Pp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Pp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=Pp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Pp(n,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}}aL.className="Cropping2D",fe(aL);class cL extends lt{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}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{const t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return Q(()=>{let n=Xe(e);const s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ye(n,[0,2,3,1]);const i=this.size[0]*s[2],o=this.size[1]*s[3],a=n.resizeNearestNeighbor([i,o]);return Ye(a,[0,3,1,2])}else{const i=this.size[0]*s[1],o=this.size[1]*s[2];return n.resizeNearestNeighbor([i,o])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}cL.className="UpSampling2D",fe(cL);function aV(e,t,n=[1,1],s="valid",i,o){return Q(()=>{i==null&&(i=ei()),jt(i);let a=nL(e,i);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return a=Co(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Ye(a,[0,3,1,2])),a})}class lL extends iL{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=gn(e.depthwiseConstraint),this.depthwiseRegularizer=zt(e.depthwiseRegularizer)}build(e){if(e=Nt(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Q(()=>{e=Xe(e);let n=aV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ri(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Nt(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=oi(t,this.kernelSize[0],this.padding,this.strides[0]),o=oi(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,i,o]:[e[0],i,o,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Kt(this.depthwiseInitializer),e.depthwiseRegularizer=Ct(this.depthwiseRegularizer),e.depthwiseConstraint=fn(this.depthwiseRegularizer),e}}lL.className="DepthwiseConv2D",fe(lL);function LN(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function i(o){return o==null||Array.isArray(o)?o:[o]}return t=i(t),n=i(n),{inputs:e,initialState:t,constants:n}}function SN(e,t,n,s=!1,i,o,a=!1,c=!1){return Q(()=>{const h=t.shape.length;if(h<3)throw new q(`Input should be at least 3D, but is ${h}D.`);const d=[1,0].concat(ni(2,h));if(t=Ye(t,d),o!=null)throw new Pe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===h-1&&(i=Zn(i,-1)),i=Ye(i,d)),s&&(t=Ts(t,0),i!=null&&(i=Ts(i,0)));const m=[];let f,b=n;const w=t.shape[0],L=Qs(t);let x;i!=null&&(x=Qs(i));for(let N=0;N<w;++N){const O=L[N],E=Q(()=>e(O,b));if(i==null)f=E[0],b=E[1];else{const k=Q(()=>{const F=x[N],U=Fn(F).sub(F),$=E[0].mul(F).add(b[0].mul(U)),Y=b.map((j,Z)=>E[1][Z].mul(F).add(j.mul(U)));return{output:$,newStates:Y}});f=k.output,b=k.newStates}c&&m.push(f)}let v;if(c){const N=1;v=es(m,N)}return[f,v,b]})}class Ei extends lt{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new fm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("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 Ln({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 ni(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ew(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){const i=[];for(const o of t)i.push([e[0],o]);return[s].concat(i)}else return s}computeMask(e,t){return Q(()=>{Array.isArray(t)&&(t=t[0]);const n=this.returnSequences?t:null;if(this.returnState){const s=this.states.map(i=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){const t=null;if(this.numConstants!=null)throw new Pe("Constants support is not implemented in RNN yet.");Ew(e)&&(e=e[0]),e=e;const n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Ln({shape:[n,null,...s]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new Pe("Constants support is not implemented in RNN yet.");this.cell.build(i);let o;if(Array.isArray(this.cell.stateSize)?o=this.cell.stateSize:o=[this.cell.stateSize],this.stateSpec!=null){if(!ae(this.stateSpec.map(a=>a.shape[a.shape.length-1]),o))throw new q(`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(a=>new Ln({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){Q(()=>{if(!this.stateful)throw new nr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(n==null)throw new q("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=>dt([n,s])):this.states_=[dt([n,this.cell.stateSize])];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>dt([n,s])):this.states_[0]=dt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):He(this.states_);for(let s=0;s<this.states_.length;++s){const i=e[s],o=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,a=[n,o];if(!ae(i.shape,a))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>bn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=LN(e,n,s,this.numConstants);e=i.inputs,n=i.initialState,s=i.constants;let o=[],a=[];if(n!=null){t.initialState=n,o=o.concat(n),this.stateSpec=[];for(const h of n)this.stateSpec.push(new Ln({shape:h.shape}));a=a.concat(this.stateSpec)}s!=null&&(t.constants=s,o=o.concat(s),this.numConstants=s.length);const c=o[0]instanceof ii;if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const f=super.apply(h,t);return this.inputSpec=m,f}else return super.apply(e,t)}call(e,t){return Q(()=>{const n=t==null?null:t.mask,s=t==null?null:t.training;let i=t==null?null:t.initialState;e=Xe(e),i==null&&(this.stateful?i=this.states_:i=this.getInitialState(e));const o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(i.length!==o)throw new q(`RNN Layer has ${o} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:s},c=(w,L)=>{const x=this.cell.call([w].concat(L),a);return[x[0],x.slice(1)]},h=SN(c,e,i,this.goBackwards,n,null,this.unroll,this.returnSequences),d=h[0],m=h[1],f=h[2];this.stateful&&this.resetStates(f,s);const b=this.returnSequences?m:d;return this.returnState?[b].concat(f):b})}getInitialState(e){return Q(()=>{let t=dt(e.shape);return t=$e(t,[1,2]),t=$h(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Iw(t,[1,n]):t):this.cell.stateSize>1?[Iw(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 n=this.cell.getConfig();return this.getClassName()===Ei.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=ri(s,n);return new e(Object.assign(t,{cell:i}))}}Ei.className="RNN",fe(Ei);class ac extends lt{}class pm extends ac{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,wn(this.units,"units"),this.activation=Kr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=gn(e.kernelConstraint),this.recurrentConstraint=gn(e.recurrentConstraint),this.biasConstraint=gn(e.biasConstraint),this.dropout=tc([1,Yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=tc([1,Yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Nt(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 Q(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Xr({ones:()=>Fn(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Xr({ones:()=>Fn(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Ci(X(e,o),this.kernel.read()):i=Ci(e,this.kernel.read()),this.bias!=null&&(i=Ri(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Ci(n,this.recurrentKernel.read()));return this.activation!=null&&(c=this.activation.apply(c)),[c,c]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:jr(this.activation),useBias:this.useBias,kernelInitializer:Kt(this.kernelInitializer),recurrentInitializer:Kt(this.recurrentInitializer),biasInitializer:Kt(this.biasInitializer),kernelRegularizer:Ct(this.kernelRegularizer),recurrentRegularizer:Ct(this.recurrentRegularizer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),kernelConstraint:fn(this.kernelConstraint),recurrentConstraint:fn(this.recurrentConstraint),biasConstraint:fn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}pm.className="SimpleRNNCell",fe(pm);class hL extends Ei{constructor(e){e.cell=new pm(e),super(e)}call(e,t){return Q(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return new e(t)}}hL.className="SimpleRNN",fe(hL);class mm extends ac{constructor(e){super(e);if(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 q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,wn(this.units,"units"),this.activation=Kr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Kr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=gn(e.kernelConstraint),this.recurrentConstraint=gn(e.recurrentConstraint),this.biasConstraint=gn(e.biasConstraint),this.dropout=tc([1,Yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=tc([1,Yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Nt(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 Q(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training==null?!1:t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Xr({ones:()=>Fn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Xr({ones:()=>Fn(s),rate:this.recurrentDropout,training:n,count:3}));const i=this.dropoutMask,o=this.recurrentDropoutMask;let a,c,h;0<this.dropout&&this.dropout<1&&(e=X(e,i[0]));let d=Ci(e,this.kernel.read());this.useBias&&(d=Ri(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[f,b]=hs(m,[2*this.units,this.units],m.rank-1),w=Ci(s,f),[L,x,v]=hs(d,3,d.rank-1),[N,O]=hs(w,2,w.rank-1);a=this.recurrentActivation.apply(be(L,N)),c=this.recurrentActivation.apply(be(x,O));const E=Ci(X(c,s),b);h=this.activation.apply(be(v,E));const k=be(X(a,s),X(be(1,Ht(a)),h));return[k,k]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:jr(this.activation),recurrentActivation:jr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Kt(this.kernelInitializer),recurrentInitializer:Kt(this.recurrentInitializer),biasInitializer:Kt(this.biasInitializer),kernelRegularizer:Ct(this.kernelRegularizer),recurrentRegularizer:Ct(this.recurrentRegularizer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),kernelConstraint:fn(this.kernelConstraint),recurrentConstraint:fn(this.recurrentConstraint),biasConstraint:fn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}mm.className="GRUCell",fe(mm);class uL extends Ei{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 mm(e),super(e)}call(e,t){return Q(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}uL.className="GRU",fe(uL);class qh extends ac{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,wn(this.units,"units"),this.activation=Kr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Kr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=gn(e.kernelConstraint),this.recurrentConstraint=gn(e.recurrentConstraint),this.biasConstraint=gn(e.biasConstraint),this.dropout=tc([1,Yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=tc([1,Yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Nt(e);const n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,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 i=this.biasInitializer,o=this.units;s=new(t=class extends Ms{apply(c,h){const d=i.apply([o]),m=new Vp().apply([o]),f=i.apply([o*2]);return yv(yv(d,m),f)}},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 Q(()=>{const n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Xr({ones:()=>Fn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Xr({ones:()=>Fn(s),rate:this.recurrentDropout,training:n,count:4}));const o=this.dropoutMask,a=this.recurrentDropoutMask;let c,h,d,m;0<this.dropout&&this.dropout<1&&(e=X(e,o[0]));let f=Ci(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),f=be(f,Ci(s,this.recurrentKernel.read())),this.useBias&&(f=Ri(f,this.bias.read()));const[b,w,L,x]=hs(f,4,f.rank-1);c=this.recurrentActivation.apply(b),h=this.recurrentActivation.apply(w),d=be(X(h,i),X(c,this.activation.apply(L))),m=this.recurrentActivation.apply(x);const v=X(m,this.activation.apply(d));return[v,v,d]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:jr(this.activation),recurrentActivation:jr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Kt(this.kernelInitializer),recurrentInitializer:Kt(this.recurrentInitializer),biasInitializer:Kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Ct(this.kernelRegularizer),recurrentRegularizer:Ct(this.recurrentRegularizer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),kernelConstraint:fn(this.kernelConstraint),recurrentConstraint:fn(this.recurrentConstraint),biasConstraint:fn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}qh.className="LSTMCell",fe(qh);class dL extends Ei{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 qh(e),super(e)}call(e,t){return Q(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}dL.className="LSTM",fe(dL);class fm extends ac{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 Q(()=>{e=e;let n=e.slice(1);const s=[];for(const a of this.cells.slice().reverse())Array.isArray(a.stateSize)?s.push(n.splice(0,a.stateSize.length)):s.push(n.splice(0,1));s.reverse();const i=[];let o;for(let a=0;a<this.cells.length;++a){const c=this.cells[a];n=s[a],a===0?o=[e[0]].concat(n):o=[o[0]].concat(n),o=c.call(o,t),i.push(o.slice(1))}n=[];for(const a of i.slice().reverse())n.push(...a);return[o[0]].concat(n)})}build(e){Ew(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Bo(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=super.getConfig(),t=i=>({className:i.getClassName(),config:i.getConfig()}),n=this.cells.map(t),s={cells:n};return Object.assign({},e,s)}static fromConfig(e,t,n={}){const s=[];for(const i of t.cells)s.push(ri(i,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return Dw(e)}setWeights(e){const t=[];for(const n of this.cells){const s=n.weights.length,i=e.splice(s);for(let o=0;o<n.weights.length;++o)t.push([n.weights[o],i[o]])}kw(t)}}fm.className="StackedRNNCells",fe(fm);function Xr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>wv(t(),n),a=()=>Bh(o,t,s);if(!i||i<=1)return bn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(h=>bn(h.clone()))}var cV=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,s=Object.getOwnPropertySymbols(e);i<s.length;i++)t.indexOf(s[i])<0&&Object.prototype.propertyIsEnumerable.call(e,s[i])&&(n[s[i]]=e[s[i]]);return n};class Lte extends ac{}class IN extends Ei{constructor(e){if(e.unroll)throw new Pe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Pe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ln({ndim:5})]}call(e,t){return Q(()=>{if(this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}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 Q(()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=dt(i);return Array.isArray(t)?Array(t.length).fill(o):[o]})}resetStates(e,t=!1){Q(()=>{if(!this.stateful)throw new nr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=n[0];if(o==null)throw new q("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(()=>dt(i)):this.states_=[dt(i)];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>dt(i)):this.states_[0]=dt(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):He(this.states_);for(let a=0;a<this.states_.length;++a){const c=e[a],h=i;if(!ae(c.shape,h))throw new q(`State ${a} is incompatible with layer ${this.name}: expected shape=${h}, received shape=${c.shape}`);this.states_[a]=c}}this.states_=this.states_.map(a=>bn(a.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:i,strides:o,dilationRate:a}=this.cell,c=t==="channelsFirst",h=e[c?3:2],d=e[c?4:3],m=oi(h,s[0],i,o[0],a[0]),f=oi(d,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,f]:[m,f,n]];return b}}IN.className="ConvRNN2D";class gm extends qh{constructor(e){const{filters:t,kernelSize:n,strides:s,padding:i,dataFormat:o,dilationRate:a}=e;super(Object.assign({},e,{units:t}));this.filters=t,wn(this.filters,"filters"),this.kernelSize=oc(n,2,"kernelSize"),this.kernelSize.forEach(c=>wn(c,"kernelSize")),this.strides=oc(s||1,2,"strides"),this.strides.forEach(c=>wn(c,"strides")),this.padding=i||"valid",vs(this.padding),this.dataFormat=o||"channelsLast",jt(this.dataFormat),this.dilationRate=oc(a||1,2,"dilationRate"),this.dilationRate.forEach(c=>wn(c,"dilationRate"))}build(e){var t;e=Nt(e);const n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[n]}`);const s=e[n],i=4,o=this.kernelSize.concat([s,this.filters*i]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const a=this.kernelSize.concat([this.filters,this.filters*i]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let c;if(this.unitForgetBias){const h=this.biasInitializer,d=this.filters;c=new(t=class extends Ms{apply(f,b){const w=h.apply([d]),L=Js([d]),x=h.apply([d*2]);return Sw([w,L,x])}},t.className="CustomInit",t)}else c=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*i],null,c,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return Q(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Xr({ones:()=>Fn(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,h=(we,Se,xe)=>!Se||!Se[xe]?we:X(Se[xe],we);let d=h(s,c,0),m=h(s,c,1),f=h(s,c,2),b=h(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Xr({ones:()=>Fn(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let L=h(i,w,0),x=h(i,w,1),v=h(i,w,2),N=h(i,w,3);const O=3,[E,k,F,U]=hs(this.kernel.read(),a,O),[$,Y,j,Z]=this.useBias?hs(this.bias.read(),a):[null,null,null,null];d=this.inputConv(d,E,$,this.padding),m=this.inputConv(m,k,Y,this.padding),f=this.inputConv(f,F,j,this.padding),b=this.inputConv(b,U,Z,this.padding);const[ie,de,he,ue]=hs(this.recurrentKernel.read(),a,O);L=this.recurrentConv(L,ie),x=this.recurrentConv(x,de),v=this.recurrentConv(v,he),N=this.recurrentConv(N,ue);const me=this.recurrentActivation.apply(be(d,L)),ce=this.recurrentActivation.apply(be(m,x)),ye=be(X(ce,o),X(me,this.activation.apply(be(f,v)))),pe=X(this.recurrentActivation.apply(be(b,N)),this.activation.apply(ye));return[pe,pe,ye]})}getConfig(){const e=super.getConfig(),{units:t}=e,n=cV(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){const i=Ji(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ri(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return Ji(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}gm.className="ConvLSTM2DCell",fe(gm);class pL extends IN{constructor(e){const t=new gm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}pL.className="ConvLSTM2D",fe(pL);class ym extends lt{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,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return Q(()=>{this.invokeCallHook(e,t);const n=Xe(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Bh(()=>wv(n,this.rate,i,this.seed),()=>n,s);return o}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()}}ym.className="Dropout",fe(ym);class mL extends ym{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}mL.className="SpatialDropout1D",fe(mL);class fL extends lt{constructor(e){super(e);if(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,wn(this.units,"units"),this.activation=Kr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=gn(e.kernelConstraint),this.biasConstraint=gn(e.biasConstraint),this.kernelRegularizer=zt(e.kernelRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.activityRegularizer=zt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Nt(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=Nt(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return Q(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=cv(this.activation.getClassName());let i;return s!=null?i=Ci(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Ci(n,this.kernel.read()),this.bias!=null&&(i=Ri(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:jr(this.activation),useBias:this.useBias,kernelInitializer:Kt(this.kernelInitializer),biasInitializer:Kt(this.biasInitializer),kernelRegularizer:Ct(this.kernelRegularizer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),kernelConstraint:fn(this.kernelConstraint),biasConstraint:fn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}fL.className="Dense",fe(fL);class gL extends lt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Nt(e);for(const t of e.slice(1))if(t==null)throw new q(`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],Gr(e,1)]}call(e,t){return Q(()=>{this.invokeCallHook(e,t);let n=Xe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){const s=[0];for(let i=2;i<n.rank;++i)s.push(i);s.push(1),n=n.transpose(s)}return Ez(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}gL.className="Flatten",fe(gL);class yL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Kr(e.activation)}call(e,t){return Q(()=>{this.invokeCallHook(e,t);const n=Xe(e);return this.activation.apply(n)})}getConfig(){const e={activation:jr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}yL.className="Activation",fe(yL);class bL extends lt{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 Q(()=>(e=Xe(e),Rz(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}bL.className="RepeatVector",fe(bL);class wL extends lt{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 n="Total size of new array must be unchanged.",s=t.slice();let i=1,o=null;for(let c=0;c<s.length;++c){const h=s[c];if(this.isUnknown(h))if(o===null)o=c;else throw new q("Can only specifiy one unknown dimension.");else i*=h}const a=Gr(e);if(o!==null){if(i===0||a%i!==0)throw new q(n);s[o]=a/i}else if(a!==i)throw new q(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return Q(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=n.shape,i=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return n.reshape(i)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}wL.className="Reshape",fe(wL);class LL extends lt{constructor(e){super(e);if(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=ni(1,e.dims.length+1);if(!ae(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 Ln({ndim:this.dims.length+1})]}computeOutputShape(e){e=Nt(e);const t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ye(Xe(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}LL.className="Permute",fe(LL);class SL extends lt{constructor(e){super(e==null?{}: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 n=Xe(e),s=-1;return ih(Br(n,this.maskValue),s)}call(e,t){return Q(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=-1,i=!0,o=ih(Br(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}SL.className="Masking",fe(SL);class IL extends lt{constructor(e){super(e);if(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(Et(e.inputLength))}this.inputDim=e.inputDim,wn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,wn(this.outputDim,"outputDim"),this.embeddingsInitializer=Pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=zt(e.embeddingsRegularizer),this.activityRegularizer=zt(e.activityRegularizer),this.embeddingsConstraint=gn(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 Q(()=>this.maskZero?(e=Xe(e),Br(e,et(e))):null)}computeOutputShape(e){if(e=Nt(e),this.inputLength==null)return[...e,this.outputDim];const t=Et(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){const i=t[s],o=e[s+1];if(i!=null&&o!=null&&i!==o)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);i==null&&(t[n]=o),n++}}return[e[0],...t,this.outputDim]}call(e,t){return Q(()=>{this.invokeCallHook(e,t);let n=Xe(e);n.dtype!=="int32"&&(n=Wh(n,"int32"));const s=bv(this.embeddings.read(),n.as1D());return s.reshape(Nt(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Kt(this.embeddingsInitializer),embeddingsRegularizer:Ct(this.embeddingsRegularizer),activityRegularizer:Ct(this.activityRegularizer),embeddingsConstraint:fn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}IL.className="Embedding",fe(IL);class Vo extends lt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Pe}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 n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){const i=e[e.length-t.length+s],o=t[s];if(i==null||o==null||i<0||o<0)n.push(null);else if(i===1)n.push(o);else if(o===1)n.push(i);else{if(i!==o)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(i)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Nt(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=Vr(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&Vr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return Q(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=Yr(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=$h(o,1);n.push(o)}return this.mergeFunction(n)}else{let i=!1;for(const c of e){const h=c.rank;if(h==null){const d=c.shape,m=d[0],f=d.slice(1).concat([m]);let b=c.reshape([m].concat(Gr(d.slice(1))));b=Ye(b,[1,0]),b=b.reshape(f),n.push(b),i=!0}else if(h>1){const d=ni(1,h).concat([0]);n.push(Ye(c,d)),i=!0}else n.push(c)}let o=this.mergeFunction(n);const a=o.rank;if(i){if(a==null){const c=o.shape,h=c.length,d=c[h-1],m=[d].concat(c.slice(0,c.length-1));o=Ye(o.reshape([-1,d]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(ni(0,a-1));o=Ye(o,c)}}return o}}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 i=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let n=[];for(const s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Vr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return Q(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Zn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Us(n,t[s]);return n})}}class jh extends Vo{constructor(e){super(e)}mergeFunction(e){return Q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return t})}}jh.className="Add",fe(jh);function Ste(e){if(Array.isArray(e)){const t=new jh({});return t.apply(e)}else return new jh(e)}class Kh extends Vo{constructor(e){super(e)}mergeFunction(e){return Q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}}Kh.className="Multiply",fe(Kh);function Ite(e){if(Array.isArray(e)){const t=new Kh({});return t.apply(e)}else return new Kh(e)}class Xh extends Vo{constructor(e){super(e)}mergeFunction(e){return Q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return X(1/e.length,t)})}}Xh.className="Average",fe(Xh);function xte(e){if(Array.isArray(e)){const t=new Xh({});return t.apply(e)}else return new Xh(e)}class Jh extends Vo{constructor(e){super(e)}mergeFunction(e){return Q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=$s(t,e[n]);return t})}}Jh.className="Maximum",fe(Jh);function Tte(e){if(Array.isArray(e)){const t=new Jh({});return t.apply(e)}else return new Jh(e)}class Zh extends Vo{constructor(e){super(e)}mergeFunction(e){return Q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Oo(t,e[n]);return t})}}Zh.className="Minimum",fe(Zh);function Ate(e){if(Array.isArray(e)){const t=new Zh({});return t.apply(e)}else return new Zh(e)}class Qh extends Vo{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 q("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 n=[];for(let s=0;s<e.length;++s){const i=e[s].slice();i.splice(this.axis,1);let o=!1;for(const a of n)if(ae(a,i)){o=!0;break}o||n.push(i)}if(n.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return Q(()=>Sw(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(const i of t.slice(1)){if(n[s]==null||i[s]==null){n[s]=null;break}n[s]+=i[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return Q(()=>{let n=!0;if(t.forEach(o=>{if(o!=null){n=!1;return}}),n)return null;const s=[];for(let o=0;o<e.length;++o)t[o]==null?s.push(Fn(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(Zn(t[o],-1)):s.push(t[o]);const i=Yt(s,this.axis);return Qd(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Qh.className="Concatenate",fe(Qh);function vte(e){if(Array.isArray(e)){const t=new Qh({});return t.apply(e)}else return new Qh(e)}function eu(e,t){for(;e<0;)e+=t;return e}function lV(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Pe("batchDot is not implemented for tensors of 4D or higher rank yet");if(A(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),A(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Pe("batchDot is not implemented for complex64-type Tensors yet.");const s=e.shape.length,i=t.shape.length;n==null&&(n=[s-1,i-2]);const o=n;return Q(()=>{let a;if(s>i){a=s-i;const h=[];for(let d=0;d<a;++d)h.push(1);t=t.reshape(t.shape.concat(h))}else if(i>s){a=i-s;const h=[];for(let d=0;d<a;++d)h.push(1);e=e.reshape(e.shape.concat(h))}else a=0;let c;if(e.shape.length===2&&t.shape.length===2)o[0]===o[1]?c=e.mul(t).sum(o[0]):c=e.transpose([1,0]).mul(t).sum(o[1]);else{const h=o[0]!==e.shape.length-1,d=o[1]===t.shape.length-1;c=e.matMul(t,h,d)}if(a>0){let h;s>i?h=s+i-3:h=s-1;const d=[];for(let m=h;m<h+a;++m)d.push(m);c=c.squeeze(d)}return c.shape.length===1&&(c=c.expandDims(1)),c})}class xL extends Vo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){A(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],n=e[1];if(t.length>3||n.length>3)throw new Pe("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new q(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((i,o)=>eu(i,e[o].shape.length)):s=[eu(this.axes,t.shape.length),eu(this.axes,n.shape.length)],this.normalize&&(t=tm(t,s[0]),n=tm(n,s[1])),lV(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[eu(this.axes,e.length),eu(this.axes,t.length)],n}computeOutputShape(e){A(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(),n=e[1].slice();if(t.length>3||n.length>3)throw new Pe("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);const i=t.concat(n);return i.length===1&&i.push(1),i}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}xL.className="Dot",fe(xL);class TL extends lt{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 Q(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=()=>zp(n.shape,0,this.stddev).add(n),i=Bh(s,()=>n,t.training||!1);return i})}}TL.className="GaussianNoise",fe(TL);class AL extends lt{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 Q(()=>{this.invokeCallHook(e,t);const n=Xe(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(zp(n.shape,1,i))};return Bh(s,()=>n,t.training||!1)}return n})}}AL.className="GaussianDropout",fe(AL);class vL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Xe(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 Q(()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const i=Xe(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let h=Zi(ko(n),this.rate);h=Wh(h,"float32");const d=((1-this.rate)*(1+this.rate*c**2))**-.5,m=-d*c*this.rate,f=i.mul(h).add(h.add(-1).mul(c));return f.mul(d).add(m)};return Bh(s,()=>Xe(e),t.training||!1)}return e})}}vL.className="AlphaDropout",fe(vL);function tu(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=qT(e,t,n,s,i,o);else if(e.rank===3)a=jT(e,t,n,s,i,o);else if(e.rank===4)a=KT(e,t,n,s,i,o);else throw new Pe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function hV(e,t,n,s,i=.001){return Q(()=>{const o=fp(e,s),a=o.mean,c=o.variance,h=tu(e,a,c,n,t,i);return[h,a,c]})}function uV(e,t,n,s,i=.001){return Q(()=>{const o=fp(e,s),a=o.mean,c=o.variance,h=[];for(const L of ni(0,e.rank))s.indexOf(L)!==-1?h.push(1):h.push(e.shape[L]);const d=a.reshape(h),m=c.reshape(h),f=t==null?null:t.reshape(h),b=n==null?null:n.reshape(h),w=tu(e,d,m,b,f,i);return[w,a,c]})}function dV(e,t,n,s,i=.001){return ae(s.slice().sort(),ni(0,e.rank-1))?hV(e,t,n,s,i):uV(e,t,n,s,i)}class NL extends lt{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=Pt(e.betaInitializer||"zeros"),this.gammaInitializer=Pt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Pt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Pt(e.movingVarianceInitializer||"ones"),this.betaConstraint=gn(e.betaConstraint),this.gammaConstraint=gn(e.gammaConstraint),this.betaRegularizer=zt(e.betaRegularizer),this.gammaRegularizer=zt(e.gammaRegularizer)}build(e){e=Nt(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ln({ndim:e.length,axes:{[t]:n}})];const s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return Q(()=>{const n=t.training==null?!1:t.training,s=Xe(e),i=s.shape,o=i.length,a=ni(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const h=$o(1,o);h[c]=i[c];const d=a.slice();d.sort();const m=!ae(d,ni(0,o).slice(0,o-1)),f=()=>{if(m){const N=this.movingMean.read().reshape(h),O=this.movingVariance.read().reshape(h),E=this.center?this.beta.read().reshape(h):null,k=this.scale?this.gamma.read().reshape(h):null;return tu(s,N,O,E,k,this.epsilon)}else return tu(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return f();const[b,w,L]=dV(s,this.gamma.read(),this.beta.read(),a,this.epsilon),x=(N,O,E)=>{Q(()=>{const k=1-E,F=N.read(),U=F.sub(O).mul(k);N.write(F.sub(U))})},v=()=>{x(this.movingMean,w,this.momentum),x(this.movingVariance,L,this.momentum)};return v(),b})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Kt(this.betaInitializer),gammaInitializer:Kt(this.gammaInitializer),movingMeanInitializer:Kt(this.movingMeanInitializer),movingVarianceInitializer:Kt(this.movingVarianceInitializer),betaRegularizer:Ct(this.betaRegularizer),gammaRegularizer:Ct(this.gammaRegularizer),betaConstraint:fn(this.betaConstraint),gammaConstraint:fn(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}NL.className="BatchNormalization",fe(NL);class CL extends lt{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=Pt(e.betaInitializer||"zeros"),this.gammaInitializer=Pt(e.gammaInitializer||"ones"),this.betaRegularizer=zt(e.betaRegularizer),this.gammaRegularizer=zt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Nt(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=t);for(const i of this.axis)if(i<0||i>=t)throw new Error(`Invalid axis: ${i}`);if(this.axis.length!==Vr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map(i=>e[i]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){const n=Xe(e),s=n.shape,i=s.length;return Q(()=>{const o=!0;let{mean:a,variance:c}=fp(n,this.axis,o);const h=$o(1,i);for(const L of this.axis)h[L]=s[L];const d=L=>L!=null&&L.shape.length!==i&&this.axis!==[i-1]?L.reshape(h):L;let m=d(this.gamma.read()),f=d(this.beta.read());const b=[],w=[];for(let L=0;L<i;++L)this.axis.indexOf(L)!==-1?(b.push(s[L]),w.push(1)):(b.push(1),w.push(s[L]));return a=a.tile(b),c=c.tile(b),m=m.tile(w),f=f.tile(w),tu(n,a,c,f,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Kt(this.betaInitializer),gammaInitializer:Kt(this.gammaInitializer),betaRegularizer:Ct(this.betaRegularizer),gammaRegularizer:Ct(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}CL.className="LayerNormalization",fe(CL);function Nte(e,t){return Q(()=>{if(e.rank!==3)throw new q(`temporalPadding expects input tensor to be 3-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new q(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);const n=[[0,0],t,[0,0]];return vi(e,n)})}function pV(e,t,n){return Q(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ei()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],vi(e,s)})}class RL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ei():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ln({ndim:4})]}computeOutputShape(e){e=Nt(e);let t,n;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?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(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?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return Q(()=>pV(Xe(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}RL.className="ZeroPadding2D",fe(RL);function bm(e,t,n,s,i,o){return Q(()=>{jt(i),uv(o),vs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=ei()),o==null&&(o="max"),e=nL(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=fh(e,t,n,c):a=ah(e,t,n,c),i==="channelsFirst"&&(a=Ye(a,[0,3,1,2])),a})}function xN(e,t,n,s,i,o){return Q(()=>{jt(i),uv(o),vs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=ei()),o==null&&(o="max"),e=gN(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=Ob(e,t,n,c):a=yb(e,t,n,c),i==="channelsFirst"&&(a=Ye(a,[0,4,1,2,3])),a})}class TN extends lt{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 q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(wn(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 q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);wn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,vs(this.padding),this.inputSpec=[new Ln({ndim:3})]}computeOutputShape(e){e=Nt(e);const t=oi(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return Q(()=>{this.invokeCallHook(e,t),e=$h(Xe(e),2);const n=this.poolingFunction(Xe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Mr(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class OL extends TN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return jt(i),vs(s),bm(e,t,n,s,i,"max")}}OL.className="MaxPooling1D",fe(OL);class EL extends TN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return jt(i),vs(s),bm(e,t,n,s,i,"avg")}}EL.className="AveragePooling1D",fe(EL);class AN extends lt{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 q(`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];wn(this.poolSize,"poolSize"),wn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),vs(this.padding),this.inputSpec=[new Ln({ndim:4})]}computeOutputShape(e){e=Nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=oi(t,this.poolSize[0],this.padding,this.strides[0]),n=oi(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return Q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Xe(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 DL extends AN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return jt(i),vs(s),bm(e,t,n,s,i,"max")}}DL.className="MaxPooling2D",fe(DL);class kL extends AN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return jt(i),vs(s),bm(e,t,n,s,i,"avg")}}kL.className="AveragePooling2D",fe(kL);class vN extends lt{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 q(`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];wn(this.poolSize,"poolSize"),wn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),vs(this.padding),this.inputSpec=[new Ln({ndim:5})]}computeOutputShape(e){e=Nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=oi(t,this.poolSize[0],this.padding,this.strides[0]),n=oi(n,this.poolSize[1],this.padding,this.strides[1]),s=oi(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return Q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Xe(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 FL extends vN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return jt(i),vs(s),xN(e,t,n,s,i,"max")}}FL.className="MaxPooling3D",fe(FL);class _L extends vN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return jt(i),vs(s),xN(e,t,n,s,i,"avg")}}_L.className="AveragePooling3D",fe(_L);class NN extends lt{constructor(e){super(e);this.inputSpec=[new Ln({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Pe}}class WL extends NN{constructor(e){super(e||{})}call(e,t){return Q(()=>{const n=Xe(e);return qt(n,1)})}}WL.className="GlobalAveragePooling1D",fe(WL);class $L extends NN{constructor(e){super(e||{})}call(e,t){return Q(()=>{const n=Xe(e);return Qn(n,1)})}}$L.className="GlobalMaxPooling1D",fe($L);class CN extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),this.inputSpec=[new Ln({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Pe}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class UL extends CN{call(e,t){return Q(()=>{const n=Xe(e);return this.dataFormat==="channelsLast"?qt(n,[1,2]):qt(n,[2,3])})}}UL.className="GlobalAveragePooling2D",fe(UL);class BL extends CN{call(e,t){return Q(()=>{const n=Xe(e);return this.dataFormat==="channelsLast"?Qn(n,[1,2]):Qn(n,[2,3])})}}BL.className="GlobalMaxPooling2D",fe(BL);class RN extends lt{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,n={}){const s=t.layer,i=ri(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class ML extends RN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Nt(e),e.length<3)throw new q(`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=Nt(e);const t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return Q(()=>{e=Xe(e);const n=(o,a)=>{const c=Xe(this.layer.call(o,t));return[c,[]]},s=SN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}ML.className="TimeDistributed",fe(ML);function mV(e){Qa(xz,"BidirectionalMergeMode",e)}const fV="concat";class PL extends RN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=ri(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=ri(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?fV:e.mergeMode,mV(this.mergeMode),e.weights)throw new Pe("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,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,i;return this.returnState&&(i=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(i).concat(i.slice()):[n].concat(i).concat(i.slice()):ts(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=LN(e,n,s,this.numConstants);if(e=i.inputs,n=i.initialState,s=i.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);const o=[],a=[];if(n!=null){const h=n.length;if(h%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,o.push(...n);const d=n.map(m=>new Ln({shape:m.shape}));this.forwardLayer.stateSpec=d.slice(0,h/2),this.backwardLayer.stateSpec=d.slice(h/2),a.push(...d)}if(s!=null)throw new Pe("Support for constants in Bidirectional layers is not implemented yet.");const c=o[0]instanceof ii;for(const h of o)if(h instanceof ii!==c)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const f=super.apply(h,t);return this.inputSpec=m,f}else return super.apply(e,t)}call(e,t){return Q(()=>{const n=t.initialState;let s,i;if(n==null)s=this.forwardLayer.call(e,t),i=this.backwardLayer.call(e,t);else{const c=n.slice(0,n.length/2),h=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:c})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:h}))}let o;this.returnState&&(Array.isArray(s)&&(o=s.slice(1).concat(i.slice(1))),s=s[0],i=i[0]),this.returnSequences&&(i=Ts(i,1));let a;return this.mergeMode==="concat"?a=Sw([s,i]):this.mergeMode==="sum"?a=be(s,i):this.mergeMode==="ave"?a=X(.5,be(s,i)):this.mergeMode==="mul"?a=X(s,i):this.mergeMode==null&&(a=[s,i]),this.returnState?this.mergeMode==null?a.concat(o):[a].concat(o):a})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Bo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Bo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){const s=this.forwardLayer.states,i=s.map(o=>null);return Array.isArray(n)?n.concat(i).concat(i):[n].concat(i).concat(i)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),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 n=ri(t.layer);if(delete t.layer,t.numConstants!=null)throw new Pe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}PL.className="Bidirectional",fe(PL);function gV(e){return new nc(e)}function yV(e){return new Qw(e)}function bV(e){return new Xw(e)}function wV(e){return new Jw(e)}function LV(e){return new Zw(e)}function SV(e){return new tL(e)}function IV(e){return new eL(e)}function xV(e){return new dm(e)}function TV(e){return new Hh(e)}function AV(e){return new rL(e)}function 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pY=Object.freeze({__proto__:null,json:dY});const 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fY=Object.freeze({__proto__:null,json:mY});const gY=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}];var yY=Object.freeze({__proto__:null,json:gY});const bY=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var wY=Object.freeze({__proto__:null,json:bY});const LY=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var SY=Object.freeze({__proto__:null,json:LY});const IY=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}];var xY=Object.freeze({__proto__:null,json:IY});const TY=[{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}];var AY=Object.freeze({__proto__:null,json:TY});const vY=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool",notSupported:!0}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}];var NY=Object.freeze({__proto__:null,json:vY});const CY=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}];var RY=Object.freeze({__proto__:null,json:CY});const OY=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];var EY=Object.freeze({__proto__:null,json:OY});class BN{static get Instance(){return this._instance||(this._instance=new this)}constructor(){const e=[QG,tY,sY,rY,aY,lY,uY,wY,yY,pY,SY,xY,AY,NY,RY,EY,fY],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,s)=>(n[s.tfOpName]=s,n),{})}transformGraph(e,t={}){const n=e.node,s=[],i=[],o=[],a=n.reduce((L,x)=>(L[x.name]=this.mapNode(x),x.op.startsWith("Placeholder")?s.push(L[x.name]):x.op==="Const"?i.push(L[x.name]):(x.input==null||x.input.length===0)&&o.push(L[x.name]),L),{});let c=[];const h=[];let d={},m={};t!=null&&(d=this.mapSignatureEntries(t.inputs),m=this.mapSignatureEntries(t.outputs));const f=Object.keys(a);f.forEach(L=>{const x=a[L];x.inputNames.forEach(v=>{const[N]=or(v);x.inputs.push(a[N]),a[N].children.push(x)})}),Object.keys(m).length===0?f.forEach(L=>{const x=a[L];x.children.length===0&&h.push(x)}):Object.keys(m).forEach(L=>{const[x]=or(L),v=a[x];v!=null&&(v.signatureKey=m[L],h.push(v))}),Object.keys(d).length>0?Object.keys(d).forEach(L=>{const[x]=or(L),v=a[x];v&&(v.signatureKey=d[L],c.push(v))}):c=s;let b={};e.library!=null&&e.library.function!=null&&(b=e.library.function.reduce((L,x)=>(L[x.signature.name]=this.mapFunction(x),L),{}));const w={nodes:a,inputs:c,outputs:h,weights:i,placeholders:s,signature:t,functions:b};return o.length>0&&(w.initNodes=o),w}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){const t=UN(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});const n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(s=>s.startsWith("^")?s.substr(1):s),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((s,i)=>(s[i.name]={type:i.type,inputIndexStart:i.start,inputIndexEnd:i.end},s),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((s,i)=>{const o=i.type;let a;switch(i.type){case"string":a=HL(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=HL(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"string[]":a=eS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=eS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"number":a=jL(e.attr,i.tfName,i.defaultValue||0),a===void 0&&!!i.tfDeprecatedName&&(a=jL(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"number[]":a=QL(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=QL(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"bool":a=qL(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=qL(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"bool[]":a=nS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=nS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"shape":a=ZL(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=ZL(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"shape[]":a=tS(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=tS(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"dtype":a=XL(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=XL(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"dtype[]":a=JL(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=JL(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"func":a=PN(e.attr,i.tfName,i.defaultValue),a===void 0&&!!i.tfDeprecatedName&&(a=PN(e.attr,i.tfDeprecatedName,i.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${i.type} for op: ${e.op}`)}return s[i.name]={value:a,type:o},s},{})),n}mapFunction(e){const t=e.nodeDef,n=[],s=[];let i={};t!=null&&(i=t.reduce((m,f)=>(m[f.name]=this.mapNode(f),f.op==="Const"&&s.push(m[f.name]),m),{}));const o=[],a=[];e.signature.inputArg.forEach(m=>{const[f]=or(m.name),b={name:f,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:KL(m.type),type:"dtype"}},children:[]};b.signatureKey=m.name,o.push(b),i[f]=b});const c=Object.keys(i);c.forEach(m=>{const f=i[m];f.inputNames.forEach(b=>{const[w]=or(b);f.inputs.push(i[w]),i[w].children.push(f)})});const h=e.ret;e.signature.outputArg.forEach(m=>{const[f,b]=or(h[m.name]),w=i[f];w!=null&&(w.defaultOutput=b,a.push(w))});const d=this.mapArgsToSignature(e);return{nodes:i,inputs:o,outputs:a,weights:s,placeholders:n,signature:d}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}}function DY(e){const t=oe().global;if(typeof t.atob!="undefined")return t.atob(e);if(typeof Buffer!="undefined")return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. 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FY=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[be(R("a",e,t,n),R("b",e,t,n))];case"AddN":return[YT(R("tensors",e,t,n))];case"FloorMod":case"Mod":return[mp(R("a",e,t,n),R("b",e,t,n))];case"Mul":return[X(R("a",e,t,n),R("b",e,t,n))];case"RealDiv":case"Div":return[We(R("a",e,t,n),R("b",e,t,n))];case"DivNoNan":return[xb(R("a",e,t,n),R("b",e,t,n))];case"FloorDiv":return[Zd(R("a",e,t,n),R("b",e,t,n))];case"Sub":return[Re(R("a",e,t,n),R("b",e,t,n))];case"Minimum":return[Oo(R("a",e,t,n),R("b",e,t,n))];case"Maximum":return[$s(R("a",e,t,n),R("b",e,t,n))];case"Pow":return[Zs(R("a",e,t,n),R("b",e,t,n))];case"SquaredDifference":return[Ih(R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Rte="arithmetic";const _Y=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[dn(R("x",e,t,n))];case"Acos":return[ob(R("x",e,t,n))];case"Acosh":return[ab(R("x",e,t,n))];case"Asin":return[hb(R("x",e,t,n))];case"Asinh":return[ub(R("x",e,t,n))];case"Atan":return[db(R("x",e,t,n))];case"Atan2":return[pb(R("x",e,t,n),R("y",e,t,n))];case"Atanh":return[mb(R("x",e,t,n))];case"Ceil":return[bb(R("x",e,t,n))];case"Complex":return[ji(R("real",e,t,n),R("imag",e,t,n))];case"Cos":return[hh(R("x",e,t,n))];case"Cosh":return[op(R("x",e,t,n))];case"Elu":return[Ua(R("x",e,t,n))];case"Erf":return[Tb(R("x",e,t,n))];case"Exp":return[Is(R("x",e,t,n))];case"Expm1":return[Ab(R("x",e,t,n))];case"Floor":return[Ma(R("x",e,t,n))];case"Log":return[cs(R("x",e,t,n))];case"Log1p":return[hp(R("x",e,t,n))];case"Imag":return[dh(R("x",e,t,n))];case"Neg":return[Ht(R("x",e,t,n))];case"Reciprocal":return[_b(R("x",e,t,n))];case"Real":return[Ga(R("x",e,t,n))];case"Relu":return[Ni(R("x",e,t,n))];case"Round":return[$b(R("x",e,t,n))];case"Selu":return[bp(R("x",e,t,n))];case"Sigmoid":return[Ti(R("x",e,t,n))];case"Sin":return[wp(R("x",e,t,n))];case"Sign":return[Bb(R("x",e,t,n))];case"Sinh":return[Lp(R("x",e,t,n))];case"Softplus":return[za(R("x",e,t,n))];case"Sqrt":return[Nn(R("x",e,t,n))];case"Square":return[At(R("x",e,t,n))];case"Tanh":return[$a(R("x",e,t,n))];case"Tan":return[zb(R("x",e,t,n))];case"Relu6":case"ClipByValue":return[Jn(R("x",e,t,n),R("clipValueMin",e,t,n),R("clipValueMax",e,t,n))];case"Rsqrt":return[yp(ss(e.inputNames[0],t,n))];case"Prod":return[gp(R("x",e,t,n),R("axes",e,t,n))];case"LeakyRelu":return[lp(R("x",e,t,n),R("alpha",e,t,n))];case"Prelu":return[yh(R("x",e,t,n),R("alpha",e,t,n))];default:throw 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s=R("size",e,t,n),i=R("dtype",e,t,n),o=R("elementShape",e,t,n),a=R("dynamicSize",e,t,n),c=R("clearAfterRead",e,t,n),h=R("identicalElementShapes",e,t,n),d=R("name",e,t,n),m=new $Y(d,i,s,o,h,a,c);return n.addTensorArray(m),[m.idTensor,Ce(1)]}case"TensorArrayWriteV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.write(i,o),[a.idTensor]}case"TensorArrayReadV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=n.getTensorArray(s.id);return[o.read(i)]}case"TensorArrayGatherV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("dtype",e,t,n),a=n.getTensorArray(s.id);return[a.gather(i,o)]}case"TensorArrayScatterV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.scatter(i,o),[a.idTensor]}case"TensorArrayConcatV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id),o=R("dtype",e,t,n);return[i.concat(o)]}case"TensorArraySplitV3":{const s=R("tensorArrayId",e,t,n),i=R("tensor",e,t,n),o=R("lengths",e,t,n),a=n.getTensorArray(s.id);return a.split(o,i),[a.idTensor]}case"TensorArraySizeV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id);return[Ce(i.size(),"int32")]}case"TensorArrayCloseV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id);return i.clearAndClose(),[i.idTensor]}case"TensorListSetItem":{const s=R("tensorListId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorList(s.id);return a.setItem(i,o),[a.idTensor]}case"TensorListGetItem":{const s=R("tensorListId",e,t,n),i=R("index",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.getItem(i,o,a)]}case"TensorListScatterV2":case"TensorListScatter":{const s=R("indices",e,t,n),i=R("tensor",e,t,n),o=R("elementShape",e,t,n),a=R("numElements",e,t,n),c=MY(i,s,o,a);return n.addTensorList(c),[c.idTensor]}case"TensorListReserve":{const s=R("elementShape",e,t,n),i=R("elementDType",e,t,n),o=R("numElements",e,t,n),a=BY(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListGather":{const s=R("tensorListId",e,t,n),i=R("indices",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.gather(i,a,o)]}case"TensorListStack":{const s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=R("numElements",e,t,n),c=n.getTensorList(s.id);return[c.stack(i,o,a)]}case"TensorListFromTensor":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=UY(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListConcat":{const s=R("tensorListId",e,t,n),i=n.getTensorList(s.id),o=R("dtype",e,t,n),a=R("elementShape",e,t,n);return[i.concat(o,a)]}case"TensorListPushBack":{const s=R("tensorListId",e,t,n),i=R("tensor",e,t,n),o=n.getTensorList(s.id);return o.pushBack(i),[o.idTensor]}case"TensorListPopBack":{const s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=n.getTensorList(s.id);return[a.popBack(i,o)]}case"TensorListSplit":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("lengths",e,t,n),a=PY(s,o,i);return n.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ete="control";function VN(e,t,n){const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=s==="fusedbatchnorm",h=R("numArgs",e,t,n);if(o){if(a&&h!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&h!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(c)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");const d=R("strides",e,t,n),m=Sm(e,t,n),f=R("dataFormat",e,t,n).toUpperCase(),b=R("dilations",e,t,n),[w,L]=R("args",e,t,n);return{stride:d,pad:m,dataFormat:f,dilations:b,biasArg:w,preluArg:L,activationFunc:i}}const VY=(e,t,n)=>{switch(e.op){case"Conv1D":{const s=R("stride",e,t,n),i=R("pad",e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilation",e,t,n);return[ip(R("x",e,t,n),R("filter",e,t,n),s,i,o,a)]}case"Conv2D":{const s=R("strides",e,t,n),i=Sm(e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilations",e,t,n);return[Ji(R("x",e,t,n),R("filter",e,t,n),[s[1],s[2]],i,o,[a[1],a[2]])]}case"_FusedConv2D":{const{stride:s,pad:i,dataFormat:o,dilations:a,biasArg:c,preluArg:h,activationFunc:d}=VN(e,t,n);return[Kb({x:R("x",e,t,n),filter:R("filter",e,t,n),strides:[s[1],s[2]],pad:i,dataFormat:o,dilations:[a[1],a[2]],bias:c,activation:d,preluActivationWeights:h})]}case"FusedDepthwiseConv2dNative":{const{stride:s,pad:i,dataFormat:o,dilations:a,biasArg:c,preluArg:h,activationFunc:d}=VN(e,t,n);return[$A({x:R("x",e,t,n),filter:R("filter",e,t,n),strides:[s[1],s[2]],pad:i,dataFormat:o,dilations:[a[1],a[2]],bias:c,activation:d,preluActivationWeights:h})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{const s=R("outputShape",e,t,n),i=R("strides",e,t,n),o=Sm(e,t,n);return[rp(R("x",e,t,n),R("filter",e,t,n),s,[i[1],i[2]],o)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{const s=R("strides",e,t,n),i=Sm(e,t,n),o=R("dilations",e,t,n),a=R("dataFormat",e,t,n).toUpperCase();return[Co(R("input",e,t,n),R("filter",e,t,n),[s[1],s[2]],i,a,[o[1],o[2]])]}case"Conv3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilations",e,t,n);return[Lb(R("x",e,t,n),R("filter",e,t,n),[s[1],s[2],s[3]],i,o,[a[1],a[2],a[3]])]}case"AvgPool":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[ah(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i)]}case"MaxPool":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[fh(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i)]}case"MaxPoolWithArgmax":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n),a=R("includeBatchInIndex",e,t,n),{result:c,indexes:h}=lA(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i,a);return[c,h]}case"AvgPool3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[yb(R("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],i)]}case"MaxPool3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[Ob(R("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],i)]}case"Dilation2D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("dilations",e,t,n),a=s[1],c=s[2],h=o[1],d=o[2];return[Ib(R("x",e,t,n),R("filter",e,t,n),[a,c],i,[h,d],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Dte="convolution";const GY=(e,t,n)=>{switch(e.op){case"Fill":{const s=R("shape",e,t,n),i=R("dtype",e,t,n),o=R("value",e,t,n);return[Ba(s,o,i)]}case"LinSpace":{const s=R("start",e,t,n),i=R("stop",e,t,n),o=R("num",e,t,n);return[oA(s,i,o)]}case"Multinomial":{const s=R("logits",e,t,n),i=R("numSamples",e,t,n),o=R("seed",e,t,n);return[hA(s,i,o)]}case"OneHot":{const s=R("indices",e,t,n),i=R("depth",e,t,n),o=R("onValue",e,t,n),a=R("offValue",e,t,n);return[To(s,i,o,a)]}case"Ones":return[Js(R("shape",e,t,n),R("dtype",e,t,n))];case"OnesLike":return[Fn(R("x",e,t,n))];case"RandomUniform":return[ko(R("shape",e,t,n),R("minval",e,t,n),R("maxval",e,t,n),R("dtype",e,t,n))];case"Range":{const s=R("start",e,t,n),i=R("stop",e,t,n),o=R("step",e,t,n);return[bh(s,i,o,R("dtype",e,t,n))]}case"TruncatedNormal":{const s=R("shape",e,t,n),i=R("mean",e,t,n),o=R("stdDev",e,t,n),a=R("seed",e,t,n);return[xh(s,i,o,R("dtype",e,t,n),a)]}case"Zeros":return[dt(R("shape",e,t,n),R("dtype",e,t,n))];case"ZerosLike":return[et(R("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},kte="creation";function sS(e,t,n){const s=R("boxes",e,t,n),i=R("scores",e,t,n),o=R("maxOutputSize",e,t,n),a=R("iouThreshold",e,t,n),c=R("scoreThreshold",e,t,n),h=R("softNmsSigma",e,t,n);return{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}}const YY=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}=sS(e,t,n),d=await zr.nonMaxSuppressionWithScoreAsync(s,i,o,a,c,h);return[d.selectedIndices,d.selectedScores]}case"NonMaxSuppressionV4":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=sS(e,t,n),h=R("padToMaxOutputSize",e,t,n),d=await zr.nonMaxSuppressionPaddedAsync(s,i,o,a,c,h);return[d.selectedIndices,d.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=sS(e,t,n);return[await zr.nonMaxSuppressionAsync(s,i,o,a,c)]}case"Where":{const s=Ae(R("condition",e,t,n),"bool"),i=[await Yb(s)];return s.dispose(),i}case"ListDiff":return dA(R("x",e,t,n),R("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},Fte="dynamic";const HY=(e,t,n)=>{switch(e.op){case"TopKV2":{const s=R("x",e,t,n),i=R("k",e,t,n),o=R("sorted",e,t,n),a=Vb(s,i,o);return[a.values,a.indices]}case"Unique":{const s=R("x",e,t,n),i=Tp(s);return[i.values,i.indices]}case"UniqueV2":{const s=R("x",e,t,n),i=R("axis",e,t,n),o=Tp(s,i);return[o.values,o.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},_te="evaluation";const qY=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const s=R("default",e,t,n);return[ss(e.name,t,n)||s];case"Placeholder":return[ss(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{const d=R("x",e,t,n);return[ar(d)]}case"IdentityN":return R("x",e,t,n).map(d=>ar(d));case"Snapshot":const i=R("x",e,t,n);return[ar(i)];case"Shape":return[ls(R("x",e,t,n).shape,"int32")];case"ShapeN":return R("x",e,t,n).map(d=>ls(d.shape));case"Size":return[Ce(R("x",e,t,n).size,"int32")];case"Rank":return[Ce(R("x",e,t,n).rank,"int32")];case"NoOp":return[Ce(1)];case"Print":const o=R("x",e,t,n),a=R("data",e,t,n),c=R("message",e,t,n),h=R("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(c);for(let d=0;d<a.length;d++)console.log(Array.prototype.slice.call(a[d].dataSync()).slice(0,h));return[o];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Wte="graph";class jY{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ce(0),this.tensorMap=new Map,bn(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);const n=await e.data();return this.tensorMap.forEach(s=>s.dispose()),this.tensorMap.clear(),Q(()=>{const s=Qs(t),i=n.length,o=s.length;A(i===o,()=>`The number of elements doesn't match, keys has ${i} elements, the values has ${o} elements.`);for(let a=0;a<i;a++){const 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i=R("tableHandle",e,t,n,s),o=R("keys",e,t,n),a=R("defaultValue",e,t,n),c=s.getHashTableById(i.id);return[await c.find(o,a)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},$te="hash_table";const XY=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{const s=R("images",e,t,n),i=R("size",e,t,n),o=R("alignCorners",e,t,n);return[zr.resizeBilinear(s,[i[0],i[1]],o)]}case"ResizeNearestNeighbor":{const s=R("images",e,t,n),i=R("size",e,t,n),o=R("alignCorners",e,t,n);return[zr.resizeNearestNeighbor(s,[i[0],i[1]],o)]}case"CropAndResize":{const s=R("image",e,t,n),i=R("boxes",e,t,n),o=R("boxInd",e,t,n),a=R("cropSize",e,t,n),c=R("method",e,t,n),h=R("extrapolationValue",e,t,n);return[zr.cropAndResize(s,i,o,a,c,h)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ute="image";const JY=(e,t,n)=>{switch(e.op){case"Equal":return[Xs(R("a",e,t,n),R("b",e,t,n))];case"NotEqual":return[Br(R("a",e,t,n),R("b",e,t,n))];case"Greater":return[xs(R("a",e,t,n),R("b",e,t,n))];case"GreaterEqual":return[Zi(R("a",e,t,n),R("b",e,t,n))];case"Less":return[ph(R("a",e,t,n),R("b",e,t,n))];case"LessEqual":return[Ur(R("a",e,t,n),R("b",e,t,n))];case"LogicalAnd":return[Us(R("a",e,t,n),R("b",e,t,n))];case"LogicalNot":return[mh(R("a",e,t,n))];case"LogicalOr":return[pp(R("a",e,t,n),R("b",e,t,n))];case"Select":case"SelectV2":return[Bn(R("condition",e,t,n),R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Bte="logical";const ZY=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[ct(R("a",e,t,n),R("b",e,t,n),R("transposeA",e,t,n),R("transposeB",e,t,n))];case"Transpose":return[Ye(R("x",e,t,n),R("perm",e,t,n))];case"_FusedMatMul":const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=R("numArgs",e,t,n);if(o){if(a&&c!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&c!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[h,d]=R("args",e,t,n);return[Ep({a:R("a",e,t,n),b:R("b",e,t,n),transposeA:R("transposeA",e,t,n),transposeB:R("transposeB",e,t,n),bias:h,activation:i,preluActivationWeights:d})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Mte="matrices";const QY=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[No(R("x",e,t,n),R("mean",e,t,n),R("variance",e,t,n),R("offset",e,t,n),R("scale",e,t,n),R("epsilon",e,t,n))];case"FusedBatchNormV3":return[No(R("x",e,t,n),R("mean",e,t,n),R("variance",e,t,n),R("offset",e,t,n),R("scale",e,t,n),R("epsilon",e,t,n))];case"LRN":return[Nb(R("x",e,t,n),R("radius",e,t,n),R("bias",e,t,n),R("alpha",e,t,n),R("beta",e,t,n))];case"Softmax":return[Fo(R("x",e,t,n))];case"LogSoftmax":return[dp(R("x",e,t,n))];case"SparseToDense":return[Hb(R("sparseIndices",e,t,n),R("outputShape",e,t,n),R("sparseValues",e,t,n),R("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Pte="normalization";const eH=(e,t,n)=>{switch(e.op){case"Max":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[Qn(R("x",e,t,n),s,i)]}case"Mean":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[qt(R("x",e,t,n),s,i)]}case"Min":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[Va(R("x",e,t,n),s,i)]}case"Sum":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[$e(R("x",e,t,n),s,i)]}case"All":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[Qd(R("x",e,t,n),s,i)]}case"Any":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[ih(R("x",e,t,n),s,i)]}case"ArgMax":{const s=R("axis",e,t,n);return[rh(R("x",e,t,n),s)]}case"ArgMin":{const s=R("axis",e,t,n);return[lb(R("x",e,t,n),s)]}case"Prod":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[gp(R("x",e,t,n),s,i)]}case"Cumsum":{const s=R("axis",e,t,n),i=R("exclusive",e,t,n),o=R("reverse",e,t,n);return[ap(R("x",e,t,n),s,i,o)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},zte="reduction";const tH=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{const s=R("n",e,t,n),i=R("axis",e,t,n);let o=R("tensors",e,t,n);return o=o.slice(0,s),[Yt(o,i)]}case"GatherV2":case"Gather":{const s=R("axis",e,t,n),i=R("x",e,t,n),o=R("indices",e,t,n);return[Pa(i,Ae(o,"int32"),s)]}case"ReverseV2":case"Reverse":{const s=R("axis",e,t,n),i=R("x",e,t,n);return[Ts(i,s)]}case"Slice":{const s=R("begin",e,t,n),i=R("size",e,t,n);return[tt(R("x",e,t,n),s,i)]}case"StridedSlice":{const s=R("begin",e,t,n),i=R("end",e,t,n),o=R("strides",e,t,n),a=R("beginMask",e,t,n),c=R("endMask",e,t,n),h=R("ellipsisMask",e,t,n),d=R("newAxisMask",e,t,n),m=R("shrinkAxisMask",e,t,n),f=R("x",e,t,n);return[Pb(f,s,i,o,a,c,h,d,m)]}case"Pack":return Q(()=>{const s=R("axis",e,t,n),i=R("tensors",e,t,n),o=i[0].shape,a=Mr(i[0]).shape,c=i.map(h=>{const d=ae(h.shape,o);if(!d&&!ae(Mr(h).shape,a))throw new Error("the input tensors shape does not match");return d?h:K(h,o)});return[es(c,s)]});case"Unpack":{const s=R("axis",e,t,n),i=R("tensor",e,t,n);return Qs(i,s)}case"Tile":{const s=R("reps",e,t,n);return[$r(R("x",e,t,n),s)]}case"Split":case"SplitV":{const s=R("axis",e,t,n),i=R("numOrSizeSplits",e,t,n),o=R("x",e,t,n);return hs(o,i,s)}case"ScatterNd":{const s=R("indices",e,t,n),i=R("values",e,t,n),o=R("shape",e,t,n);return[EA(s,i,o)]}case"GatherNd":{const s=R("x",e,t,n),i=R("indices",e,t,n);return[DA(s,i)]}case"SparseToDense":{const s=R("sparseIndices",e,t,n),i=R("outputShape",e,t,n),o=R("sparseValues",e,t,n),a=R("defaultValue",e,t,n);return[Hb(s,o,i,o.dtype===a.dtype?a:Ae(a,o.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Vte="slice_join";const nH=(e,t,n)=>{switch(e.op){case"FFT":return[Lh(R("x",e,t,n))];case"IFFT":return[qa(R("x",e,t,n))];case"RFFT":return[Sh(R("x",e,t,n))];case"IRFFT":return[xp(R("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Gte="spectral";const sH=(e,t,n)=>{switch(e.op){case"Cast":return[Ae(R("x",e,t,n),R("dtype",e,t,n))];case"ExpandDims":{const s=R("axis",e,t,n);return[Zn(R("x",e,t,n),s)]}case"Squeeze":{const s=R("axis",e,t,n);return[Mr(R("x",e,t,n),s)]}case"Reshape":return[K(R("x",e,t,n),R("shape",e,t,n))];case"MirrorPad":return[Eb(R("x",e,t,n),R("padding",e,t,n),R("mode",e,t,n))];case"PadV2":case"Pad":return[vi(R("x",e,t,n),R("padding",e,t,n),R("constantValue",e,t,n))];case"SpaceToBatchND":{const s=R("blockShape",e,t,n),i=R("paddings",e,t,n);return[gh(R("x",e,t,n),s,i)]}case"BatchToSpaceND":{const s=R("blockShape",e,t,n),i=R("crops",e,t,n);return[ch(R("x",e,t,n),s,i)]}case"DepthToSpace":{const s=R("blockSize",e,t,n),i=R("dataFormat",e,t,n).toUpperCase();return[Sb(R("x",e,t,n),s,i)]}case"BroadcastTo":return[lh(R("x",e,t,n),R("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Yte="transformation";function GN(e,t,n,s){const i=((o,a,c)=>{switch(o.category){case"arithmetic":return Q(()=>FY(o,a,c));case"basic_math":return Q(()=>_Y(o,a,c));case"control":return zY(o,a,c);case"convolution":return Q(()=>VY(o,a,c));case"creation":return Q(()=>GY(o,a,c));case"dynamic":return YY(o,a,c);case"evaluation":return Q(()=>HY(o,a,c));case"image":return Q(()=>XY(o,a,c));case"graph":return Q(()=>qY(o,a,c));case"logical":return Q(()=>JY(o,a,c));case"matrices":return Q(()=>ZY(o,a,c));case"normalization":return Q(()=>QY(o,a,c));case"reduction":return Q(()=>eH(o,a,c));case"slice_join":return Q(()=>tH(o,a,c));case"spectral":return Q(()=>nH(o,a,c));case"transformation":return Q(()=>sH(o,a,c));case"hash_table":return KY(o,a,c,s);case"custom":const h=UN(o.op);if(h&&h.customExecutor)return h.customExecutor(new kY(o,a,c));throw TypeError(`Custom op ${o.op} is not registered.`);default:throw TypeError(`Unknown op '${o.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return bo(i)?i.then(o=>[].concat(o)):[].concat(i)}class YN{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function HN(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const h=new Set,d=Object.keys(e).map(b=>ds(b)[0]);let m=[];s!=null&&(m=s.map(b=>ds(b.name)[0]));const f=[...t];for(;f.length>0;){const b=f.pop();if((qN(b)||cH(b)||lH(b))&&(a==null&&(a=b,c=a.children.map(w=>w.name).filter(w=>i.has(w)))),i.add(b.name),n[b.name]!=null)continue;if(d.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){o.push(b.name);continue}b.inputs.forEach(w=>{if(h.has(w.name))return;h.add(w.name),f.push(w)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:o,dynamicNode:a,syncInputs:c}}function iH(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>ds(m)[0]).map(m=>e.nodes[m]),c=e.initNodes;a.forEach(m=>{s.has(m.name)&&o.push(m)}),e.weights.forEach(m=>{s.has(m.name)&&o.push(m)}),c!=null&&c.forEach(m=>{s.has(m.name)&&o.push(m)});const h=new Set,d=[];for(;o.length>0;){const m=o.pop();h.add(m.name),t[m.name]||d.push(m),m.children.forEach(f=>{!h.has(f.name)&&s.has(f.name)&&f.inputs.every(b=>h.has(b.name))&&o.push(f)})}return d}const rH=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],oH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],aH=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function qN(e){return rH.indexOf(e.op)>=0}function cH(e){return oH.indexOf(e.op)>=0}function lH(e){return aH.indexOf(e.op)>=0}class iS{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new iS(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){const n=e.map(i=>i.name).sort(),s=t.map(i=>i.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){const n=HN(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:i,syncInputs:o}=n;if(i!=null)throw new Error(`This execution contains the node '${i.name}', which has the dynamic op '${i.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${o}]`);if(s.length>0){const a=t.map(h=>h.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${c}]. Missing the following inputs: [${s}]`)}return iH(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const s=n.map(m=>this.graph.nodes[ds(m)[0]]),i=t.map(m=>ds(m)[0]);let o=i.map(m=>this.graph.nodes[m]);o.length===0&&(o=this._outputs);const a=this.getCompilationKey(s,o);let c=this.compiledMap.get(a);c==null&&(c=this.compile(e,o),this.compiledMap.set(a,c));const h={},d={};return Q(()=>{const m=new YN(this.weightMap,h,d,this.functionExecutorMap),f=Object.assign({},this.weightMap);Object.keys(e).forEach(L=>{const[x,v]=ds(L),N=[];N[v]=e[L],f[x]=N});const b=this.getFrozenTensorIds(f),w={};for(let L=0;L<c.length;L++){const x=c[L];if(!f[x.name]){const v=GN(x,f,m,this._resourceManager);if(bo(v))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=v,this.checkTensorForDisposal(x.name,x,f,m,b,i,w)}}return this.parent==null&&m.dispose(b),t.map(L=>ss(L,f,m))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,i,o,a){if(t.category==="control"||o.indexOf(e)!==-1)return;n[e].forEach(c=>{c!=null&&(a[c.id]=(a[c.id]||0)+t.children.length)}),t.inputs.forEach(c=>{if(c.category!=="control"){const h=JG(c.name,n,s);h!=null&&h.forEach(d=>{if(d&&!i.has(d.id)){const m=a[d.id];m===1?(d.dispose(),delete a[d.id]):m!=null&&a[d.id]--}})}})}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},i={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const o=new YN(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(f=>ss(f,a,o)),h=c.map(f=>f.id),d=Object.keys(e).map(f=>e[f].id),m=new Set([...h,...d,...this.weightIds]);return Object.keys(a).forEach(f=>{const b=a[f];b.forEach(w=>{w&&!w.isDisposed&&!m.has(w.id)&&w.dispose()})}),this.parent==null&&o.dispose(m),c}async executeFunctionAsync(e,t,n){const s=e.reduce((i,o,a)=>(i[this.inputs[a].name]=o,i),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){const i=Object.keys(e),o=i.map(O=>this.graph.nodes[ds(O)[0]]),a=n.map(O=>ds(O)[0]);let c=a.map(O=>this.graph.nodes[O]);c.length===0&&(c=this._outputs);const{usedNodes:h,missingInputs:d,dynamicNode:m,syncInputs:f}=HN(e,c,this.weightMap,this._initNodes),b=[...o,...this.graph.weights,...this._initNodes||[]].map(O=>({node:O,contexts:t.currentContext})),w=Object.assign({},this.weightMap);Object.keys(e).forEach(O=>{const[E,k]=ds(O),F=[];F[k]=e[O],w[E]=F});const L={},x=this.getFrozenTensorIds(w),v={};for(;b.length>0;){const O=this.processStack(o,b,t,w,v,x,a,L,h);await Promise.all(O)}m==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const N=c.filter(O=>!qN(O)&&!ss(O.name,w,t)).map(O=>O.name);if(N.length>0){let O="";throw m!=null&&(O=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${i}]. Consider providing the following inputs: [${d}]. ${O}`)}return w}processStack(e,t,n,s,i,o,a,c,h){const d=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let f="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([f]=or(m.node.name,n)),s[m.node.name]==null){const b=GN(m.node,s,n,this._resourceManager);f||([f]=or(m.node.name,n));const w=n.currentContext;bo(b)?d.push(b.then(L=>(s[f]=L,n.currentContext=w,this.checkTensorForDisposal(f,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h),L))):(s[f]=b,this.checkTensorForDisposal(f,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h))}else this.processChildNodes(m.node,t,n,s,i,h)}return d}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=or(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(h=>!!ss(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(h=>!!ss(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=ds(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,h)=>o[h]===-1||o[h]===c);A(a,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${o}], but was [${n.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&A(n.dtype===i.attrParams.dtype.value,()=>`The dtype of dict['${i.name}'] provided in model.execute(dict) must be ${i.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=ds(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null){const n=this._signature.outputs[t];return n.name}return t},{})}checkOutputs(e){e.forEach(t=>{const[n]=ds(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}class hH{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}const uH="?tfjs-format=file",dH="model.json";class jN{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new hH}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Yd(e,this.loadOptions);else{const t=Vy(e,this.loadOptions);if(t.length===0)t.push(Yd(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n={};this.artifacts.userDefinedMetadata!=null&&(n=this.artifacts.userDefinedMetadata.signature),this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const s=Pd(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new iS(BN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null){const i=BN.Instance.transformGraph(e.modelInitializer);this.initializer=new iS(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=zy(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof ee)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function pH(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${dH}${uH}`));const n=new jN(e,t);return await n.load(),n}const KN="2.7.0";function mH(e,t){return Im(e,t)}function Im(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(i.recurse)if(cc(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],h=Im(c,t,n,s);o[a]=h}return s.delete(e),o}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,i.value),i.value}function fH(e,t=JN){return XN(e,t)}function XN(e,t,n=new Set){const s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(i.recurse)if(cc(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(d=>d[a]),h=XN(c,t,n);o[a]=h}return n.delete(s),o}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return i.value}function JN(e){return e===null?null:cc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function ZN(e,t){const n=new Map;Im(e,t,n);for(const i of Array.from(n.keys())){const o=n.get(i);if(bo(o)){const a=await o;n.set(i,a)}}const s=Im(e,t,n);return s}function cc(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof ee))}function gH(e){return e==null||yH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof ee||hn(e)}function yH(e){return e===null||typeof e!="object"&&typeof e!="function"}function bH(e){return mH(e,wH)}function wH(e){return e instanceof ee?{value:e.clone(),recurse:!1}:cc(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class QN{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class rS extends QN{constructor(){super(rS.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}rS.INITIAL_CAPACITY=32;function e0(e){return new SH(e)}function Hte(e){let t=e;return su(()=>({value:t++,done:!1}))}function su(e){return new IH(e)}function t0(e,t){return new s0(e,t)}function qte(e,t,n){return t0(su(e).take(t),n)}function LH(e,t=Jr.FAIL){return new EH(e,t)}class Sn{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new RH(this,e)}filter(e){return new NH(this,e)}map(e){return new CH(this,e)}mapAsync(e){return new n0(this,e)}serialMapAsync(e){return new n0(this,e).serial()}flatmap(e){return new OH(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new vH(this,e,t)}columnMajorBatch(e,t=!0,n=JN){const s=this.rowMajorBatch(e,t);return s.map(i=>fH(i,n))}concatenate(e,t){return new s0(e0([this,e]),t)}take(e){return e<0||e==null?this:new AH(this,e)}skip(e){return e<0||e==null?this:new TH(this,e)}prefetch(e){return new i0(this,e)}shuffle(e,t){return new DH(this,e,t)}serial(){return new xH(this)}}class SH extends Sn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:bH(e),done:!1}}}class IH extends Sn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class xH extends Sn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class TH extends Sn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;He(e.value)}return this.upstream.next()}}class AH extends Sn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class vH extends Sn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class NH extends Sn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;He(e.value)}}}class CH extends Sn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Hi(e.value),n=this.transform(e.value),s=Hi(n);for(const i of t)Bd(i,s)||i.dispose();return{value:n,done:!1}}}class RH extends Sn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class n0 extends Sn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Hi(e.value),n=await this.transform(e.value),s=Hi(n);for(const i of t)Bd(i,s)||i.dispose();return{value:n,done:!1}}}class oS extends Sn{constructor(){super();this.outputQueue=new rS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class OH extends oS{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=Hi(e.value),n=this.transform(e.value),s=Hi(n);this.outputQueue.pushAll(n);for(const i of t)Bd(i,s)||i.dispose();return!0}}class s0 extends Sn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){const e="TODO: fill in upstream of chained summaries";return`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Jr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Jr||(Jr={}));class EH extends Sn{constructor(e,t=Jr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,n=0;function s(o){if(o instanceof Sn){const a=o.next();return{value:a.then(c=>(t++,c.done&&n++,c.value)),recurse:!1}}else return{value:null,recurse:!0}}const i=await ZN(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Jr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Jr.SHORTEST:return{value:null,done:!0};case Jr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class i0 extends Sn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new QN(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class DH extends i0{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Ha(n||jn().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}}class lc{constructor(){this.size=null}batch(e,t=!0){const n=this;A(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let s;return this.size===Infinity||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),ps(async()=>(await n.iterator()).columnMajorBatch(e,t,_H),s)}concatenate(e){const t=this;let n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,ps(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,ps(async()=>(await t.iterator()).filter(s=>Q(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return ps(async()=>(await t.iterator()).map(n=>Q(()=>e(n))),this.size)}mapAsync(e){const t=this;return ps(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return ps(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,ps(async()=>{const s=su(async()=>({value:await t.iterator(),done:!1}));return t0(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,ps(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=Ha(t||jn().toString());return ps(async()=>{let o=i.int32();return n&&(o+=i.int32()),(await s.iterator()).shuffle(e,o.toString())},this.size)}take(e){const t=this;let n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,ps(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}lc.MAX_BUFFER_SIZE=1e4;function ps(e,t=null){return new class extends lc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function kH(e){return ps(async()=>e0(e),e.length)}function FH(e){if(!cc(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(const n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return ps(async()=>{const n=await ZN(e,s=>{if(s instanceof lc)return{value:s.iterator(),recurse:!1};if(cc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return LH(n,Jr.SHORTEST)},t)}function _H(e){if(e===null)return null;const t=e[0];if(gH(t)){const n=WH(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function WH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof ee?es(e):sn(e)}class r0 extends lc{constructor(e){super();this.input=e}async iterator(){const e=await this.input.iterator(),t=e.decodeUTF8(),n=t.split(`
`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s));return n}}const xm='"',iu=Symbol("out"),o0=Symbol("field"),Tm=Symbol("quote"),aS=Symbol("quoteafterquote"),a0=Symbol("quoteinquote");class c0 extends lc{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new r0(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(A(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&A(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce((s,i)=>(s[i]=s[i]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(A(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs)for(const s of Object.keys(this.columnConfigs)){const i=this.fullColumnNames.indexOf(s);if(i===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value,s=this.parseRow(n,!1);return s}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){const t=this.parseRow(e),n={},s={};for(let i=0;i<this.fullColumnNames.length;i++){const o=this.fullColumnNames[i],a=this.columnConfigs?this.columnConfigs[o]:null;if(this.configuredColumnsOnly&&!a)continue;{const c=t[i];let h=null;if(c==="")if(a&&a.default!==void 0)h=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${o} is empty in this line: ${e}`);h=void 0}else{const d=Number(c);if(isNaN(d))a&&a.dtype==="bool"?h=this.getBoolean(c):h=c;else if(!a||!a.dtype)h=d;else switch(a.dtype){case"float32":h=d;break;case"int32":h=Math.floor(d);break;case"bool":h=this.getBoolean(c);break;default:h=d}}a&&a.isLabel?s[o]=h:n[o]=h}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){const n=[];let s=0;const i=e.length;let o=iu;for(let a=0;a<i;a++)switch(o){case iu:switch(e.charAt(a)){case xm:s=a+1,o=Tm;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=iu;break;default:o=o0,s=a;break}break;case o0:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=iu,s=a+1;break;default:}break;case Tm:switch(e.charAt(a)){case xm:o=aS;break;default:}break;case aS:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=iu,s=a+1;break;case xm:o=Tm;break;default:o=a0;break}break;case a0:switch(e.charAt(a)){case xm:o=Tm;break;default:}break;default:}if(o===aS?n.push(e.substring(s,i-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}}class l0 extends Sn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!(e.includeSpectrogram===!1),this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(oe().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new l0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise(s=>{const i=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(i),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,i)=>n.set(s,i*t)),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(P(t));return n.set(e,n.length-e.length),sn(n,t)}}class h0 extends Sn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=ls([0],"int32"),this.webcamConfig.centerCrop){const n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,i=(1-n)/2,o=(1-s)/2,a=i+n,c=s+o;this.cropBox=Pr([o,i,c,a],[1,4])}else this.cropBox=Pr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(oe().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new h0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&A(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=OT(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return Q(()=>{const t=e.toFloat().expandDims(0);let n;n=zr.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const s=n.shape;return n.reshape(s.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class u0{}class d0 extends Sn{split(e){return new $H(this,e)}}class $H extends d0{constructor(e,t){super();this.upstream=e,this.impl=new UH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class UH extends oS{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}}class BH extends Sn{decodeUTF8(){return new MH(this)}}class MH extends d0{constructor(e){super();this.upstream=e,this.impl=new PH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class PH extends oS{constructor(e){super();if(this.upstream=e,oe().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=require("string_decoder");this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let n;return oe().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}}class p0 extends BH{constructor(e,t={}){super();this.file=e,this.options=t,A(e instanceof Uint8Array||(oe().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,n)=>{const s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{const i=new FileReader;i.onload=a=>{let c=i.result;if(c instanceof ArrayBuffer&&(c=new Uint8Array(c)),!(c instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(c)},i.onabort=a=>n(new Error("Aborted")),i.onerror=a=>n(new Error(a.type));const o=this.file.slice(this.offset,s);i.readAsArrayBuffer(o)}this.offset=s});return{value:await e,done:!1}}}async function zH(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=VH(e));const i=await nT(n,s);if(i.ok){const o=new Uint8Array(await i.arrayBuffer());return new p0(o,t)}else throw new Error(i.statusText)}const VH=e=>{const t={method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity};return t};function m0(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class f0 extends u0{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(m0(this.input)&&oe().get("IS_NODE")){const e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new p0(this.input,this.options)}}class g0 extends u0{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return m0(this.url)?new f0(this.url,this.fileOptions).iterator():zH(this.url,this.fileOptions)}}function GH(e,t={}){return new c0(new g0(e),t)}function YH(e){const t=su(e);return ps(async()=>t)}function HH(e){return ps(async()=>{const t=await e();return su(()=>t.next())})}async function qH(e,t){return h0.create(e,t)}async function jH(e){return l0.create(e)}const y0="2.7.0";var KH=Object.freeze({__proto__:null,array:kH,Dataset:lc,zip:FH,CSVDataset:c0,TextLineDataset:r0,csv:GH,func:YH,generator:HH,microphone:jH,webcam:qH,FileDataSource:f0,URLDataSource:g0,version_data:y0});function Te(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&A(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}const XH=Dp,JH=lw,ZH=hw,QH=uw,eq=Ap;class tq extends y{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new p(this,Ki())}write(e,t,n){this.firstUse&&(this.firstUse=!1,oe().get("IS_NODE")&&Za(`
============================
Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));const s={};return this.data.set(s,{values:e,dtype:n,refCount:1}),s}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&Yi(n[0])){const i=n.map(o=>Wd(o));s=this.write(i,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}incRef(e){const t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){const t=this.data.get(e);t.refCount--}}move(e,t,n,s){this.data.set(e,{values:t,dtype:s,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){const{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){const s=this.readSync(n.real.dataId),i=this.readSync(n.imag.dataId);return tr(s,i)}return this.data.get(e).values}bufferSync(e){const t=this.readSync(e.dataId);let n=t;if(e.dtype==="string")try{n=t.map(s=>Kl(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(e.shape,e.dtype,n)}makeOutput(e,t,n){const s=this.write(e,t,n);return Ki().makeTensorFromDataId(s,t,n,this)}disposeData(e){if(this.data.has(e)){const{complexTensorInfos:t}=this.data.get(e);t!=null&&(this.disposeData(t.real.dataId),this.disposeData(t.imag.dataId)),this.data.delete(e)}}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.data.has(t)){const n=this.data.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}async time(e){const t=jn();e();const n=jn()-t;return{kernelMs:n}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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_q={kernelName:xd,backendName:"cpu",kernelFunc:W0};const $0=xt(Fl,e=>Math.max(0,e)),Wq={kernelName:Fl,backendName:"cpu",kernelFunc:$0};const U0=xt(Wl,e=>Math.min(Math.max(0,e),6)),$q={kernelName:Wl,backendName:"cpu",kernelFunc:U0};function uS(e,t,n,s){if(n==="linear")return Go({inputs:{x:t},backend:e});if(n==="relu")return $0({inputs:{x:t},backend:e});if(n==="elu")return _0({inputs:{x:t},backend:e});if(n==="relu6")return U0({inputs:{x:t},backend:e});if(n==="prelu")return W0({inputs:{x:t,alpha:s},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function Di(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=P(i.shape),c=Vt(o,a),h=P(c);A(a===h,()=>`The new shape (${c}) has ${h} elements and the old shape (${i.shape}) has ${a} elements. 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Got strides ${a} and dilations '${d}'`);const m=Un(i.shape,o,a,d,c,h);let f;if(m.filterWidth===1&&m.filterHeight===1&&ae(m.inShape,m.outShape))f=Go({inputs:{x:i},backend:n});else{const b=n.data.get(i.dataId).values,w=je(i.shape),L=dS(b,i.shape,i.dtype,w,m,"avg");f=n.makeTensorInfo(m.outShape,i.dtype,L.values)}return f}const t4={kernelName:dl,backendName:"cpu",kernelFunc:e4};function n4(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;Te([i,o],"avgPoolBackprop");const{filterSize:c,strides:h,pad:d}=s,m=Un(a.shape,c,h,1,d),f=m.strideHeight,b=m.strideWidth,w=m.filterHeight,L=m.filterWidth,x=m.dilationHeight,v=m.dilationWidth,N=m.effectiveFilterHeight,O=m.effectiveFilterWidth,E=O-1-m.padInfo.left,k=N-1-m.padInfo.top,F=wt(a.shape,"float32"),U=1/(w*L),$=n.data.get(i.dataId).values,Y=wt(i.shape,"float32",$);for(let j=0;j<m.batchSize;++j)for(let Z=0;Z<m.inChannels;++Z)for(let ie=0;ie<m.inHeight;++ie)for(let de=0;de<m.inWidth;++de){const he=ie-k,ue=de-E;let me=0;for(let ce=0;ce<N;ce+=x){const ye=(he+ce)/f;if(ye<0||ye>=m.outHeight||Math.floor(ye)!==ye)continue;for(let pe=0;pe<O;pe+=v){const we=(ue+pe)/b;if(we<0||we>=m.outWidth||Math.floor(we)!==we)continue;const Se=Y.get(j,ye,we,Z);me+=Se}}F.set(me*U,j,ie,de,Z)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}const s4={kernelName:sd,backendName:"cpu",kernelFunc:n4};function i4(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,scale:o,offset:a,mean:c,variance:h}=t;A(c.shape.length===h.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),A(a==null||c.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),A(o==null||c.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Te([i,c,h,o,a],"batchNorm");let{varianceEpsilon:d}=s;d==null&&(d=.001);const m=n.data.get(i.dataId).values,f=n.data.get(c.dataId).values,b=n.data.get(h.dataId).values,w=o?n.data.get(o.dataId).values:new Float32Array([1]),L=a?n.data.get(a.dataId).values:new Float32Array([0]),x=new Float32Array(m.length),v=L.length,N=w.length,O=b.length,E=f.length;let k=0,F=0,U=0,$=0;for(let Y=0;Y<m.length;++Y)x[Y]=L[k++]+(m[Y]-f[F++])*w[U++]/Math.sqrt(b[$++]+d),k>=v&&(k=0),F>=E&&(F=0),U>=N&&(U=0),$>=O&&($=0);return n.makeTensorInfo(i.shape,i.dtype,x)}const r4={kernelName:Il,backendName:"cpu",kernelFunc:i4};const o4=xt(ml,(e,t)=>{const n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),a4={kernelName:ml,backendName:"cpu",kernelFunc:o4};function Am(e){const{inputs:t,backend:n}=e,{input:s}=t,i=n.data.get(s.dataId).complexTensorInfos.imag,o=n.data.get(i.dataId).values;return n.makeTensorInfo(i.shape,i.dtype,o)}const c4={kernelName:gd,backendName:"cpu",kernelFunc:Am};function cu(e){const{inputs:t,backend:n,attrs:s}=e,{axis:i}=s,o=qe(i,t[0].shape)[0];let a=Xi(t.map(w=>w.shape),o);if(P(a)===0)return n.makeTensorInfo(a,t[0].dtype,[]);const c=t.filter(w=>P(w.shape)>0);if(c.length===1)return c[0];const h=c.map(w=>w.shape);if(np(h,o),c[0].dtype==="complex64"){const w=c.map(O=>ru({inputs:{input:O},backend:n})),L=c.map(O=>Am({inputs:{input:O},backend:n})),x=cu({inputs:w,backend:n,attrs:{axis:o}}),v=cu({inputs:L,backend:n,attrs:{axis:o}}),N=ci({inputs:{real:x,imag:v},backend:n});return w.forEach(O=>n.disposeIntermediateTensorInfo(O)),L.forEach(O=>n.disposeIntermediateTensorInfo(O)),n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),N}const d=c.map(w=>{const L=P(w.shape.slice(o)),x=[-1,L];return Di({inputs:{x:w},backend:n,attrs:{shape:x}})});a=Xi(d.map(w=>w.shape),1);const m=bt(c[0].dtype,P(a));if(d[0].shape[0]===1){let w=0;d.forEach(L=>{const x=n.data.get(L.dataId).values,v=P(L.shape);m.set(x,w),w+=v})}else{let w=0;d.forEach(L=>{const x=n.data.get(L.dataId).values;let v=0;for(let N=0;N<L.shape[0];++N){const 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De=0;De<w;++De){const Ue=Ne+De*x;if(Ue<0||Ue>=b.inHeight)continue;const ze=De*U[0],ht=we+Ue*Y;for(let it=0;it<b.outWidth;++it){const rt=Oe+it*he,mt=it*b.strideWidth-N;for(let ut=0;ut<L;++ut){const Dt=mt+ut*v;if(Dt<0||Dt>=b.inWidth)continue;const rn=ze+ut*U[1],Ut=ht+Dt*j;let kt=rn;for(let Ft=0;Ft<b.inChannels;++Ft){const Xt=me[Ut+Ft*Z];for(let Rn=0;Rn<b.outChannels;++Rn)ye[rt+Rn*ue]+=Xt*ce[kt+Rn];kt+=b.outChannels}}}}}}return n.makeTensorInfo(k.shape,k.dtype,ye)}const h4={kernelName:od,backendName:"cpu",kernelFunc:P0};function u4(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,dy:o}=t,{strides:a,pad:c,dataFormat:h,dimRoundingMode:d,filterShape:m}=s;Te([i,o],"conv2dBackpropFilter");const f=Wr(h),b=kn(i.shape,m,a,1,c,d,!1,f),{strideHeight:w,strideWidth:L,filterHeight:x,filterWidth:v}=b,N=b.dataFormat==="channelsLast",O=new an(b.filterShape,"float32"),E=b.padInfo.left,k=b.padInfo.top,F=n.data.get(i.dataId).values,U=n.data.get(o.dataId).values,$=new an(i.shape,i.dtype,F),Y=new an(o.shape,o.dtype,U);for(let j=0;j<x;++j){const Z=Math.max(0,Math.ceil((k-j)/w)),ie=Math.min(b.outHeight,(b.inHeight+k-j)/w);for(let de=0;de<v;++de){const he=Math.max(0,Math.ceil((E-de)/L)),ue=Math.min(b.outWidth,(b.inWidth+E-de)/L);for(let me=0;me<b.inChannels;++me)for(let ce=0;ce<b.outChannels;++ce){let ye=0;for(let pe=0;pe<b.batchSize;++pe)for(let we=Z;we<ie;++we){const Se=j+we*w-k;for(let xe=he;xe<ue;++xe){const Oe=de+xe*L-E;N?ye+=$.get(pe,Se,Oe,me)*Y.get(pe,we,xe,ce):ye+=$.get(pe,me,Se,Oe)*Y.get(pe,ce,we,xe)}}O.set(ye,j,de,me,ce)}}}return n.makeTensorInfo(O.shape,O.dtype,O.values)}const d4={kernelName:Xg,backendName:"cpu",kernelFunc:u4};function p4(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,filter:o}=t,{inputShape:a,strides:c,pad:h,dataFormat:d,dimRoundingMode:m}=s;Te([i,o],"conv2dBackpropInput");const f=je(o.shape),b=je(i.shape);let w=Wr(d);const L=kn(a,o.shape,c,1,h,m,!1,w),x=new an(L.inShape,"float32"),v=x.values,N=n.data.get(i.dataId).values,O=n.data.get(o.dataId).values,[E,k,F]=f,{batchSize:U,filterHeight:$,filterWidth:Y,inChannels:j,inHeight:Z,inWidth:ie,outChannels:de,outHeight:he,outWidth:ue,strideHeight:me,strideWidth:ce}=L;w=L.dataFormat;const ye=$-1-L.padInfo.top,pe=Y-1-L.padInfo.left,we=w==="channelsLast",Se=x.strides[0],xe=we?x.strides[1]:x.strides[2],Oe=we?x.strides[2]:1,Ne=we?1:x.strides[1],De=b[0],Ue=we?b[1]:b[2],ze=we?b[2]:1,ht=we?1:b[1];for(let it=0;it<U;++it)for(let rt=0;rt<j;++rt)for(let mt=0;mt<Z;++mt){const ut=mt-ye,Dt=Math.max(0,Math.ceil(ut/me)),rn=Math.min(he,($+ut)/me);for(let Ut=0;Ut<ie;++Ut){const kt=Ut-pe,Ft=Math.max(0,Math.ceil(kt/ce)),Xt=Math.min(ue,(Y+kt)/ce);let Rn=0;for(let On=Dt;On<rn;++On){const li=On*me-ut;for(let Cs=Ft;Cs<Xt;++Cs){const qo=Cs*ce-kt,hi=De*it+Ue*On+ze*Cs,Fi=E*($-1-li)+k*(Y-1-qo)+F*rt;for(let eo=0;eo<de;++eo){const to=N[hi+ht*eo],no=O[Fi+eo];Rn+=to*no}}}const pr=Se*it+xe*mt+Oe*Ut+Ne*rt;v[pr]=Rn}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}const m4={kernelName:ad,backendName:"cpu",kernelFunc:p4};function f4(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,filter:o}=t,{strides:a,pad:c,dilations:h}=s;Te([i,o],"conv3d");const d=Fr(i.shape,o.shape,a,h,c),{filterDepth:m,filterHeight:f,filterWidth:b,dilationDepth:w,dilationHeight:L,dilationWidth:x,padInfo:v}=d,N=v.front,O=v.left,E=v.top,k=new an(d.outShape,i.dtype),F=n.data.get(i.dataId).values,U=n.data.get(o.dataId).values,$=k.values,Y=je(i.shape),j=je(o.shape);for(let Z=0;Z<d.batchSize;++Z){const ie=Z*Y[0],de=Z*k.strides[0];for(let he=0;he<d.outDepth;++he){const ue=de+he*k.strides[1],me=he*d.strideDepth-N;for(let ce=0;ce<m;++ce){const ye=me+ce*w;if(ye<0||ye>=d.inDepth)continue;const pe=ce*j[0],we=ie+ye*Y[1];for(let Se=0;Se<d.outHeight;++Se){const xe=ue+Se*k.strides[2],Oe=Se*d.strideHeight-E;for(let Ne=0;Ne<f;++Ne){const De=Oe+Ne*L;if(De<0||De>=d.inHeight)continue;const Ue=pe+Ne*j[1],ze=we+De*Y[2];for(let ht=0;ht<d.outWidth;++ht){const it=xe+ht*d.outChannels,rt=ht*d.strideWidth-O;for(let mt=0;mt<b;++mt){const ut=rt+mt*x;if(ut<0||ut>=d.inWidth)continue;const Dt=Ue+mt*j[2],rn=ze+ut*d.inChannels;let Ut=Dt;for(let kt=0;kt<d.inChannels;++kt){const Ft=F[rn+kt];for(let Xt=0;Xt<d.outChannels;++Xt)$[it+Xt]+=Ft*U[Ut+Xt];Ut+=d.outChannels}}}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}const g4={kernelName:cd,backendName:"cpu",kernelFunc:f4};function y4(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,dy:o}=t,{strides:a,pad:c,filterShape:h}=s;Te([i,o],"conv3dBackpropFilterV2");const d=je(i.shape),m=je(o.shape),f=Fr(i.shape,h,a,1,c),b=f.strideDepth,w=f.strideHeight,L=f.strideWidth,x=f.filterDepth,v=f.filterHeight,N=f.filterWidth,O=new an(f.filterShape,"float32"),E=O.values,[k,F,U,$]=O.strides,Y=n.data.get(o.dataId).values,[j,Z,ie,de]=m,he=n.data.get(i.dataId).values,[ue,me,ce,ye]=d,pe=f.padInfo.front,we=f.padInfo.left,Se=f.padInfo.top;for(let xe=0;xe<x;++xe){const Oe=Math.max(0,Math.ceil((pe-xe)/b)),Ne=Math.min(f.outDepth,(f.inDepth+pe-xe)/b),De=xe*k;for(let Ue=0;Ue<v;++Ue){const ze=Math.max(0,Math.ceil((Se-Ue)/w)),ht=Math.min(f.outHeight,(f.inHeight+Se-Ue)/w),it=Ue*F+De;for(let rt=0;rt<N;++rt){const mt=Math.max(0,Math.ceil((we-rt)/L)),ut=Math.min(f.outWidth,(f.inWidth+we-rt)/L),Dt=rt*U+it;for(let rn=0;rn<f.inChannels;++rn){const Ut=rn*$+Dt;for(let kt=0;kt<f.outChannels;++kt){let Ft=0;for(let Xt=0;Xt<f.batchSize;++Xt){const Rn=Xt*ue,pr=Xt*j;for(let On=Oe;On<Ne;++On){const li=xe+On*b-pe,Cs=li*me+Rn,qo=On*Z+pr;for(let hi=ze;hi<ht;++hi){const Fi=Ue+hi*w-Se,eo=Fi*ce+Cs,to=hi*ie+qo;for(let no=mt;no<ut;++no){const Tc=rt+no*L-we,RS=Tc*ye+eo,OS=no*de+to;Ft+=he[RS+rn]*Y[OS+kt]}}}}E[Ut+kt]=Ft}}}}}return n.makeTensorInfo(O.shape,O.dtype,O.values)}const b4={kernelName:Jg,backendName:"cpu",kernelFunc:y4};function w4(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,filter:o}=t,{pad:a,strides:c,inputShape:h}=s;Te([i],"conv3dBackpropInputV2");const d=je(i.shape),m=je(o.shape),f=Fr(h,o.shape,c,1,a),b=new an(f.inShape,"float32"),w=b.values,[L,x,v,N]=b.strides,O=n.data.get(i.dataId).values,[E,k,F,U]=d,$=n.data.get(o.dataId).values,[Y,j,Z,ie]=m,{batchSize:de,filterDepth:he,filterHeight:ue,filterWidth:me,inChannels:ce,inDepth:ye,inHeight:pe,inWidth:we,outChannels:Se,outDepth:xe,outHeight:Oe,outWidth:Ne,strideDepth:De,strideHeight:Ue,strideWidth:ze}=f,ht=he-1-f.padInfo.front,it=ue-1-f.padInfo.top,rt=me-1-f.padInfo.left;for(let mt=0;mt<de;++mt)for(let ut=0;ut<ce;++ut)for(let Dt=0;Dt<ye;++Dt){const rn=Dt-ht,Ut=Math.max(0,Math.ceil(rn/De)),kt=Math.min(xe,(he+rn)/De);for(let Ft=0;Ft<pe;++Ft){const Xt=Ft-it,Rn=Math.max(0,Math.ceil(Xt/Ue)),pr=Math.min(Oe,(ue+Xt)/Ue);for(let On=0;On<we;++On){const li=On-rt,Cs=Math.max(0,Math.ceil(li/ze)),qo=Math.min(Ne,(me+li)/ze);let hi=0;for(let Fi=Ut;Fi<kt;++Fi){const eo=Fi*De-rn;for(let to=Rn;to<pr;++to){const no=to*Ue-Xt;for(let Tc=Cs;Tc<qo;++Tc){const RS=Tc*ze-li,OS=E*mt+k*Fi+F*to+U*Tc,yJ=Y*(he-1-eo)+j*(ue-1-no)+Z*(me-1-RS)+ie*ut;for(let Bm=0;Bm<Se;++Bm){const bJ=O[OS+Bm],wJ=$[yJ+Bm];hi+=bJ*wJ}}}}w[L*mt+x*Dt+v*Ft+N*On+ut]=hi}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}const L4={kernelName:Zg,backendName:"cpu",kernelFunc:w4};const S4=xt(Ia,e=>Math.cos(e)),I4={kernelName:Ia,backendName:"cpu",kernelFunc:S4};const x4=xt(gl,e=>Math.cosh(e)),T4={kernelName:gl,backendName:"cpu",kernelFunc:x4};function z0(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,filter:o}=t,{strides:a,pad:c,dilations:h,dimRoundingMode:d}=s;Te([i,o],"depthwiseConv2DNative");const m=je(i.shape),f=je(o.shape);let b=h;b==null&&(b=[1,1]),A(cn(a,b),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${b}'`);const w=kn(i.shape,o.shape,a,b,c,d,!0),{filterHeight:L,filterWidth:x,dilationHeight:v,dilationWidth:N,padInfo:O}=w,E=O.left,k=O.top,F=w.outChannels/w.inChannels,U=new an(w.outShape,i.dtype),$=n.data.get(i.dataId).values,Y=n.data.get(o.dataId).values,j=U.values;for(let Z=0;Z<w.batchSize;++Z){const ie=Z*m[0],de=Z*U.strides[0];for(let he=0;he<w.outHeight;++he){const ue=de+he*U.strides[1],me=he*w.strideHeight-E;for(let ce=0;ce<L;++ce){const ye=me+ce*v;if(ye<0||ye>=w.inHeight)continue;const pe=ce*f[0],we=ie+ye*m[1];for(let Se=0;Se<w.outWidth;++Se){const xe=ue+Se*U.strides[2],Oe=Se*w.strideWidth-k;for(let Ne=0;Ne<x;++Ne){const De=Oe+Ne*N;if(De<0||De>=w.inWidth)continue;const Ue=pe+Ne*f[1],ze=we+De*w.inChannels;let ht=xe,it=Ue;for(let rt=0;rt<w.inChannels;++rt){const mt=$[ze+rt];for(let ut=0;ut<F;++ut)j[ht+ut]+=mt*Y[it+ut];ht+=F,it+=F}}}}}}return n.makeTensorInfo(U.shape,U.dtype,U.values)}const A4={kernelName:ld,backendName:"cpu",kernelFunc:z0};function v4(e){const{inputs:t,backend:n,attrs:s}=e,{x:i,dy:o}=t,{strides:a,dilations:c,pad:h,dimRoundingMode:d,filterShape:m}=s;Te([i,o],"depthwiseConv2dNativeBackpropFilter");const f=kn(i.shape,m,a,c,h,d,!0),{strideHeight:b,strideWidth:w,filterHeight:L,filterWidth:x}=f,v=new an(f.filterShape,"float32"),N=f.padInfo.left,O=f.padInfo.top,E=f.outChannels/f.inChannels,k=n.data.get(i.dataId).values,F=new an(i.shape,i.dtype,k),U=n.data.get(o.dataId).values,$=new an(o.shape,o.dtype,U);for(let Y=0;Y<L;++Y){const j=Math.max(0,Math.ceil((O-Y)/b)),Z=Math.min(f.outHeight,(f.inHeight+O-Y)/b);for(let ie=0;ie<x;++ie){const de=Math.max(0,Math.ceil((N-ie)/w)),he=Math.min(f.outWidth,(f.inWidth+N-ie)/w);for(let ue=0;ue<f.outChannels;++ue){const me=Math.trunc(ue/E),ce=ue%E;let ye=0;for(let pe=0;pe<f.batchSize;++pe)for(let we=j;we<Z;++we){const Se=Y+we*b-O;for(let xe=de;xe<he;++xe){const Oe=ie+xe*w-N;ye+=F.get(pe,Se,Oe,me)*$.get(pe,we,xe,ue)}}v.set(ye,Y,ie,me,ce)}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}const N4={kernelName:ey,backendName:"cpu",kernelFunc:v4};function C4(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,filter:o}=t,{strides:a,dilations:c,pad:h,dimRoundingMode:d,inputShape:m}=s;Te([i,o],"depthwiseConv2DNativeBackpropInput");const f=je(i.shape),b=je(o.shape),w=kn(m,o.shape,a,c,h,d,!0),L=new an(w.inShape,"float32"),x=L.values,[v,N,O]=L.strides,E=n.data.get(i.dataId).values,[k,F,U]=f,$=n.data.get(o.dataId).values,[Y,j,Z]=b,{batchSize:ie,filterHeight:de,filterWidth:he,inChannels:ue,inHeight:me,inWidth:ce,outChannels:ye,outHeight:pe,outWidth:we,strideHeight:Se,strideWidth:xe}=w,Oe=de-1-w.padInfo.top,Ne=he-1-w.padInfo.left,De=ye/ue;for(let Ue=0;Ue<ie;++Ue)for(let ze=0;ze<ue;++ze)for(let ht=0;ht<me;++ht){const it=ht-Oe,rt=Math.max(0,Math.ceil(it/Se)),mt=Math.min(pe,(de+it)/Se);for(let ut=0;ut<ce;++ut){const Dt=ut-Ne,rn=Math.max(0,Math.ceil(Dt/xe)),Ut=Math.min(we,(he+Dt)/xe);let kt=0;for(let Ft=rt;Ft<mt;++Ft){const Xt=Ft*Se-it;for(let Rn=rn;Rn<Ut;++Rn){const pr=Rn*xe-Dt,On=k*Ue+F*Ft+U*Rn,li=Y*(de-1-Xt)+j*(he-1-pr)+Z*ze;for(let Cs=0;Cs<De;++Cs){const qo=ze*De+Cs,hi=E[On+qo],Fi=$[li+Cs];kt+=hi*Fi}}}x[v*Ue+N*ht+O*ut+ze]=kt}}return n.makeTensorInfo(L.shape,L.dtype,L.values)}const R4={kernelName:ty,backendName:"cpu",kernelFunc:C4};const O4={kernelName:hd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i}=e,{strides:o,pad:a,dilations:c}=n,h=t,d=h.data.get(s.dataId).values,m=s.shape.length,f=h.data.get(i.dataId).values,b=i.shape.length,{batchSize:w,inHeight:L,inWidth:x,inChannels:v,outHeight:N,outWidth:O,padInfo:E,strideHeight:k,strideWidth:F,filterHeight:U,filterWidth:$,dilationHeight:Y,dilationWidth:j,outShape:Z}=ep(s.shape,i.shape,o,a,"NHWC",c),ie=P(Z),de=Z.length,he=ws(s.dtype,ie);for(let me=0;me<w;++me)for(let ce=0;ce<N;++ce){const ye=ce*k-E.top;for(let pe=0;pe<O;++pe){const we=pe*F-E.left;for(let Se=0;Se<v;++Se){let xe=Number.MIN_SAFE_INTEGER;for(let Ne=0;Ne<U;++Ne){const De=ye+Ne*Y;if(De>=0&&De<L)for(let Ue=0;Ue<$;++Ue){const ze=we+Ue*j;if(ze>=0&&ze<x){const ht=_s([me,De,ze,Se],m,je(s.shape)),it=_s([Ne,Ue,Se],b,je(i.shape)),rt=d[ht]+f[it];rt>xe&&(xe=rt)}}}const Oe=_s([me,ce,pe,Se],de,je(Z));he[Oe]=xe}}}const ue=h.write(Or(he,s.dtype),Z,s.dtype);return{dataId:ue,shape:Z,dtype:s.dtype}}};const E4={kernelName:dd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i,dy:o}=e,{strides:a,pad:c,dilations:h}=n,d=t,m=Ls(s.shape,d.data.get(s.dataId).values),f=Ls(i.shape,d.data.get(i.dataId).values),{batchSize:b,inHeight:w,inWidth:L,inChannels:x,outHeight:v,outWidth:N,padInfo:O,strideHeight:E,strideWidth:k,filterHeight:F,filterWidth:U,dilationHeight:$,dilationWidth:Y,outShape:j}=ep(s.shape,i.shape,a,c,"NHWC",h);A(o.rank===j.length,()=>`Error in ${dd}, dy must have the same rank as output ${j.length}, but got ${o.rank}`);const Z=Ls(j,d.data.get(o.dataId).values),ie=Pg(i.shape,i.dtype);for(let he=0;he<b;++he)for(let ue=0;ue<v;++ue){const me=ue*E-O.top;for(let ce=0;ce<N;++ce){const ye=ce*k-O.left;for(let pe=0;pe<x;++pe){let we=Number.MIN_SAFE_INTEGER,Se=0,xe=0;for(let Oe=0;Oe<F;++Oe){const Ne=me+Oe*$;if(Ne>=0&&Ne<w)for(let De=0;De<U;++De){const Ue=ye+De*Y;if(Ue>=0&&Ue<L){const ze=m[he][Ne][Ue][pe]+f[Oe][De][pe];ze>we&&(we=ze,Se=Oe,xe=De)}}}ie[Se][xe][pe]+=Z[he][ue][ce][pe]}}}const de=d.write(Or(ie,s.dtype),i.shape,i.dtype);return{dataId:de,shape:i.shape,dtype:i.dtype}}};const D4={kernelName:ud,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i,dy:o}=e,{strides:a,pad:c,dilations:h}=n,d=t,m=Ls(s.shape,d.data.get(s.dataId).values),f=Ls(i.shape,d.data.get(i.dataId).values),{batchSize:b,inHeight:w,inWidth:L,inChannels:x,outHeight:v,outWidth:N,padInfo:O,strideHeight:E,strideWidth:k,filterHeight:F,filterWidth:U,dilationHeight:$,dilationWidth:Y,outShape:j}=ep(s.shape,i.shape,a,c,"NHWC",h);A(o.rank===j.length,()=>`Error in ${ud}, dy must have the same rank as output ${j.length}, but got ${o.rank}`);const Z=Ls(j,d.data.get(o.dataId).values),ie=Pg(s.shape,s.dtype);for(let he=0;he<b;++he)for(let ue=0;ue<v;++ue){const me=ue*E-O.top;for(let ce=0;ce<N;++ce){const ye=ce*k-O.left;for(let pe=0;pe<x;++pe){let we=Number.MIN_SAFE_INTEGER,Se=me<0?0:me,xe=ye<0?0:ye;for(let Oe=0;Oe<F;++Oe){const Ne=me+Oe*$;if(Ne>=0&&Ne<w)for(let De=0;De<U;++De){const Ue=ye+De*Y;if(Ue>=0&&Ue<L){const ze=m[he][Ne][Ue][pe]+f[Oe][De][pe];ze>we&&(we=ze,Se=Ne,xe=Ue)}}}ie[he][Se][xe][pe]+=Z[he][ue][ce][pe]}}}const de=d.write(Or(ie,s.dtype),s.shape,s.dtype);return{dataId:de,shape:s.shape,dtype:s.dtype}}};const k4=Zr((e,t)=>e/t),F4=hc(xa,k4),pS={kernelName:xa,backendName:"cpu",kernelFunc:F4};const _4=ew,W4=tw,$4=nw,U4=sw,B4=iw,M4=rw,P4=xt(bl,e=>{const t=Math.sign(e),n=Math.abs(e),s=1/(1+_4*n);return t*(1-((((M4*s+B4)*s+U4)*s+$4)*s+W4)*s*Math.exp(-n*n))}),z4={kernelName:bl,backendName:"cpu",kernelFunc:P4};function V0(e,t,n){const s=e.shape,i=s[0],o=s[1],a=n.data.get(e.dataId),c=a.complexTensorInfos.real,h=a.complexTensorInfos.imag,d=[i,o],m=P(d),f=bt("float32",m),b=bt("float32",m);for(let v=0;v<i;v++){const N=lS({inputs:{x:c},backend:n,attrs:{begin:[v,0],size:[1,o]}}),O=lS({inputs:{x:h},backend:n,attrs:{begin:[v,0],size:[1,o]}}),E=ci({inputs:{real:N,imag:O},backend:n}),{real:k,imag:F}=V4(E,t,n),U=tr(k,F);for(let $=0;$<o;$++){const Y=ow(U,$);f[v*o+$]=Y.real,b[v*o+$]=Y.imag}n.disposeIntermediateTensorInfo(N),n.disposeIntermediateTensorInfo(O),n.disposeIntermediateTensorInfo(E)}const w=n.makeTensorInfo(d,"float32",f),L=n.makeTensorInfo(d,"float32",b),x=ci({inputs:{real:w,imag:L},backend:n});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(L),x}function V4(e,t,n){const s=P(e.shape),i=n.data.get(e.dataId),o=n.data.get(i.complexTensorInfos.real.dataId).values,a=n.data.get(i.complexTensorInfos.imag.dataId).values;if(G4(s)){const c=mS(o,a,s,t,n),h=[e.shape[0],e.shape[1]];if(t){const 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}
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}
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}
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int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:i,output:o,defineOutput:a,defineSpecialNaN:c,defineSpecialInf:h,defineRound:d}}function Yo(e,t,n="index"){const s=je(t);return s.map((i,o)=>{const a=`int ${e[o]} = ${n} / ${i}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${i}`:`index -= ${e[o]} * ${i}`;return`${a}; ${c};`}).join("")}function Dm(e){return e.length===1?`${e[0]}`:`vec${e.length}(${e.join(",")})`}function sne(e,t){if(e.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${e.length} and ${t.length}`);const n=[],s=Math.floor(e.length/4),i=e.length%4;for(let o=0;o<s;o++){const a=e.slice(o*4,o*4+4),c=t.slice(o*4,o*4+4);n.push(`${Dm(a)}, ${Dm(c)}`)}if(i!==0){let o=e.slice(s*4),a=t.slice(s*4);o.length===1&&(o=o.map(c=>`float(${c})`),a=a.map(c=>`float(${c})`)),n.push(`${Dm(o)}, ${Dm(a)}`)}return n.map((o,a)=>`dot(${o})`).join("+")}function IS(e){const t=je(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}const Z0=`
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:Q0}=cw;function o5(e,t,n,s){const i=[];e.forEach(L=>{const x=P(L.shapeInfo.logicalShape);L.shapeInfo.isUniform?i.push(`uniform float ${L.name}${x>1?`[${x}]`:""};`):(i.push(`uniform sampler2D ${L.name};`),i.push(`uniform int offset${L.name};`))});const o=i.join(`
`),a=e.map(L=>a5(L,t,s)).join(`
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`);return w}function gc(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return R5(e);case 1:return E5(e);case 2:return k5(e);case 3:return _5(e);case 4:return $5(e);case 5:return U5(e);case 6:return B5(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function eC(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return C5(e);case 1:return O5(e);case 2:return D5(e);case 3:return F5(e);default:return W5(e)}}function a5(e,t,n=!1){let s="";n?s+=eC(e):s+=gc(e);const i=e.shapeInfo.logicalShape,o=t.logicalShape;return i.length<=o.length&&(n?s+=M5(e,t):s+=P5(e,t)),s}function c5(e,t){switch(e.length){case 0:return tC();case 1:return b5(e,t);case 2:return v5(e,t);case 3:return L5(e,t);default:return I5(e,t)}}function l5(e,t){switch(e.length){case 0:return tC();case 1:return w5(e,t);case 2:return N5(e,t);case 3:return S5(e,t);case 4:return x5(e,t);case 5:return T5(e,t);case 6:return A5(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function h5(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function u5(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function d5(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function p5(e){const t=`${e.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${e.varyingFs} vec2 resultUV;
${e.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;
${e.defineSpecialNaN}
${e.defineSpecialInf}
${e.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);
}
${m5}
${f5}
${g5}
`;return t}const m5=`
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);
}
`,f5=`
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);
}
`,g5=`
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);
}
`,y5=`
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 tC(){return`
int getOutputCoords() {
return 0;
}
`}function b5(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function w5(e,t){return t[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function L5(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),i=s*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${i};
index -= b * ${i};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec3(b, r, c);
}
`}function S5(e,t){const n=Yo(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function I5(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[e.length-1]/2),i=s*Math.ceil(e[e.length-2]/2);let o=i,a="",c="b, r, c";for(let h=2;h<e.length-1;h++)o*=e[e.length-h-1],a=`
int b${h} = index / ${o};
index -= b${h} * ${o};
`+a,c=`b${h}, `+c;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${a}
int b = index / ${i};
index -= b * ${i};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec${e.length}(${c});
}
`}function x5(e,t){const n=Yo(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function T5(e,t){const n=Yo(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function A5(e,t){const n=Yo(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function v5(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(ae(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;const s=Math.ceil(e[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec2(r, c);
}
`}function N5(e,t){return ae(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function Ho(e){return`offset${e}`}function C5(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Pn();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function R5(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;const[s,i]=e.shapeInfo.texShape;if(s===1&&i===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;const[o,a]=e.shapeInfo.texShape,c=Ho(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${o}, ${a}, ${c});
return sampleTexture(${t}, uv);
}
`}function O5(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=Pn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${o.texture2D}(${t}, uv);
}
`}function E5(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${yc(e)}
}
`;const s=e.shapeInfo.texShape,i=s[0],o=s[1];if(o===1&&i===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;const a=Ho(t);return o===1?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${i}.0);
return sampleTexture(${t}, uv);
}
`:i===1?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${o}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${i}, ${o}, index + ${a});
return sampleTexture(${t}, uv);
}
`}function D5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape,o=i[0],a=i[1],c=Pn();if(i!=null&&ae(t,i))return`
vec4 ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${o}.0);
return ${c.texture2D}(${n}, uv);
}
`;const h=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],d=Math.ceil(t[1]/2);return`
vec4 ${s}(int row, int col) {
vec2 uv = packedUVfrom2D(${d}, ${h[0]}, ${h[1]}, row, col);
return ${c.texture2D}(${n}, uv);
}
`}function k5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape;if(i!=null&&ae(t,i)){const f=i[0],b=i[1];return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${f}.0);
return sampleTexture(${n}, uv);
}
`}const{newShape:o,keptDims:a}=ln(t),c=o;if(c.length<t.length){const f=bc(e,c),b=["row","col"];return`
${gc(f)}
float ${s}(int row, int col) {
return ${s}(${wc(b,a)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${yc(e)}
}
`;const h=i[0],d=i[1],m=Ho(n);return d===1?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${h}.0);
return sampleTexture(${n}, uv);
}
`:h===1?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${d}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${m};
vec2 uv = uvFromFlat(${h}, ${d}, index);
return sampleTexture(${n}, uv);
}
`}function F5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape,o=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];if(t[0]===1){const f=t.slice(1),b=[1,2],w=bc(e,f),L=["b","row","col"];return`
${eC(w)}
vec4 ${s}(int b, int row, int col) {
return ${s}(${wc(L,b)});
}
`}const a=o[0],c=o[1],h=Math.ceil(t[2]/2),d=h*Math.ceil(t[1]/2),m=Pn();return`
vec4 ${s}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${a}, ${c}, ${d}, ${h}, b, row, col);
return ${m.texture2D}(${n}, uv);
}
`}function _5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[1]*t[2],o=t[2],{newShape:a,keptDims:c}=ln(t),h=a;if(h.length<t.length){const L=bc(e,h),x=["row","col","depth"];return`
${gc(L)}
float ${s}(int row, int col, int depth) {
return ${s}(${wc(x,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${i}, ${o}, 1)));
${yc(e)}
}
`;const d=e.shapeInfo.texShape,m=d[0],f=d[1],b=e.shapeInfo.flatOffset;if(f===i&&b==null)return`
float ${s}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;if(f===o&&b==null)return`
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(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;const w=Ho(n);return`
float ${s}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} + depth + ${w};
vec2 uv = uvFromFlat(${m}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function W5(e){const t=e.shapeInfo.logicalShape,n=t.length,s=e.name,i="get"+s.charAt(0).toUpperCase()+s.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],c=a[0],h=a[1],d=Math.ceil(t[n-1]/2);let m=d*Math.ceil(t[n-2]/2),f="int b, int row, int col",b=`b * ${m} + (row / 2) * ${d} + (col / 2)`;for(let L=2;L<n-1;L++)f=`int b${L}, `+f,m*=t[n-L-1],b=`b${L} * ${m} + `+b;const w=Pn();return`
vec4 ${i}(${f}) {
int index = ${b};
int texR = index / ${h};
int texC = index - texR * ${h};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}, ${c});
return ${w.texture2D}(${s}, uv);
}
`}function $5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[3],o=t[2]*i,a=t[1]*o,{newShape:c,keptDims:h}=ln(t);if(c.length<t.length){const L=bc(e,c),x=["row","col","depth","depth2"];return`
${gc(L)}
float ${s}(int row, int col, int depth, int depth2) {
return ${s}(${wc(x,h)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${a}, ${o}, ${i}, 1)));
${yc(e)}
}
`;const d=e.shapeInfo.flatOffset,m=e.shapeInfo.texShape,f=m[0],b=m[1];if(b===a&&d==null)return`
float ${s}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${o}, ${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${b}.0, ${f}.0);
return sampleTexture(${n}, uv);
}
`;if(b===i&&d==null)return`
float ${s}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${b}.0, ${f}.0);
return sampleTexture(${n}, uv);
}
`;const w=Ho(n);return`
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 * ${o} +
depth * ${i} + depth2;
vec2 uv = uvFromFlat(${f}, ${b}, index + ${w});
return sampleTexture(${n}, uv);
}
`}function U5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[4],o=t[3]*i,a=t[2]*o,c=t[1]*a,{newShape:h,keptDims:d}=ln(t);if(h.length<t.length){const x=bc(e,h),v=["row","col","depth","depth2","depth3"];return`
${gc(x)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${wc(v,d)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${a}, ${o}, ${i})) +
depth3;
${yc(e)}
}
`;const m=e.shapeInfo.flatOffset,f=e.shapeInfo.texShape,b=f[0],w=f[1];if(w===c&&m==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${a}, ${o}, ${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${w}.0, ${b}.0);
return sampleTexture(${n}, uv);
}
`;if(w===i&&m==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${w}.0, ${b}.0);
return sampleTexture(${n}, uv);
}
`;const L=Ho(n);return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${a} + depth * ${o} +
depth2 * ${i} + depth3 + ${L};
vec2 uv = uvFromFlat(${b}, ${w}, index);
return sampleTexture(${n}, uv);
}
`}function B5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=ln(t);if(i.length<t.length){const v=bc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
${gc(v)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${wc(N,o)});
}
`}const a=t[5],c=t[4]*a,h=t[3]*c,d=t[2]*h,m=t[1]*d;if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${m}, ${d}, ${h}, ${c})) +
dot(
vec2(depth3, depth4),
vec2(${a}, 1)));
${yc(e)}
}
`;const f=e.shapeInfo.flatOffset,b=e.shapeInfo.texShape,w=b[0],L=b[1];if(L===m&&f==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${d}, ${h}, ${c}, ${a})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${L}.0, ${w}.0);
return sampleTexture(${n}, uv);
}
`;if(L===a&&f==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${L}.0, ${w}.0);
return sampleTexture(${n}, uv);
}
`;const x=Ho(n);return`
float ${s}(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 * ${m} + col * ${d} + depth * ${h} +
depth2 * ${c} + depth3 * ${a} + depth4 + ${x};
vec2 uv = uvFromFlat(${w}, ${L}, index);
return sampleTexture(${n}, uv);
}
`}function yc(e){const t=e.name,n=P(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function M5(e,t){const n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),i="get"+s+"AtOutCoords",o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,c=Q0(e.shapeInfo.logicalShape,t.logicalShape),h=Rt(a),d=a-o;let m;const f=["x","y","z","w","u","v"];o===0?m="":a<2&&c.length>=1?m="coords = 0;":m=c.map(O=>`coords.${f[O+d]} = 0;`).join(`
`);let b="";a<2&&o>0?b="coords":b=e.shapeInfo.logicalShape.map((O,E)=>`coords.${f[E+d]}`).join(", ");let w="return outputValue;";const L=P(e.shapeInfo.logicalShape),x=L===1,v=P(t.logicalShape),N=v===1;if(o===1&&!x&&!N)w=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(x&&!N)a===1?w=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:w=`
return vec4(outputValue.x);
`;else if(c.length){const O=o-2,E=o-1;c.indexOf(O)>-1&&c.indexOf(E)>-1?w="return vec4(outputValue.x);":c.indexOf(O)>-1?w="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":c.indexOf(E)>-1&&(w="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${i}() {
${h} coords = getOutputCoords();
${m}
vec4 outputValue = get${s}(${b});
${w}
}
`}function P5(e,t){const n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),i="get"+s+"AtOutCoords",o=t.texShape,a=e.shapeInfo.texShape,c=e.shapeInfo.logicalShape.length,h=t.logicalShape.length;if(!e.shapeInfo.isUniform&&c===h&&e.shapeInfo.flatOffset==null&&ae(a,o))return`
float ${i}() {
return sampleTexture(${n}, resultUV);
}
`;const d=Rt(h),m=Q0(e.shapeInfo.logicalShape,t.logicalShape),f=h-c;let b;const w=["x","y","z","w","u","v"];c===0?b="":h<2&&m.length>=1?b="coords = 0;":b=m.map(x=>`coords.${w[x+f]} = 0;`).join(`
`);let L="";return h<2&&c>0?L="coords":L=e.shapeInfo.logicalShape.map((x,v)=>`coords.${w[v+f]}`).join(", "),`
float ${i}() {
${d} coords = getOutputCoords();
${b}
return get${s}(${L});
}
`}function Rt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function bc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function wc(e,t){return t.map(n=>e[n]).join(", ")}class z5{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,A(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);const i=e[e.length-1],o=Math.ceil(i/t);this.outputShape=e.slice(0,-1),o>1&&this.outputShape.push(o),s||this.variableNames.push("bestIndicesA");const a=this.outputShape,c=a.length,h=Rt(c),d=Mn("coords",c);let m,f;if(o===1){f=c+1;const $=Rt(f);m=`
${$} sourceLocR = ${$}(${d.join()}, 0);
++${d[c-1]};
${$} sourceLocG = ${$}(${d.join()}, 0);
++${d[c-2]};
${$} sourceLocA = ${$}(${d.join()}, 0);
--${d[c-1]};
${$} sourceLocB = ${$}(${d.join()}, 0);
--${d[c-2]};`}else f=c,m=`
${h} sourceLocR = coords;
++${d[c-1]};
${h} sourceLocG = coords;
++${d[c-2]};
${h} sourceLocA = coords;
--${d[c-1]};
${h} sourceLocB = coords;
--${d[c-2]};`;const b=["x","y","z","w","u","v"].slice(0,f),w="."+b[f-1],L=b.map($=>"int "+$),x=Mn("sourceLocR",f-1).concat("inIdx.r"),v=Mn("sourceLocG",f-1).concat("inIdx.g"),N=Mn("sourceLocB",f-1).concat("inIdx.b"),O=Mn("sourceLocA",f-1).concat("inIdx.a"),E=n==="max"?"greaterThan":"lessThan",k=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${x.join()}),
getBestIndicesAChannel(${v.join()}),
getBestIndicesAChannel(${N.join()}),
getBestIndicesAChannel(${O.join()})));`,F=`vec4(
getAChannel(${x.join()}),
hasNextCol ? getAChannel(${v.join()}) : 0.,
hasNextRow ? getAChannel(${N.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${O.join()}) : 0.)`,U=s?"":`
float getBestIndicesAChannel(${L.join()}) {
return getChannel(getBestIndicesA(${b.join()}),
vec2(${b.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${L.join()}) {
return getChannel(getA(${b.join()}),
vec2(${b.slice(-2).join()}));
}
${U}
void main() {
${h} coords = getOutputCoords();
bool hasNextCol = ${d[c-1]} < ${a[c-1]-1};
bool hasNextRow = ${d[c-2]} < ${a[c-2]-1};
${m}
ivec4 srcIdx = ivec4(sourceLocR${w}, sourceLocG${w},
sourceLocB${w}, sourceLocA${w}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${F};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${k}
vec4 candidate = ${F};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${E}(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);
}
`}}class V5{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,i=e.strideWidth,o=e.dilationHeight,a=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=c-1-e.padInfo.top,m=h-1-e.padInfo.left,f=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${d}, ${m});
const float avgMultiplier = float(${f});
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 < ${c};
wR += ${o}) {
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 < ${h};
wC+= ${a}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}}class G5{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=e.dilationDepth,h=e.dilationHeight,d=e.dilationWidth,m=e.effectiveFilterDepth,f=e.effectiveFilterHeight,b=e.effectiveFilterWidth,w=m-1-e.padInfo.front,L=f-1-e.padInfo.top,x=b-1-e.padInfo.left,v=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${w}, ${L}, ${x});
const float avgMultiplier = float(${v});
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 < ${m};
wD += ${c}) {
float dyD = float(dyDCorner + wD) / ${i}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${f};
wR += ${h}) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${b};
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);
}
`}}const nC=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Y5=`
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;
}
`,H5=`
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);
`,ine="return (a - b) * (a - b);",q5="return float(a == b);",j5="return float(a < b);",K5="return float(a <= b);",X5="return float(a > b);",J5="return float(a >= b);",Z5="return float(a >= 1.0 && b >= 1.0);",Q5="return float(a >= 1.0 || b >= 1.0);",e8=nC+`
return max(a, b);
`,t8=nC+`
return min(a, b);
`,n8=`if (b == 0.0) return NAN;
return mod(a, b);`,s8="return (b >= 1.0) ? a : a * (b + 1.0);",sC="return (a < 0.) ? b * a : a;";class _n{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=nt(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}}const km=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`,i8=`
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);
`,r8=`
// 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;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+km+`
return result;
`,iC=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`,o8=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,a8=`
return vec4(equal(a, b));
`,rne=`
return vec4(notEqual(a, b));
`,c8=`
return vec4(lessThan(a, b));
`,l8=`
return vec4(lessThanEqual(a, b));
`,h8=`
return vec4(greaterThan(a, b));
`,u8=`
return vec4(greaterThanEqual(a, b));
`,d8=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,p8=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,m8=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+km+`
return result;
`,f8=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+km+`
return result;
`,g8=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+km+`
return result;
`;class lr{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=nt(t,n);const i=this.outputShape.length;let o="";if(s)if(i===0||P(this.outputShape)===1)o=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else{const a=Rt(i);if(o=`
${a} coords = getOutputCoords();
`,i===1)o+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{const c=Mn("coords",i);o+=`
bool nextRowOutOfBounds =
(${c[i-2]} + 1) >= ${this.outputShape[i-2]};
bool nextColOutOfBounds =
(${c[i-1]} + 1) >= ${this.outputShape[i-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);
${o}
setOutput(result);
}
`}}class y8{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(n,s)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(s,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(s,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}}class b8{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(n,s)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(s,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(s,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}}class w8{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))
);
}
`}}class L8{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,i=e.padInfo.left,o=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 * ${n} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${o}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}}class S8{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,i=e.strideWidth,o=e.dataFormat==="channelsLast",a=t-1-e.padInfo.top,c=n-1-e.padInfo.left,h=o?1:2,d=o?2:3,m=o?3:1;this.userCode=`
const ivec2 pads = ivec2(${a}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${m}];
ivec2 dyCorner = ivec2(coords[${h}], coords[${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 < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${o}) {
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 I8{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,i=e.padInfo.front,o=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} - ${i};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${o};
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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}}class x8{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=t-1-e.padInfo.front,h=n-1-e.padInfo.top,d=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${c}, ${h}, ${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) / ${i}.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 < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 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);
}
`}}class T8{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,i=e.padInfo.left,o=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 * ${o} + 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 * ${n} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}}class A8{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.top,a=n-1-e.padInfo.left,c=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${o}, ${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 < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${c}; dm++) {
int d2 = d1 * ${c} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}}class rC{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,b=Math.floor(e.inChannels/4)*4,w=e.inChannels%4,L=e.dataFormat==="channelsLast",x=L?1:2,v=L?2:3,N=L?3:1;let O="",E="";n&&(s?O=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:O=`
float activation(float x) {
${n}
}
`,E="result = activation(result);");const k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${O}
const ivec2 strides = ivec2(${a}, ${c});
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${N}];
ivec2 xRCCorner =
ivec2(coords[${x}], coords[${v}]) * 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 * ${h};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${b}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${L}) {
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 (${w===1}) {
if (${L}) {
dotProd +=
getX(batch, xR, xC, ${b}) *
getW(wR, wC, ${b}, d2);
} else {
dotProd +=
getX(batch, ${b}, xR, xC) *
getW(wR, wC, ${b}, d2);
}
} else if (${w===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${b}, d2),
getW(wR, wC, ${b} + 1, d2)
);
if (${L}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${b}),
getX(batch, xR, xC, ${b} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${b}, xR, xC),
getX(batch, ${b} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${w===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${b}, d2),
getW(wR, wC, ${b} + 1, d2),
getW(wR, wC, ${b} + 2, d2)
);
if (${L}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${b}),
getX(batch, xR, xC, ${b} + 1),
getX(batch, xR, xC, ${b} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${b}, xR, xC),
getX(batch, ${b} + 1, xR, xC),
getX(batch, ${b} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${k}
${E}
setOutput(result);
}
`}}class v8{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=e.dilationDepth,h=e.dilationHeight,d=e.dilationWidth,m=e.filterDepth,f=e.filterHeight,b=e.filterWidth,w=Math.floor(e.inChannels/4)*4,L=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${i}, ${o}, ${a});
const ivec3 pads = ivec3(${t}, ${n}, ${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 < ${m}; wF++) {
int xF = xFCorner + wF * ${c};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${f}; wR++) {
int xR = xRCorner + wR * ${h};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${w}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${L===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${w}) *
getW(wF, wR, wC, ${w}, d2);
} else if (${L===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${w}),
getX(batch, xF, xR, xC, ${w} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${w}, d2),
getW(wF, wR, wC, ${w} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${L===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${w}),
getX(batch, xF, xR, xC, ${w} + 1),
getX(batch, xF, xR, xC, ${w} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${w}, d2),
getW(wF, wR, wC, ${w} + 1, d2),
getW(wF, wR, wC, ${w} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}}class oC{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.inHeight,o=e.inWidth,a=e.padInfo.top,c=e.padInfo.left,h=e.strideHeight,d=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,b=e.filterHeight,w=e.filterWidth,L=e.outChannels/e.inChannels;let x="",v="";n&&(s?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:x=`
float activation(float x) {
${n}
}
`,v="result = activation(result);");const N=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${x}
const ivec2 strides = ivec2(${h}, ${d});
const ivec2 pads = ivec2(${a}, ${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 < ${b}; wR++) {
int xR = xRCorner + wR * ${m};
if (xR < 0 || xR >= ${i}) {
continue;
}
for (int wC = 0; wC < ${w}; wC++) {
int xC = xCCorner + wC * ${f};
if (xC < 0 || xC >= ${o}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${N}
${v}
setOutput(result);
}
`}}class aC{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;const i=e.inHeight,o=e.inWidth,a=e.padInfo.top,c=e.padInfo.left,h=e.strideHeight,d=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,b=e.filterHeight,w=e.filterWidth,L=w;let x="int xR; int xC; int xCOffset;";for(let E=0;E<b;E++)for(let k=0;k<w;k++)x+=`
vec4 xTexelR${E}C${k*2} = vec4(0.);
vec4 wR${E}C${k} = vec4(0.);
vec4 xR${E}C${k} = vec4(0.);`;for(let E=0;E<b;E++)for(let k=0;k<L;k++){const F=k*2;if(x+=`
xR = xRCorner + ${E*m};
xC = xCCorner + ${F*f};
`,d===1){if(F<w&&(c%2===1?x+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${i} && xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${o}) {
xTexelR${E}C${F}.zw = vec2(0.);
}
} else {
xTexelR${E}C${F} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${i} && xCOffset >= 0 && xCOffset < ${o}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${o}) {
previous.zw = vec2(0.);
}
xR${E}C${F} = vec4(previous.zw, xTexelR${E}C${F}.xy);
} else {
xR${E}C${F} = vec4(0, 0, xTexelR${E}C${F}.xy);
}
`:x+=`
if(xR >= 0 && xR < ${i} && xC >= 0 && xC < ${o}) {
xTexelR${E}C${F} = getX(batch, xR, xC, d1);
} else {
xTexelR${E}C${F} = vec4(0.);
}
xR${E}C${F} = xTexelR${E}C${F};
`,F+1<w)){const U=c%2===0?T(f):f;f%2===0&&c%2===1||f%2!==0&&c%2!==1?(x+=`
xCOffset = xC + ${c%2} + ${U};
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F+2} = getX(batch, xR, xCOffset, d1);
}
`,f>1&&(x+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${E}C${F} = vec4(0.);
}
`),x+=`
xR${E}C${F+1} = vec4(
xTexelR${E}C${F}.zw, xTexelR${E}C${F+2}.xy);
`):x+=`
xCOffset = xC + ${U};
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F+2} = getX(batch, xR, xCOffset, d1);
}
xR${E}C${F+1} = xTexelR${E}C${F+2};
`}}else F<w&&(x+=`
if(xR >= 0 && xR < ${i}) {
`,c%2===1?(x+=`
xCOffset = xC + 1 - ${d};
if(xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${E}C${F} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${o}) {
xTexelR${E}C${F+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${E}C${F+2} = vec4(0.);
}
xR${E}C${F} = vec4(
xTexelR${E}C${F}.zw, xTexelR${E}C${F+2}.zw);
`,F+1<w&&(x+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${d};
if(xCOffset >= 0 && xCOffset < ${o}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${E}C${F+1} = vec4(xTexelR${E}C${F+2}.xy, final.xy);
`)):(x+=`
if(xC >= 0 && xC < ${o}) {
xTexelR${E}C${F} = getX(batch, xR, xC, d1);
} else {
xTexelR${E}C${F} = vec4(0.);
}
xCOffset = xC + ${d};
if(xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${E}C${F+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${E}C${F+2} = vec4(0.);
}
xR${E}C${F} = vec4(
xTexelR${E}C${F}.xy, xTexelR${E}C${F+2}.xy);
`,F+1<w&&(x+=`
xR${E}C${F+1} = vec4(
xTexelR${E}C${F}.zw, xTexelR${E}C${F+2}.zw);
`)),x+="}");F<w&&(x+=`
vec4 wTexelR${E}C${F} = getW(${E}, ${F}, d1, q);
wR${E}C${F} = vec4(wTexelR${E}C${F}.xz, wTexelR${E}C${F}.xz);
`,F+1<w&&(x+=`
vec4 wTexelR${E}C${F+1} = getW(${E}, ${F+1}, d1, q);
wR${E}C${F+1} =
vec4(wTexelR${E}C${F+1}.xz, wTexelR${E}C${F+1}.xz);`))}for(let E=0;E<b;E++)for(let k=0;k<w;k++)x+=`dotProd += xR${E}C${k} * wR${E}C${k};`;let v="",N="";n&&(s?v=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:v=`vec4 activation(vec4 x) {
${n}
}`,N="result = activation(result);");const O=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${v}
const ivec2 strides = ivec2(${h}, ${d});
const ivec2 pads = ivec2(${a}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${x}
vec4 result = dotProd;
${O}
${N}
setOutput(result);
}
`}}class N8{constructor(e,t,n,s,i){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[o,a,c,h]=e,[d]=t,[m,f]=n;this.outputShape=[d,m,f,h];const b=s==="bilinear"?1:0,[w,L]=[`${a-1}.0`,`${c-1}.0`],[x,v,N]=m>1?[`${(a-1)/(m-1)}`,"(y2-y1) * height_ratio",`y1*${w} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${w}`],[O,E,k]=f>1?[`${(c-1)/(f-1)}`,"(x2-x1) * width_ratio",`x1*${L} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${L}`];this.userCode=`
const float height_ratio = float(${x});
const float width_ratio = float(${O});
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 >= ${o}) {
return;
}
float height_scale = ${v};
float width_scale = ${E};
float in_y = ${N};
if( in_y < 0.0 || in_y > ${w} ) {
setOutput(float(${i}));
return;
}
float in_x = ${k};
if( in_x < 0.0 || in_x > ${L} ) {
setOutput(float(${i}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${b} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}}class cC{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;const s=e.length,i=t?"0.0":`getX(${lC(s,"coords")})`,o=e[e.length-1];let a="",c="";t?(a=n?`end != ${o-1}`:"end != 0",c=n?"end + 1":"end - 1"):(a=n?`end + pow2 < ${o}`:"end >= pow2",c=n?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${Rt(s)} coords = getOutputCoords();
int end = ${hC(s,"coords")};
float val = ${i};
int pow2 = int(pow(2.0, index));
if (${a}) {
int idx = ${c};
${hC(s,"coords")} = idx;
val += getX(${lC(s,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}}function lC(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function hC(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}class C8{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=lu.DENSE;const t=uu(e),n=Pn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Yo(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[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);
}
${n.output} = result;
}
`}}class R8{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=lu.DENSE;const t=uu(e),n=Pn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Yo(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[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));
}
${n.output} = result;
}
`}}class O8{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,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)"}}class E8{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);
}
`}}class D8{constructor(e){this.variableNames=["A"],this.outTexUsage=Ns.DOWNLOAD;const t=Pn();this.outputShape=e,this.userCode=`
${Z0}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}}class k8{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Ns.DOWNLOAD;const t=Pn();this.outputShape=e,this.userCode=`
${Z0}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}}class F8{constructor(e,t,n=!1){this.variableNames=["A"];const s=Pn(),[i,o]=t;this.outputShape=e;let a="result";n&&(a="floor(result * 255. + 0.5)"),this.userCode=`
${IS(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${o};
int c = imod(flatIndex, ${o});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${o}.0, ${i}.0);
vec4 values = ${s.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${s.output} = vec4(${a}, 0., 0., 0.);
}
`}}class _8{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const s=Pn(),[i,o]=t;this.outputShape=e;let a="",c="result";n&&(c="floor(result * 255. + 0.5)");for(let h=0;h<=1;h++)for(let d=0;d<=1;d++){const m=h*2+d;a+=`
localCoords = coords;
if(localCoords[2] + ${d} < ${e[2]}) {
localCoords[2] += ${d};
if(localCoords[1] + ${h} < ${e[1]}) {
localCoords[1] += ${h};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${o};
c = imod(flatIndex, ${o});
uv = (vec2(c, r) + halfCR) / vec2(${o}.0, ${i}.0);
values = ${s.texture2D}(A, uv);
if(offset == 0) {
result[${m}] = values[0];
} else if(offset == 1) {
result[${m}] = values[1];
} else if(offset == 2) {
result[${m}] = values[2];
} else {
result[${m}] = values[3];
}
}
}
`}this.userCode=`
${IS(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${a}
${s.output} = ${c};
}
`}}class W8{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}}class $8{constructor(e,t,n){this.variableNames=["A","indices"];const s=e.slice();s[n]=t,this.outputShape=s,this.rank=s.length;const i=Rt(this.rank),o=U8(e,n);this.userCode=`
void main() {
${i} resRC = getOutputCoords();
setOutput(getA(${o}));
}
`}}function U8(e,t){const n=e.length;if(n>4)throw Error(`Gather for rank ${n} is not yet supported`);if(n===1)return"int(getIndices(resRC))";const s=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[];for(let o=0;o<e.length;o++)o===t?i.push(`int(getIndices(${s[o]}))`):i.push(`${s[o]}`);return i.join()}class B8{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;const s=Rt(t.length),i=Rt(n.length),o=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${s} strides = ${s}(${this.strides});
void main() {
${i} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${o};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}}function M8(e){const t=Pn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return wK(e,n)}function P8(e){const t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return AK(e,t)}function z8(e){const t=new Uint16Array([0,1,2,2,1,3]);return vK(e,t)}function pu(e,t,n,s,i,o){CK(t,n);const a=NK(e),c=e.TEXTURE_2D;return Ee(e,()=>e.bindTexture(c,a)),Ee(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ee(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ee(e,()=>e.texParameteri(c,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ee(e,()=>e.texParameteri(c,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ee(e,()=>e.texImage2D(c,0,s,t,n,0,i,o,null)),Ee(e,()=>e.bindTexture(e.TEXTURE_2D,null)),a}function uC(e){return e.internalFormatFloat}function V8(e,t,n,s){const[i,o]=hu(t,n);return pu(e,i,o,uC(s),s.textureFormatFloat,e.FLOAT)}function dC(e){return e.internalFormatHalfFloat}function G8(e,t,n,s){const[i,o]=hu(t,n);return pu(e,i,o,dC(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function pC(e){return e.downloadTextureFormat}function Y8(e,t,n,s){const[i,o]=hu(t,n);return pu(e,i,o,pC(s),e.RGBA,e.UNSIGNED_BYTE)}function mC(e){return e.internalFormatPackedFloat}function H8(e,t,n,s){const[i,o]=pc(t,n);return pu(e,i,o,mC(s),e.RGBA,e.FLOAT)}function fC(e){return e.internalFormatPackedHalfFloat}function q8(e,t,n,s){const[i,o]=pc(t,n);return pu(e,i,o,fC(s),e.RGBA,s.textureTypeHalfFloat)}function j8(e,t,n){const s=0,i=3*4,o=3*4+2*4;Ee(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=q0(e,t,"clipSpacePos",n,3,o,s);return a&&q0(e,t,"uv",n,2,o,i)}function K8(e,t,n,s,i,o){Ee(e,()=>e.bindTexture(e.TEXTURE_2D,t));let a,c,h;i instanceof Uint8Array?(a=new Uint8Array(n*s*4),c=e.UNSIGNED_BYTE,h=e.RGBA):(a=new Float32Array(n*s*4),c=e.FLOAT,h=o.internalFormatPackedFloat),a.set(i),Ee(e,()=>e.texImage2D(e.TEXTURE_2D,0,h,n,s,0,e.RGBA,c,a)),Ee(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function X8(e,t,n){Ee(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ee(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ee(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ee(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function J8(e,t,n,s){const i=e.createBuffer();Ee(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const o=4,a=4,c=o*a*t*n;return Ee(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,c,e.STREAM_READ)),Ee(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ee(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function Z8(e,t,n){const s=e,i=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,i),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),i}function Q8(e,t,n,s){const[i,o]=hu(t,n),a=4,c=new Uint8Array(dK(t*n,a));return Ee(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function e6(e,t,n,s,i,o,a,c){const h=e,d=new Float32Array(pK(o,a));return h.bindBuffer(h.PIXEL_PACK_BUFFER,t),h.getBufferSubData(h.PIXEL_PACK_BUFFER,0,d),h.bindBuffer(h.PIXEL_PACK_BUFFER,null),d}function t6(e,t,n){const s=new Float32Array(t*n*4);return Ee(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}class n6{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=oe().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,lK(t,e)):this.gl=ki(t);let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(oe().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",o="OES_texture_half_float";if(this.textureFloatExtension=vm(this.gl,i),Vs(this.gl,o))this.textureHalfFloatExtension=vm(this.gl,o);else if(oe().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Vs(this.gl,s))this.colorBufferHalfFloatExtension=vm(this.gl,s);else if(oe().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Vs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Vs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=P8(this.gl),this.indexBuffer=z8(this.gl),this.framebuffer=RK(this.gl),this.textureConfig=gS(this.gl,this.textureHalfFloatExtension)}get debug(){return oe().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;Ee(e,()=>e.finish()),Ee(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ee(e,()=>e.deleteFramebuffer(this.framebuffer)),Ee(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ee(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ee(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),V8(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),G8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Y8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),X8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),K8(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),q8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),H8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(j0(this.gl,this.framebuffer),this.outputTexture=null),Ee(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Q8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return e6(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Z8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=J8(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(oe().getBool("WEBGL_FENCE_API_ENABLED")){const s=e,i=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const o=s.clientWaitSync(i,0,0);return o===s.ALREADY_SIGNALED||o===s.CONDITION_SATISFIED},t=i}else oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>t6(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=LK(t,e),s=M8(t),i=xK(t);return Ee(t,()=>t.attachShader(i,s)),Ee(t,()=>t.attachShader(i,n)),TK(t,i),this.debug&&yS(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=j8(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ee(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&yS(this.gl,this.program),Ee(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?EK(this.gl,e,t):DK(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ee(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),kK(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=pc(t,n);this.setOutputMatrixTextureDriver(e,s,i)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&yS(this.gl,this.program),Nm(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Ee(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ee(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=vm(this.gl,oe().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(oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,i),i}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await $t(()=>this.disposed||this.isQueryAvailable(e,oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const n=this.gl,s=n.getQueryParameter(e,n.QUERY_RESULT);return s/1e6}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT);return s/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=s6(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;$t(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),bS(this.gl,e,this.framebuffer),this.debug&&Nm(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bS(this.gl,this.outputTexture,this.framebuffer),this.debug&&Nm(this.gl)):j0(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const s=this.gl;bS(s,e,this.framebuffer),this.debug&&Nm(s),this.outputTexture=e,Ee(s,()=>s.viewport(0,0,t,n)),Ee(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Ee(this.gl,()=>this.gl.scissor(e,t,n,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 s6(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function i6(e,t,n,s){const i=t.userCode,o=n.map((w,L)=>{const x={logicalShape:w.shape,texShape:w.isUniform?null:w.texData.texShape,isUniform:w.isUniform,isPacked:w.isUniform?!1:w.texData.isPacked,flatOffset:null};return w.texData!=null&&w.texData.slice!=null&&w.texData.slice.flatOffset>0&&(x.flatOffset=w.texData.slice.flatOffset),{name:t.variableNames[L],shapeInfo:x}}),a=o.map(w=>w.shapeInfo),c={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},h=o5(o,c,i,t.packedInputs),d=e.createProgram(h);let m=null;const f=e.getUniformLocation(d,"NAN",!1);oe().getNumber("WEBGL_VERSION")===1&&(m=e.getUniformLocation(d,"INFINITY",!1));const b={};for(let w=0;w<t.variableNames.length;w++){const L=t.variableNames[w],x=!1;b[L]=e.getUniformLocation(d,L,x),b[`offset${L}`]=e.getUniformLocation(d,`offset${L}`,x)}return{program:t,source:h,webGLProgram:d,uniformLocations:b,inShapeInfos:a,outShapeInfo:c,infLoc:m,nanLoc:f}}function gC(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{const i=n.logicalShape,o=t[s],a=o.shape;if(!ae(i,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${i} and ${a} must match`);if(n.isUniform&&o.isUniform)return;const c=n.texShape,h=o.isUniform?null:o.texData.texShape;if(!ae(c,h))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${c} and ${h} must match`)})}function r6(e,t,n,s,i){gC(t.inShapeInfos,n),gC([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),oe().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,h)=>{const d=t.program.variableNames[h],m=t.uniformLocations[d],f=t.uniformLocations[`offset${d}`];if(m==null)return;if(c.isUniform){if(P(c.shape)<2)e.gl.uniform1f(m,c.uniformValues[0]);else{let b=c.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),e.gl.uniform1fv(m,b)}return}c.texData.slice!=null&&f!=null&&e.gl.uniform1i(f,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,m,h)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function o6(e,t,n){let s="";t.concat(n).forEach(a=>{const c=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,h=a.isUniform?"uniform":a.texData.texShape;s+=`${a.shape}_${h}_${c}`});const i=e.userCode;let o=e.constructor.name;return o+="_"+s+"_"+i,o}class a6{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:s,inChannels:i,strideWidth:o,strideHeight:a,padInfo:c,outWidth:h,dilationWidth:d,dilationHeight:m,dataFormat:f}=n,{left:b,top:w}=c,L=i*s,x=Pn(),v=f==="channelsLast",N=v?0:1,O=v?1:2;let E="";for(let k=0;k<=1;k++)for(let F=0;F<=1;F++)E+=`
blockIndex = rc.y + ${F};
pos = rc.x + ${k};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${h})) * ${a} - ${w};
d0 = offsetY + ${m} * (pos / ${L});
if(d0 < ${t[N]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${h}.) * ${o}. - ${b}.);
d1 = offsetX + ${d} * (int(mod(float(pos), ${L}.) / ${i}.));
if(d1 < ${t[O]} && d1 >= 0) {
ch = int(mod(float(pos), ${i}.));
if (${v}) {
innerDims = vec2(d1, ch);
result[${k*2+F}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${k*2+F}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${E}
${x.output} = result;
}
`}}class c6{constructor(e,t,n,s,i){this.variableNames=["x"],this.outputShape=[];const o=t,a=e[3]-1;this.outputShape=e;let c;const h=`float(${n}) + float(${s}) * sum`;i===.5?c=`inversesqrt(${h})`:i===1?c=`1.0/(${h})`:c=`exp(log(${h}) * float(-${i}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${o}; j <= ${o}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${a}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${c};
setOutput(val);
}
`}}class l6{constructor(e,t,n,s,i){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=i,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(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${s})
* float(${i})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${i});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}}class h6{constructor(e,t,n,s,i){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const o=t,a=e[3]-1;this.outputShape=e;let c;const h=`float(${n}) + float(${s}) * sum`;i===.5?c=`inversesqrt(${h})`:i===1?c=`1.0/(${h})`:c=`exp(log(${h}) * float(-${i}));`,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 - ${o};
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 = - ${o}; j <= ${o}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${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 * ${c};
setOutput(result);
}
`}}class u6{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,i=e.effectiveFilterHeight,o=e.effectiveFilterWidth,a=i-1-e.padInfo.top,c=o-1-e.padInfo.left,h=i*o-1;this.userCode=`
const ivec2 pads = ivec2(${a}, ${c});
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 < ${i};
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 < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.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 = ${h} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${o} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}}class d6{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,i=e.dilationDepth,o=e.dilationHeight,a=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,m=c-1-e.padInfo.front,f=h-1-e.padInfo.top,b=d-1-e.padInfo.left,w=c*h*d-1;this.userCode=`
const ivec3 pads = ivec3(${m}, ${f}, ${b});
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 < ${c};
wD += ${i}) {
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 < ${h};
wR += ${o}) {
float dyR = float(dyRCorner + wR) / ${n}.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 = ${w} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${h} * ${d} +
wR * ${d} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}}class xS{constructor(e,t,n,s=!1,i=!1,o=!1,a=null,c=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;const h=s?e[1]:e[2],d=Math.ceil(h/2),m=s?"i * 2, rc.y":"rc.y, i * 2",f=i?"rc.z, i * 2":"i * 2, rc.z",b=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],w=i?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let L="",x="";a&&(c?L=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${a}
}`:L=`vec4 activation(vec4 x) {
${a}
}`,x="result = activation(result);");const v=o?"result += getBiasAtOutCoords();":"";o&&this.variableNames.push("bias"),c&&this.variableNames.push("preluActivationWeights");let N="rc.x",O="rc.x";e[0]<t[0]?N=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(O=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${L}
const float sharedDimension = ${d}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${d}; i++) {
int batchA = ${N};
int batchB = ${O};
vec4 a = getMatrixA(batchA, ${m});
vec4 b = getMatrixB(batchB, ${f});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${b[0]} * ${w[0]});
result += (${b[1]} * ${w[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${v}
${x}
setOutput(result);
}
`}}class p6{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
uniform float seed;
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}));
}
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}}class m6{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${s}), float(${n}),
float(index == coords.y)));
}
`}}class f6{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;const t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{const n=Mn("rc",t),s=Rt(t),i=y6(t,e,n),o=b6(t,e[e.length-1],e[e.length-2],n),a=w6(e,n);this.userCode=`
void main() {
${s} rc = getOutputCoords();
if(${i}) {
setOutput(vec4(0));
} else {
${o}
setOutput(vec4(${a}));
}
}
`}}}function g6(e,t){const n=[];for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=`${s===0?"r":"rp1"}, ${i===0?"c":"cp1"}`;for(let a=2;a<e;a++)o=`${t[t.length-1-a]},`+o;n.push(o)}return n}function y6(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let i=e-2;i<e;i++)s+=`${n[i]} >= ${t[i]}`,i<e-1&&(s+="||");return s}function b6(e,t,n,s){if(e===1)return"";const i=s.slice(-2);return`
int r = ${i[0]};
int c = ${i[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function w6(e,t){const n=e.length,s=g6(n,t);return n===1?`getA(rc),
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${s[0]}),
cEdge ? 0. : getA(${s[1]}),
rEdge ? 0. : getA(${s[2]}),
rEdge || cEdge ? 0. : getA(${s[3]})`}class L6{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((h,d)=>h[0]+e[d]+h[1]);const s=e.length,i=Rt(s),o=t.map(h=>h[0]).join(","),a=t.map((h,d)=>h[0]+e[d]).join(","),c=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
int start = ${o};
int end = ${a};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(float(${n}));
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${i} start = ${i}(${o});
${i} end = ${i}(${a});
void main() {
${i} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(float(${n}));
} else {
${i} coords = outC - start;
setOutput(getX(${c}));
}
}
`}}class S6{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((L,x)=>L[0]+e[x]+L[1]);const s=e.length,i=Rt(s),o=t.map(L=>L[0]).join(","),a=t.map((L,x)=>L[0]+e[x]).join(","),c=Mn("rc",s),h=Mn("source",s),d=`${c[s-1]} < ${this.outputShape[s-1]}`,m=s===1?"source":`vec2(${h.slice(-2).join()})`,f=[`${i} rc = outputLoc;`,`${c[s-1]} += 1;
if(${d}) {
`,s===1?"":`}
rc = outputLoc;
${c[s-2]} += 1;
if(${c[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${c[s-1]} += 1;
if(${d}) {`],b=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let w="";for(let L=0,x=s===1?2:4;L<x;L++)w+=`
${f[L]}
if (${b}) {
result[${L}] = float(${n});
} else {
${i} source = rc - start;
result[${L}] = getChannel(getX(${h.join()}), ${m});
}
`;w+=s===1?"} ":"}}",this.userCode=`
const ${i} start = ${i}(${o});
const ${i} end = ${i}(${a});
void main() {
${i} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${w}
setOutput(result);
}
`}}class mu{constructor(e,t,n,s=!1,i=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");const o=e.filterWidth,a=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,b=e.padInfo.top,w=e.padInfo.left;this.outputShape=e.outShape;const L=t==="avg",x=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,v=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let N="0.0";if(L||(N="-1.0 / 1e-20"),n){const $=">=";this.userCode=`
const ivec2 strides = ivec2(${a}, ${c});
const ivec2 pads = ivec2(${b}, ${w});
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 < ${m};
wR += ${h}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
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?i?x:v:`wR * ${f} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}const O="max";let E=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(E="avgValue / count");const k=Math.floor(o/4)*4,F=o%4,U=`
if (${L}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${O}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${a}, ${c});
const ivec2 pads = ivec2(${b}, ${w});
const float initializationValue = ${N};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${N});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${m};
wR += ${h}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${k}; 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)
);
${U}
}
int xC = xCCorner + ${k};
if (${F===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${U}
} else if (${F===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${d}, d),
initializationValue,
initializationValue
);
${U}
} else if (${F===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${d}, d),
getValue(batch, xR, xC + 2 * ${d}, d),
initializationValue
);
${U}
}
}
setOutput(${E});
}
`}}class TS{constructor(e,t,n,s=!1,i=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");const o=e.filterWidth,a=e.strideDepth,c=e.strideHeight,h=e.strideWidth,d=e.dilationDepth,m=e.dilationHeight,f=e.dilationWidth,b=e.effectiveFilterDepth,w=e.effectiveFilterHeight,L=e.effectiveFilterWidth,x=e.padInfo.front,v=e.padInfo.top,N=e.padInfo.left;this.outputShape=e.outShape;const O=t==="avg";let E="0.0";if(O||(E="-1.0 / 1e-20"),n){const j=">=";this.userCode=`
const ivec3 strides =
ivec3(${a}, ${c}, ${h});
const ivec3 pads = ivec3(${x}, ${v}, ${N});
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 < ${b};
wD += ${d}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${w};
wR += ${m}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${L};
wC += ${f}) {
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 ${j} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?i?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${w} * ${L} +
wR * ${L} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}const k="max";let F=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(F="avgValue / count");const U=Math.floor(o/4)*4,$=o%4,Y=`
if (${O}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${k}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${a}, ${c}, ${h});
const ivec3 pads = ivec3(${x}, ${v}, ${N});
const float initializationValue = ${E};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${E});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${b};
wD += ${d}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${w};
wR += ${m}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${U}; wC += 4) {
int xC = xCCorner + wC * ${f};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${f}, ch),
getValue(batch, xD, xR, xC + 2 * ${f}, ch),
getValue(batch, xD, xR, xC + 3 * ${f}, ch)
);
${Y}
}
int xC = xCCorner + ${U};
if (${$===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${Y}
} else if (${$===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${f}, ch),
initializationValue,
initializationValue
);
${Y}
} else if (${$===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${f}, ch),
getValue(batch, xD, xR, xC + 2 * ${f}, ch),
initializationValue
);
${Y}
}
}
setOutput(${F});
}
}
`}}class yC{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:s,inSize:i,outSize:o}=e;this.outputShape=[s,o];let a="0.0",c="";t==="prod"?a="1.0":t==="min"?(a="1.0 / 1e-20",c="min"):t==="max"&&(a="-1.0 / 1e-20",c="max");let h=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?h="sumValue":t==="prod"?h="prodValue":t==="all"?h="allValue":t==="any"&&(h="anyValue");const d=Math.floor(n/4)*4,m=n%4;let f=`
if (${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 = ${c}(values, minMaxValue);
}
`,b="vec4";t==="all"?(a="1.0",f=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,b="bvec4"):t==="any"&&(a="0.0",f=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,b="bvec4");let w="";i%n>0&&(w=`
if (inIdx < 0 || inIdx >= ${i}) {
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) {
${w}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${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;
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${f}
}
int inIdx = inOffset + ${d};
if (${m===1}) {
${b} values = ${b}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${f}
} else if (${m===2}) {
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${f}
} else if (${m===3}) {
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${f}
}
setOutput(${h});
}
`}}class bC{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let s=0;s<4;s++){let i="thisRC = rc;";s%2===1&&(i+="thisRC.z += 1;"),s>1&&(i+="thisRC.y += 1;"),n+=`
${i}
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${s}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${s>0?"}":""}
`}this.userCode=`
${I6(t)}
${IS(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${n}
setOutput(result);
}
`}}function I6(e){const t=Yo(["r","c","d"],e);return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t}
return ivec3(r, c, d);
}
`}class x6{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;const[,s,i]=t.shape,[,o,a]=e.shape,c=[n&&o>1?s-1:s,n&&a>1?i-1:i],h=[n&&o>1?o-1:o,n&&a>1?a-1:a],d=c[0]/h[0],m=c[1]/h[1],f=1/d,b=1/m,w=Math.ceil(f)*2+2,L=Math.ceil(b)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${d});
const float widthScale = float(${m});
const float invHeightScale = float(${f});
const float invWidthScale = float(${b});
const int winHeight = int(${w});
const int winWidth = int(${L});
// 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 >= ${o}) {
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), ${i-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);
}
`}}class T6{constructor(e,t,n,s){this.variableNames=["A"],this.outputShape=[];const[i,o,a,c]=e;this.outputShape=[i,t,n,c];const h=[s&&t>1?o-1:o,s&&n>1?a-1:a],d=[s&&t>1?t-1:t,s&&n>1?n-1:n];this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${h[0]/d[0]},
${h[1]/d[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${a}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);
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 A6{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,o,a,c]=e;this.outputShape=[i,t,n,c];const h=[s&&t>1?o-1:o,s&&n>1?a-1:a],d=[s&&t>1?t-1:t,s&&n>1?n-1:n];this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${h[0]/d[0]},
${h[1]/d[1]},
${h[1]/d[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${a}.0,
${a}.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 = vec3(yRC) * effectiveInputOverOutputRatioRC;
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);
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 < ${n-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);
}
`}}class v6{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;const[,s,i]=t.shape,[,o,a]=e.shape,c=[n&&o>1?s-1:s,n&&a>1?i-1:i],h=[n&&o>1?o-1:o,n&&a>1?a-1:a],d=c[0]/h[0],m=c[1]/h[1],f=1/d,b=1/m,w=Math.ceil(f)*2+2,L=Math.ceil(b)*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(${m});
const float invHeightScale = float(${f});
const float invWidthScale = float(${b});
const int winHeight = int(${w});
const int winWidth = int(${L});
// 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 >= ${o}) {
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 >= ${a}) {
continue;
}
float sourceFracRow =
float(${c[0]}) *
(float(dyR) / float(${h[0]}));
float sourceFracCol =
float(${c[1]}) *
(float(dyC) / float(${h[1]}));
int sourceNearestRow = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${i}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}}class N6{constructor(e,t,n,s){this.variableNames=["A"],this.outputShape=[];const[i,o,a,c]=e;this.outputShape=[i,t,n,c];const h=[s&&t>1?o-1:o,s&&n>1?a-1:a],d=[s&&t>1?t-1:t,s&&n>1?n-1:n],m=s?"0.5":"0.0";this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${h[0]/d[0]},
${h[1]/d[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${a}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}}class C6{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===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}]`,i=e.map((a,c)=>s(c)).join(","),o=Rt(n);this.userCode=`
void main() {
${o} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}}class R6{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;const s=Mn("rc",n),i=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,o=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,a=Rt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${i}){
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 = ${c(s.slice())};
if(${i}){
result.g = ${h(s.slice())};
}
if(${o}) {
result.b = ${d(s.slice())};
if(${i}) {
result.a = ${m(s.slice())};
}
}
setOutput(result);
}
`;function c(w){return f(w)}function h(w){return w[n-1]="("+w[n-1]+" + 1)",f(w)}function d(w){return w[n-2]="("+w[n-2]+" + 1)",f(w)}function m(w){return w[n-1]="("+w[n-1]+" + 1)",w[n-2]="("+w[n-2]+" + 1)",f(w)}function f(w){const L=e.map((N,O)=>b(O,w)),x=L.join(","),v=L.slice(-2).join(",");return`getChannel(getX(${x}), vec2(${v}))`}function b(w,L){return t.indexOf(w)!==-1&&e[w]!==1?`${e[w]} - ${L[w]} - 1`:`${L[w]}`}}}class wC{constructor(e,t,n,s,i,o,a=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=o;const c=Rt(i.length),h=Rt(o.length);let d="";n===1?d="i":n===2&&(d="i, j");const m=`getIndices(${d})`;let f="";s===1?f="i":s===2&&(f="i, coords[1]");const b=`getUpdates(${f})`,w=t>1?"strides[j]":"strides";this.userCode=`
${c} strides = ${c}(${i});
void main() {
${h} 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 * ${w};
}
if (flattenedIndex == coords[0]) {
sum += ${b};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}}class O6{constructor(e,t){this.variableNames=["x","segmentIds"];const n=e.windowSize,s=e.batchSize,i=e.inSize,o=e.numSegments,a=o*Math.ceil(i/n);this.outputShape=[s,a];const c="0.0",h="sumValue",d=Math.floor(n/4)*4,m=n%4,f=`
sumValue += dot(values, segFilter);
`;let b="";i%n>0&&(b=`
if (inIdx < 0 || inIdx >= ${i}) {
return initializationValue;
}
`);let w="";i%n>0&&(w=`
if (inIdx < 0 || inIdx >= ${i}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${c};
float getValue(int batch, int inIdx) {
${b}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${w}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${o})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${o})));
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
);
${f}
}
int inIdx = inOffset + ${d};
if (${m===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
);
${f}
} else if (${m===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
);
${f}
} else if (${m===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
);
${f}
}
setOutput(${h});
}
`}}class E6{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,i;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)i="resRC",s="resRC";else{const a=["resRC.x","resRC.y","resRC.z","resRC.w"],c=[],h=[];for(let d=0;d<t.length;d++)h.push(`${a[d]}`),d<e&&c.push(`${a[d]}`);s=c.join(),i=h.join()}const o=Rt(n);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${i}));
} else {
setOutput(getB(${i}));
}
}
`}}class D6{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=Rt(this.rank),n=`uniform int start[${this.rank}];`,s=k6(this.rank);let i;const o=e.map((a,c)=>`sourceLoc.${AS[c]} = start[${c}] + coords.${AS[c]};`);i=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${o.join(`
`)}
`,this.userCode=`
${n}
void main() {
${i}
setOutput(getSource(${s}));
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}const AS=["x","y","z","w","u","v"];function k6(e){if(e===1)return"sourceLoc";if(e<=6)return AS.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}class F6{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;const t=Rt(this.rank),n=Mn("coords",this.rank),s=Mn("sourceLoc",this.rank),i=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,o=`getChannel(getSource(${s.join()}), ${i})`,a=`
result.x = ${o};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.y = ${o};
--${s[this.rank-1]};
}
`,c=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${s[this.rank-2]};
result.z = ${o};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.w = ${o};
}
}
`,h=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((d,m)=>`start[${m}]`).join()});`:e.map((d,m)=>`${s[m]} = ${n[m]} + start[${m}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${h}
vec4 result = vec4(0.);
${a}
${c}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}class _6{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const s=n.length,i=Rt(n.length),o=Rt(n.length);let a="";if(s===1)a="coords * strides + begin";else{let c=0;a=n.map((h,d)=>(c++,n.length===1?`coords * strides[${d}] + begin[${d}]`:`coords[${c-1}] * strides[${d}] + begin[${d}]`)).join(",")}this.userCode=`
${i} begin = ${i}(${e});
${i} strides = ${i}(${t});
void main() {
${o} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}}class W6{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const s=SC(t,n),i=IC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=LC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=o,this.log();const c=this.freeTextures[i].shift();return this.usedTextures[i].push(c),c}let a;return s===Cn.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Cn.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Cn.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Cn.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Cn.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[i].push(a),this.numUsedTextures++,this._numBytesAllocated+=o,this.log(),a}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;const i=SC(n,s),o=IC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=LC(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,s),c=oe().get("WEBGL_DELETE_TEXTURE_THRESHOLD");c!==-1&&this._numBytesAllocated>c?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=a):(this.freeTextures[o].push(e),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;const h=this.usedTextures[o],d=h.indexOf(e);if(d<0)throw new Error("Cannot release a texture that was never provided by this texture manager");h.splice(d,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function $6(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function LC(e,t,n,s,i){const o=U6(t,s);let a;if(i){const[h,d]=pc(e[0],e[1]);a=h*d}else{const[h,d]=hu(e[0],e[1]);a=h*d}const c=$6(n,o);return a*c}function U6(e,t){switch(e){case Cn.PACKED_2X2_FLOAT32:return mC(t);case Cn.PACKED_2X2_FLOAT16:return fC(t);case Cn.UNPACKED_FLOAT32:return uC(t);case Cn.UNPACKED_FLOAT16:return dC(t);case Cn.PACKED_4X1_UNSIGNED_BYTE:return pC(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function B6(e){return oe().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Cn.PACKED_2X2_FLOAT32:Cn.UNPACKED_FLOAT32:e?Cn.PACKED_2X2_FLOAT16:Cn.UNPACKED_FLOAT16}function SC(e,t){if(e===Ns.UPLOAD)return Cn.PACKED_2X2_FLOAT32;if(e===Ns.RENDER||e==null)return B6(t);if(e===Ns.DOWNLOAD||e===Ns.PIXELS)return Cn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function IC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class M6{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[o]*t[o];this.outputShape=n,this.rank=n.length;const s=Rt(this.rank),i=P6(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${i}));
}
`}}function P6(e){const t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let i=0;i<e.length;i++)s.push(`imod(${n[i]}, ${e[i]})`);return s.join()}class st{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}}const hr="if (isnan(x)) return x;",z6="return x;",xC="return abs(x);",TC=hr+`
return (x < 0.0) ? 0.0 : x;
`,AC=hr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,vC="return (x >= 0.0) ? x : (exp(x) - 1.0);",V6=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${Wp};
float scale = ${$p};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;function G6(e=0){return hr+`
return x > 0.0 ? 1.0 : float(${e});
`}const NC="return -x;",CC="return ceil(x);",RC="return floor(x);",Y6=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,H6="return float(isnan(x));",q6="return float(isinf(x));",j6="return float(!isnan(x) && !isinf(x));",K6=`
// 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;
}
}
`,OC="return exp(x);",EC="return exp(x) - 1.0;",X6=`if (x < 0.0) return NAN;
return log(x);`,J6="return log(1.0 + x);",Z6="return sqrt(x);",Q6="return inversesqrt(x);",eX="return 1.0 / (1.0 + exp(-1.0 * x));",tX=`
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;
`,nX=hr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,sX=hr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,iX=hr+`
return atan(x);
`,rX=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,oX=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,aX=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,cX=hr+"return log(x + sqrt(x * x + 1.0));",lX=hr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,hX=hr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,uX=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${ew};
float a1 = ${tw};
float a2 = ${nw};
float a3 = ${sw};
float a4 = ${iw};
float a5 = ${rw};
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));
`,dX="return 1.0 / x;",pX="return float(!(x >= 1.0));",Fm="return x;";const mX="return x;",fX=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,DC=`
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;
`,kC=`
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;
`,FC=`
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;
`;class fu{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}}class gX{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;const t=e.length,n=Mn("rc",t),s=Rt(t),i=r5(t,n),o=n.slice(-2),a=t<=1?"rc":`vec2(${o.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${i});
setOutput(getChannel(packedInput, ${a}));
}
`}}const{segment_util:_C}=cw,yX=lw,bX=hw,wX=uw,LX=Ap,SX=1e-7,IX=1e-4,_m={};function xX(e){return e in _m||(_m[e]={}),_m[e]}function Wm(e,t=!1){if(e==="linear")return t?mX:z6;if(e==="relu")return t?DC:TC;if(e==="elu")return t?FC:vC;if(e==="relu6")return t?kC:AC;if(e==="prelu")return t?iC:sC;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const TX=128,AX=600;function vX(){return oe().global.screen==null?1024:oe().global.screen.height*oe().global.screen.width*window.devicePixelRatio*AX/1024/1024}const WC=1e3;class NX extends y{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!oe().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=ki(oe().getNumber("WEBGL_VERSION"));this.binaryCache=xX(oe().getNumber("WEBGL_VERSION")),this.gpgpu=new n6(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new W6(this.gpgpu),this.numMBBeforeWarning=vX(),this.texData=new p(this,Ki())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((oe().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||oe().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const s={};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:Ns.UPLOAD,refCount:1,complexParentRefCount:0}),s}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,n,s){if(oe().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:n,dtype:s,values:t,usage:Ns.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.texData.has(t)){const n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){const t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:i,slice:o,shape:a,isPacked:c}=t;if(o!=null){let f;c?f=new fu(a,Fm):f=new st(a,Fm);const b=this.runWebGLProgram(f,[{dataId:e,shape:a,dtype:s}],s),w=this.readSync(b.dataId);return this.disposeIntermediateTensorInfo(b),w}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;const h=this.activeTimers!=null;let d;h&&(d=jn());let m;if(s==="complex64"){const f=this.readSync(i.real.dataId),b=this.readSync(i.imag.dataId);m=tr(f,b)}else m=this.getValuesFromTexture(e);return h&&(this.downloadWaitMs+=jn()-d),this.convertAndCacheOnCPU(e,m)}async read(e){if(this.pendingRead.has(e)){const w=this.pendingRead.get(e);return new Promise(L=>w.push(L))}const t=this.texData.get(e),{values:n,shape:s,slice:i,dtype:o,complexTensorInfos:a,isPacked:c}=t;if(i!=null){let w;c?w=new fu(s,Fm):w=new st(s,Fm);const L=this.runWebGLProgram(w,[{dataId:e,shape:s,dtype:o}],o),x=this.read(L.dataId);return this.disposeIntermediateTensorInfo(L),x}if(n!=null)return this.convertAndCacheOnCPU(e);if(!oe().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&oe().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let h=null,d;if(o!=="complex64"&&oe().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);const w=this.texData.get(d.dataId);h=this.gpgpu.createBufferFromTexture(w.texture,...uu(s))}this.pendingRead.set(e,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let m;if(o==="complex64"){const w=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),L=w[0],x=w[1];m=tr(L,x)}else if(h==null)m=this.getValuesFromTexture(e);else{const w=P(s);m=this.gpgpu.downloadFloat32MatrixFromBuffer(h,w)}d!=null&&this.disposeIntermediateTensorInfo(d);const f=this.convertAndCacheOnCPU(e,m),b=this.pendingRead.get(e);return this.pendingRead.delete(e),b.forEach(w=>w(f)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),f}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const n=e[t];if(!yK(n))throw oe().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=P(t);if(oe().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const f=this.decode(e),b=this.texData.get(f.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...uu(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(f),w}const o=oe().getBool("WEBGL_PACK")&&s===!0,a=o?wS(t):t,c=o?new k8(a):new D8(a),h=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),d=this.texData.get(h.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(h),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=te(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=te(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=C(c),a.getExtraProfileInfo=()=>c.map((h,d)=>({name:o[d],ms:h})).map(h=>`${h.name}: ${h.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 oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:jn(),endMs:null}}endTimer(e){return oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=jn(),e)}async getQueryTime(e){if(oe().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);const{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}releaseGPUData(e){const{texture:t,dtype:n,texShape:s,usage:i,isPacked:o,slice:a}=this.texData.get(e),c=a&&a.origDataId||e,h=this.dataRefCount.get(c);h>1?this.dataRefCount.set(c,h-1):(this.dataRefCount.delete(c),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,i,o)));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}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return oe().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Ki().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=TX){const n=this.getCPUBackend();return!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(s=>this.texData.get(s.dataId).texture==null&&P(s.shape)<t)}getGPGPUContext(){return this.gpgpu}slice(e,t,n){if(this.shouldExecuteOnCPU([e])){const o=QK(this.texData.get(e.dataId).values,t,n,e.shape,e.dtype);return this.makeOutput(n,e.dtype,o)}if(P(n)===0)return sn([],n,e.dtype);const{isPacked:s}=this.texData.get(e.dataId),i=eb(e.shape,t,n);if(s||!i){const o=oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new F6(n):new D6(n),a=o.getCustomSetupFunc(t);return this.compileAndRun(o,[e],null,a)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,n)}shallowSlice(e,t,n){const s=this.texData.get(e.dataId),i=this.makeOutput(n,e.dtype),o=this.texData.get(i.dataId);Object.assign(o,s),o.shape=n,o.dtype=e.dtype;let a=tb(t,e.strides);s.slice&&(a+=s.slice.flatOffset),o.slice={flatOffset:a,origDataId:s.slice&&s.slice.origDataId||e.dataId};const c=this.dataRefCount.get(o.slice.origDataId)||1;return this.dataRefCount.set(o.slice.origDataId,c+1),i}stridedSlice(e,t,n,s){const i=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.stridedSlice(e,t,n,s));if(i)return i;const o=jd(t,n,s);if(o.some(c=>c===0))return sn([],o);const a=new _6(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new R6(e.shape,t):new C6(e.shape,t);return this.compileAndRun(n,[e])}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(oe().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,NC,e.dtype);const n=new st(e.shape,NC);return this.compileAndRun(n,[e])}batchMatMul(e,t,n,s){const i=n?e.shape[2]:e.shape[1],o=s?t.shape[1]:t.shape[2],a=n?e.shape[1]:e.shape[2],c=Math.max(e.shape[0],t.shape[0]);if((i===1||o===1)&&a>WC){n&&(e=Ye(e,[0,2,1])),s&&(t=Ye(t,[0,2,1]));const m=o===1?e:e.as3D(c,a,1),f=o===1?2:1,b=o===1?t.as3D(c,1,a):t,w=X(m,b);return w.sum(f,!0)}const h=$n(e.dtype,t.dtype),d=new xS(e.shape,t.shape,[c,i,o],n,s);return this.compileAndRun(d,[e,t],h)}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:s,bias:i,activation:o,preluActivationWeights:a}){const c=n?e.shape[2]:e.shape[1],h=s?t.shape[1]:t.shape[2],d=Math.max(e.shape[0],t.shape[0]),m=$n(e.dtype,t.dtype),f=i!=null,b=a!=null,w=o?Wm(o,!0):null,L=new xS(e.shape,t.shape,[d,c,h],n,s,f,w,b),x=[e,t];return i&&x.push(i),a&&x.push(a),this.compileAndRun(L,x,m)}localResponseNormalization4D(e,t,n,s,i){const o=oe().getBool("WEBGL_PACK_NORMALIZATION")?new h6(e.shape,t,n,s,i):new c6(e.shape,t,n,s,i);return this.compileAndRun(o,[e])}LRNGrad(e,t,n,s,i,o,a){const c=new l6(t.shape,s,i,o,a);return this.compileAndRun(c,[t,n,e])}tile(e,t){if(e.dtype==="string"){const s=this.readSync(e.dataId),i=s.map(a=>Kl(a)),o=wt(e.shape,e.dtype,i);return bX(o,t)}const n=new M6(e.shape,t);return this.compileAndRun(n,[e])}pad(e,t,n){const s=oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new S6(e.shape,t,n):new 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x6(e,t,n);return this.compileAndRun(s,[e])}resizeNearestNeighbor(e,t,n,s){const i=new N6(e.shape,t,n,s);return this.compileAndRun(i,[e])}resizeNearestNeighborBackprop(e,t,n){const s=new v6(e,t,n);return this.compileAndRun(s,[e])}multinomial(e,t,n,s){const i=t?e:Fo(e),o=i.shape[0],a=i.shape[1],c=new p6(o,a,n),h=c.getCustomSetupFunc(s);return this.compileAndRun(c,[i],"int32",h)}oneHot(e,t,n,s){const i=new m6(e.size,t,n,s);return this.compileAndRun(i,[e])}diag(e){const t=new E8(e.size);return this.compileAndRun(t,[e])}cropAndResize(e,t,n,s,i,o){const a=new N8(e.shape,t.shape,s,i,o);return this.compileAndRun(a,[e,t,n],"float32")}depthToSpace(e,t,n){A(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`);const s=e.shape[0],i=n==="NHWC"?e.shape[1]:e.shape[2],o=n==="NHWC"?e.shape[2]:e.shape[3],a=n==="NHWC"?e.shape[3]:e.shape[1],c=i*t,h=o*t,d=a/(t*t),m=n==="NHWC"?[s,c,h,d]:[s,d,c,h],f=new O8(m,t,n);return this.compileAndRun(f,[e])}split(e,t,n){return yX(e,t,n)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=_a(t,e,n),h=[c/o,o],d=e.reshape([i,s]),m=t.reshape([i,o]);if(c===0)return JA(sn([]),n);const f=Ce(0),b=new wC(i,s,d.rank,m.rank,a,h),w=this.compileAndRun(b,[m,d,f]);return w.reshape(n)}sparseToDense(e,t,n,s){const{sliceRank:i,numUpdates:o,strides:a,outputSize:c}=_a(t,e,n),h=!1,d=new wC(o,i,e.rank,t.rank,a,[c,1],h),m=this.compileAndRun(d,[t,e,s]);return m.reshape(n)}gatherND(e,t){const n=t.shape,s=n[n.length-1],[i,o,a,c]=Hd(e,t),h=t.reshape([o,s]),d=e.reshape([e.size/a,a]),m=new B8(s,c,[o,a]),f=this.compileAndRun(m,[d,h]);return f.reshape(i)}fill(e,t,n){if(n=n||wa(t),n==="string"){const s=ws(n,P(e));return s.fill(t),Ki().makeTensor(s,e,n,this)}else{const s=new W8(e,t),i=s.getCustomSetupFunc(t);return this.compileAndRun(s,[],n,i)}}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(e.shape,1,e.dtype)}zerosLike(e){return this.fill(e.shape,e.dtype==="string"?"":0,e.dtype)}linspace(e,t,n){return aw(e,t,n)}makeTensorInfo(e,t,n){const s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){const{dataId:s}=this.makeTensorInfo(e,t,n);return Ki().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new gX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new f6(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){const n=[mc(e.shape),...fc(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},i=[mc(t),...fc(t)],o=new bC(i,n),a=!0,c=this.runWebGLProgram(o,[s],e.dtype,null,a);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e){const t=this.texData.get(e),{isPacked:n,shape:s,dtype:i}=t,o=wS(s);let a;n?a=new R8(o):a=new C8(o);const c=!0,h=this.runWebGLProgram(a,[{shape:o,dtype:i,dataId:e}],i,null,c);return{dtype:i,shape:s,dataId:h.dataId}}runWebGLProgram(e,t,n,s,i=!1){const o=this.makeTensorInfo(e.outputShape,n),a=this.texData.get(o.dataId);if(e.packedOutput&&(a.isPacked=!0),e.outPackingScheme===lu.DENSE){const L=uu(e.outputShape);a.texShape=L.map(x=>x*2)}if(e.outTexUsage!=null&&(a.usage=e.outTexUsage),P(o.shape)===0)return a.values=bt(o.dtype,0),o;const c=[],h=t.map(L=>{if(L.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let x=this.texData.get(L.dataId);if(x.texture==null){if(!e.packedInputs&&P(L.shape)<=oe().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:L.shape,texData:null,isUniform:!0,uniformValues:x.values};e.packedInputs&&(x.isPacked=!0,x.shape=L.shape)}else if(!!x.isPacked!==!!e.packedInputs)L=x.isPacked?this.unpackTensor(L):this.packTensor(L),c.push(L),x=this.texData.get(L.dataId);else if(x.isPacked&&!Rm(x.shape,L.shape)){const v=L,N=L.shape;L.shape=x.shape,L=this.packedReshape(L,N),c.push(L),x=this.texData.get(L.dataId),v.shape=N}return this.uploadToGPU(L.dataId),{shape:L.shape,texData:x,isUniform:!1}});this.uploadToGPU(o.dataId);const d={shape:o.shape,texData:a,isUniform:!1},m=o6(e,h,d),f=this.getAndSaveBinary(m,()=>i6(this.gpgpu,e,h,d)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),r6(this.gpgpu,f,h,d,s),c.forEach(L=>this.disposeIntermediateTensorInfo(L)),b&&(w=this.endTimer(w),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(w)})),!oe().getBool("WEBGL_LAZILY_UNPACK")&&a.isPacked&&i===!1){const L=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),L}return o}compileAndRun(e,t,n,s,i=!1){n=n||t[0].dtype;const o=this.runWebGLProgram(e,t,n,s,i);return Ki().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!oe().getBool("IS_TEST")){const e=Object.keys(this.binaryCache);e.forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&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=Q(()=>{if(!oe().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=oe().getBool("DEBUG");oe().set("DEBUG",!1);const t=this.abs(Ce(1e-8)).dataSync()[0];if(oe().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?SX:IX}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:s,values:i,texture:o,usage:a,isPacked:c}=t;if(o!=null)return;const h=this.activeTimers!=null;let d;h&&(d=jn());let m=t.texShape;if(m==null&&(m=_K(n,c),t.texShape=m),i!=null){const f=wS(n);let b,w=m[1],L=m[0];const x=i instanceof Uint8Array;c?([w,L]=pc(m[0],m[1]),b=new _8(f,[L,w],x)):b=new F8(f,[L,w],x);const v=this.makeTensorInfo([L,w],s);x?this.texData.get(v.dataId).usage=Ns.PIXELS:this.texData.get(v.dataId).usage=Ns.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(v.dataId),w,L,i);const N=!0,O=this.runWebGLProgram(b,[v],s,null,N),E=this.texData.get(O.dataId);t.texture=E.texture,t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,this.disposeIntermediateTensorInfo(v),this.texData.delete(O.dataId),t.values=null,h&&(this.uploadWaitMs+=jn()-d)}else{const f=this.acquireTexture(m,a,s,c);t.texture=f}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=CX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const i=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${i} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*Bg(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(n){if(oe().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function CX(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){const n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}const RX="2.7.0";function OX(){oe().set("WEBGL_FORCE_F16_TEXTURES",!0)}Uy()&&rb("webgl",()=>new NX,2);const one={forceHalfFloat:OX};function ur(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const EX={kernelName:xl,backendName:"webgl",kernelFunc:ur};function Lc(e){const{inputs:t,backend:n}=e,{real:s,imag:i}=t,o=n.makeTensorInfo(s.shape,"complex64"),a=n.texData.get(o.dataId),c=ur({inputs:{x:s},backend:n}),h=n.texData.get(c.dataId);h.complexParentRefCount++;const d=ur({inputs:{x:i},backend:n}),m=n.texData.get(d.dataId);return m.complexParentRefCount++,a.complexTensorInfos={real:c,imag:d},o}const DX={kernelName:rd,backendName:"webgl",kernelFunc:Lc};const $C="if (isnan(x)) return x;",kX=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,FX=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function $m(e){return({inputs:t,backend:n})=>{const{x:s}=t,i=n,o=new st(s.shape,e);return i.runWebGLProgram(o,[s],s.dtype)}}function Sc({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:i,dtype:o}){return({inputs:a,backend:c})=>{const{a:h,b:d}=a,m=c;if(s&&h.dtype==="complex64"){const L=m.texData.get(h.dataId),x=m.texData.get(d.dataId),[v,N]=[[L.complexTensorInfos.real,x.complexTensorInfos.real],[L.complexTensorInfos.imag,x.complexTensorInfos.imag]].map(E=>{const[k,F]=E,U={dataId:k.dataId,dtype:k.dtype,shape:h.shape},$={dataId:F.dataId,dtype:F.dtype,shape:d.shape},Y=new _n(e,h.shape,d.shape);return m.runWebGLProgram(Y,[U,$],$n(k.dtype,F.dtype))}),O=Lc({inputs:{real:v,imag:N},backend:m});return m.disposeIntermediateTensorInfo(v),m.disposeIntermediateTensorInfo(N),O}const f=o||$n(h.dtype,d.dtype);if(m.shouldExecuteOnCPU([h,d])&&i!=null){const L=m.texData.get(h.dataId),x=m.texData.get(d.dataId),[v,N]=i(h.shape,d.shape,L.values,x.values,f),O=m.makeTensorInfo(N,f),E=m.texData.get(O.dataId);return E.values=v,O}const b=oe().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null;let w;return b?w=new lr(t,h.shape,d.shape,n):w=new _n(e,h.shape,d.shape),m.runWebGLProgram(w,[h,d],f)}}const UC="return a + b;",_X=Sc({opSnippet:UC,packedOpSnippet:UC,supportsComplex:!0,cpuKernelImpl:GK}),WX={kernelName:wo,backendName:"webgl",kernelFunc:_X};const $X=kX+`
return atan(a, b);
`,UX=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+FX+`
return result;
`,BX=Sc({opSnippet:$X,packedOpSnippet:UX}),MX={kernelName:nd,backendName:"webgl",kernelFunc:BX};function PX(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;du(i,"avgPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;A(cn(a,d),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Un(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ae(m.inShape,m.outShape))return ur({inputs:{x:i},backend:n});const f=new mu(m,"avg",!1);return n.runWebGLProgram(f,[i],"float32")}const zX={kernelName:dl,backendName:"webgl",kernelFunc:PX};function VX(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;du([i,o],"avgPoolBackprop");const{filterSize:c,strides:h,pad:d}=s,m=Un(a.shape,c,h,1,d),f=new V5(m);return n.runWebGLProgram(f,[i],a.dtype)}const GX={kernelName:sd,backendName:"webgl",kernelFunc:VX};class YX{constructor(e,t,n,s,i,o){this.outputShape=[],this.variableNames=["x","mean","variance"],nt(e,t),nt(e,n);let a="0.0";s!=null&&(nt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="1.0";i!=null&&(nt(e,i),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${a};
float scale = ${c};
float inv = scale * inversesqrt(variance + float(${o}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}}class HX{constructor(e,t,n,s,i,o){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],nt(e,t),nt(e,n);let a="vec4(0.0)";s!=null&&(nt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="vec4(1.0)";i!=null&&(nt(e,i),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${a};
vec4 scale = ${c};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${o}));
setOutput((x - mean) * inv + offset);
}
`}}const qX=({inputs:e,backend:t,attrs:n})=>{const{x:s,mean:i,variance:o,offset:a,scale:c}=e;A(i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),A(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),A(c==null||i.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:h}=n;h==null&&(h=.001);const d=[s,i,o];let m=null;a!=null&&(m=a.shape,d.push(a));let f=null;c!=null&&(f=c.shape,d.push(c));const b=oe().getBool("WEBGL_PACK_NORMALIZATION")?new HX(s.shape,i.shape,o.shape,m,f,h):new YX(s.shape,i.shape,o.shape,m,f,h),w=t.runWebGLProgram(b,d,d[0].dtype);return w},jX={kernelName:Il,backendName:"webgl",kernelFunc:qX};const KX="return float(a != b);",BC=Sc({opSnippet:KX,dtype:"bool"}),XX={kernelName:Dl,backendName:"webgl",kernelFunc:BC};function vS(e){const{inputs:t,backend:n}=e,{input:s}=t,i=n.texData.get(s.dataId);return ur({inputs:{x:i.complexTensorInfos.real},backend:n})}const JX={kernelName:Td,backendName:"webgl",kernelFunc:vS};const ZX="return float(int(x));";function QX(e,t){const n=new st(e.shape,ZX),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function NS(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{dtype:o}=s;if(o==="complex64"){if(i.dtype==="complex64")return ur({inputs:{x:i},backend:n});const a=dt(i.shape),c=NS({inputs:{x:i},backend:n,attrs:{dtype:"float32"}}),h=Lc({inputs:{real:c,imag:a},backend:n});return a.dispose(),n.disposeIntermediateTensorInfo(c),h}if(i.dtype==="complex64"){const a=vS({inputs:{input:i},backend:n}),c=NS({inputs:{x:a},backend:n,attrs:{dtype:o}});return n.disposeIntermediateTensorInfo(a),c}if(!ba(i.dtype,o)){const a=ur({inputs:{x:i},backend:n});return{dataId:a.dataId,shape:a.shape,dtype:o}}if(o==="int32")return QX(i,n);if(o==="bool"){const a=n.makeTensorInfo([],"bool",bt("bool",1)),c={a:i,b:a},h=BC({inputs:c,backend:n});return n.disposeIntermediateTensorInfo(a),h}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${o}`)}const e7={kernelName:Sa,backendName:"webgl",kernelFunc:NS};class t7{constructor(e){this.outputShape=[],this.outputShape=Xi(e,1),this.variableNames=e.map((o,a)=>`T${a}`);const t=new Array(e.length-1);t[0]=e[0][1];for(let o=1;o<t.length;o++)t[o]=t[o-1]+e[o][1];const n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let o=1;o<t.length;o++){const a=t[o-1];n.push(`else if (yC < ${t[o]}) setOutput(getT${o}(yR, yC-${a}));`)}const s=t.length,i=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${i}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}}class n7{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=Xi(e,t);const n=this.outputShape,s=n.length,i=Rt(s),o=Mn("coords",s),a=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((L,x)=>`T${x}`);const c=new Array(e.length-1);c[0]=e[0][t];for(let L=1;L<c.length;L++)c[L]=c[L-1]+e[L][t];const h=a[t],d=a.slice(-2),m=a.join();let f=`if (${h} < ${c[0]}) {
return getChannel(
getT0(${m}), vec2(${d.join()}));
}`;for(let L=1;L<c.length;L++){const x=c[L-1];f+=`
if (${h} < ${c[L]} && ${h} >= ${c[L-1]}) {
return getChannel(
getT${L}(${Um(a,h,x)}),
vec2(${Um(d,h,x)}));
}`}const b=c.length,w=c[c.length-1];f+=`
return getChannel(
getT${b}(${Um(a,h,w)}),
vec2(${Um(d,h,w)}));`,this.userCode=`
float getValue(${a.map(L=>"int "+L)}) {
${f}
}
void main() {
${i} coords = getOutputCoords();
vec4 result = vec4(getValue(${o}), 0., 0., 0.);
${o[s-1]} = ${o[s-1]} + 1;
if (${o[s-1]} < ${n[s-1]}) {
result.g = getValue(${o});
}
${o[s-2]} = ${o[s-2]} + 1;
if (${o[s-2]} < ${n[s-2]}) {
result.a = getValue(${o});
}
${o[s-1]} = ${o[s-1]} - 1;
if (${o[s-2]} < ${n[s-2]} &&
${o[s-1]} < ${n[s-1]}) {
result.b = getValue(${o});
}
setOutput(result);
}
`}}function Um(e,t,n){const s=e.indexOf(t),i=e.map((o,a)=>a===s?`${o} - ${n}`:o);return i.join()}function MC(e){const{inputs:t,backend:n}=e,{input:s}=t,i=n.texData.get(s.dataId);return ur({inputs:{x:i.complexTensorInfos.imag},backend:n})}const s7={kernelName:gd,backendName:"webgl",kernelFunc:MC};function i7(e,t,n){const s=[mc(e.shape),...fc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[mc(t),...fc(t)],a=new bC(o,s),c=!0,h=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:h.dataId,shape:t,dtype:h.dtype}}function dr(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=P(i.shape),h=Vt(o,c),d=P(h);A(c===d,()=>`The new shape (${h}) has ${d} elements and the old shape (${i.shape}) has ${c} elements. The new shape and old shape must have the same number of elements.`);const m=a.texData.get(i.dataId);return m.isPacked&&!Rm(i.shape,h)&&!(m.texture!==null&&Rm(m.shape,h))?i7(i,h,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:h,dtype:i.dtype})}const r7={kernelName:_l,backendName:"webgl",kernelFunc:dr};function Ic(e,t,n){const s=e[0].dtype;if(s==="complex64"){const d=e.map(L=>vS({inputs:{input:L},backend:n})),m=e.map(L=>MC({inputs:{input:L},backend:n})),f=Ic(d,t,n),b=Ic(m,t,n),w=Lc({inputs:{real:f,imag:b},backend:n});return d.forEach(L=>n.disposeIntermediateTensorInfo(L)),m.forEach(L=>n.disposeIntermediateTensorInfo(L)),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),w}if(e.length>oe().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const d=Math.floor(e.length/2),m=Ic(e.slice(0,d),t,n),f=Ic(e.slice(d),t,n),b=Ic([m,f],t,n);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),b}if(oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){const d=new n7(e.map(m=>m.shape),t);return n.runWebGLProgram(d,e,s)}const i=Xi(e.map(d=>d.shape),t),o=e.map(d=>dr({inputs:{x:d},attrs:{shape:[-1,P(d.shape.slice(t))]},backend:n})),a=new t7(o.map(d=>d.shape)),c=n.runWebGLProgram(a,o,s);o.forEach(d=>n.disposeIntermediateTensorInfo(d));const h=dr({inputs:{x:c},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(c),h}function o7(e){const{inputs:t,backend:n,attrs:s}=e,{axis:i}=s,o=qe(i,t[0].shape)[0],a=Xi(t.map(d=>d.shape),o);if(P(a)===0)return n.makeTensorInfo(a,t[0].dtype,[]);const c=t.filter(d=>P(d.shape)>0);if(c.length===1)return c[0];const h=c.map(d=>d.shape);return np(h,o),Ic(c,o,n)}const a7={kernelName:fl,backendName:"webgl",kernelFunc:o7};const c7=$C+`
return cos(x);
`,l7=$m(c7),h7={kernelName:Ia,backendName:"webgl",kernelFunc:l7};const u7=`
if (a == b) {
return 1.0;
};
return a / b;`,d7=`
// 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;
`,p7=Sc({opSnippet:u7,packedOpSnippet:d7,checkOutOfBounds:!0}),m7={kernelName:xa,backendName:"webgl",kernelFunc:p7};class PC{constructor(e,t,n){this.variableNames=["real","imag"];const s=t[1];this.outputShape=t;const i=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,o=n?`${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 = ${i};
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) / ${o};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}}function zC(e,t,n){const s=n.texData.get(e.dataId),i=P(e.shape),o=e.shape[e.shape.length-1],a=i/o,c=dr({inputs:{x:e},backend:n,attrs:{shape:[a,o]}}),h=c.shape,d=new PC("real",h,t),m=new PC("imag",h,t),f=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:h},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:h}],b=n.runWebGLProgram(d,f,"float32"),w=n.runWebGLProgram(m,f,"float32"),L=Lc({inputs:{real:b,imag:w},backend:n});n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(w);const x=dr({inputs:{x:L},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(x),x}function f7(e){const{inputs:t,backend:n}=e,{input:s}=t;return zC(s,!1,n)}const g7={kernelName:pd,backendName:"webgl",kernelFunc:f7};class y7{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;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}}const b7={kernelName:md,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,s=t,i=new y7(n.shape),o=s.runWebGLProgram(i,[n],n.dtype);return o}};class w7{constructor(e){this.variableNames=["A"];const t=Pn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}}class L7{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=Pn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}.0, ${n}.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 S7={kernelName:Rd,backendName:"webgl",kernelFunc:I7};let xc;function I7(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:i}=t;const{numChannels:o}=s,a=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,c=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[h,d]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[d,h],f=[d,h,o];(c||a)&&(xc==null&&(xc=document.createElement("canvas").getContext("2d")),xc.canvas.width=h,xc.canvas.height=d,xc.drawImage(i,0,0,h,d),i=xc.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=Ns.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=oe().getBool("WEBGL_PACK")?new L7(f):new w7(f),L=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),L}function x7(e){const{inputs:t,backend:n}=e,{input:s}=t;return zC(s,!0,n)}const T7={kernelName:fd,backendName:"webgl",kernelFunc:x7};class VC{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:s,inSize:i,outSize:o}=e;this.outputShape=[s,o];const a=Math.floor(n/4)*4,c=n%4;let h="sumValue += dot(values, ones);";if(t!=null){const m=1/t;h=`sumValue += dot(values * ${Le(m)?m.toPrecision(2):m}, ones);`}let d="";i%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${i}) {
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 * ${n};
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)
);
${h}
}
int inIdx = inOffset + ${a};
if (${c===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${h}
} else if (${c===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${h}
} else if (${c===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${h}
}
setOutput(sumValue);
}
`}}function A7(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const n=t.length?t[t.length-1].outSize:e[1],s=uh(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function GC(e,t,n,s){const i=A7(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:h,outSize:d}=i[a];let m,f;n==="mean"?m=a===0?new VC({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d},c):new VC({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d}):m=new yC({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d},n),f=o,o=s.runWebGLProgram(m,[o],t),f.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(f)}return o}function v7(e,t,n,s){const i=P(t),o=P(e.shape),a=o/i,c=dr({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),h=GC(c,e.dtype,"max",s),d=dr({inputs:{x:h},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(h),d}class N7{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[t[o]];this.outputShape=n,this.rank=n.length;const s=Rt(this.rank),i=C7(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${i}));
}
`}}function C7(e){const t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let i=0;i<e.length;i++)s[e[i]]=n[i];return s.join()}class R7{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const n=new Array(e.length);for(let d=0;d<n.length;d++)n[d]=e[t[d]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const s=Rt(this.rank),i=J0("rc",this.rank),o=new Array(this.rank);for(let d=0;d<t.length;d++)o[t[d]]=i[d];const a=`vec2(${o.slice(-2).join()})`,c=`++${i[this.rank-1]} < ${n[this.rank-1]}`,h=`getChannel(getA(${o.join()}), ${a})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${h};
if(${c}) {
result[1] = ${h};
}
--${i[this.rank-1]};
if(++${i[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${h};
if(${c}) {
result[3] = ${h};
}
}
setOutput(result);
}
`}}function CS(e,t,n){const s=oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new R7(e.shape,t):new N7(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}const O7={kernelName:Rl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n,c=s.shape.length,h=qe(i,s.shape);let d=h;const m=Xn(d,c),f=m!=null,b=a.shouldExecuteOnCPU([s]);let w=s;if(f){if(b){const O=a.texData.get(w.dataId),E=O.values,k=new Array(c);for(let $=0;$<k.length;$++)k[$]=s.shape[m[$]];const F=SS(E,s.shape,s.dtype,m,k);w=a.makeTensorInfo(k,s.dtype);const U=a.texData.get(w.dataId);U.values=F}else w=CS(s,m,a);d=as(d.length,c)}Kn("max",d,c);const[L,x]=An(w.shape,d);let v=L;o&&(v=vn(L,h));let N;if(b){const O=a.texData.get(w.dataId),E=O.values,k=XK(E,P(x),v,s.dtype);N=a.makeTensorInfo(v,s.dtype);const F=a.texData.get(N.dataId);F.values=k}else N=v7(w,x,v,a);return f&&a.disposeIntermediateTensorInfo(w),N}};function E7(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;du(i,"maxPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;A(cn(a,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Un(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ae(m.inShape,m.outShape))return ur({inputs:{x:i},backend:n});const f=new mu(m,"max",!1);return n.runWebGLProgram(f,[i],i.dtype)}const D7={kernelName:Ol,backendName:"webgl",kernelFunc:E7};function k7(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o,output:a}=t,c=o;du([o,a],"maxPoolBackprop");const{filterSize:h,strides:d,pad:m,dimRoundingMode:f}=s,b=Un(c.shape,h,d,1,m,f),w=!0,L=new mu(b,"max",w),x=n.runWebGLProgram(L,[c],c.dtype),v=new u6(b),N=n.runWebGLProgram(v,[i,x],c.dtype);return n.disposeIntermediateTensorInfo(x),N}const F7={kernelName:bd,backendName:"webgl",kernelFunc:k7};function _7(e,t,n,s){let i=new mu(n,"max",!1);const o=s.runWebGLProgram(i,[e],"float32");i=new mu(n,"max",!0,!0,t);const a=s.runWebGLProgram(i,[e],"float32");return[o,a]}const W7={kernelName:wd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,h=n;A(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);const d=[1,1];A(cn(o,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);const m=Un(s.shape,i,o,d,a),[f,b]=_7(s,c,m,h);return[f,b]}};function $7(e,t,n,s){const i=P(t),o=P(e.shape),a=o/i,c=dr({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),h=GC(c,"float32","mean",s),d=dr({inputs:{x:h},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(h),d}const U7={kernelName:hy,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{keepDims:i,axis:o}=t,a=n,c=s.shape.length,h=qe(o,s.shape);let d=h;const m=Xn(d,c),f=m!=null,b=a.shouldExecuteOnCPU([s]),w=[];let L=s;if(f){if(b){const E=a.texData.get(L.dataId),k=E.values,F=new Array(c);for(let Y=0;Y<F.length;Y++)F[Y]=s.shape[m[Y]];const U=SS(k,s.shape,s.dtype,m,F);L=a.makeTensorInfo(F,s.dtype);const $=a.texData.get(L.dataId);$.values=U}else L=CS(s,m,a);w.push(L),d=as(d.length,c)}Kn("sum",d,c);const[x,v]=An(L.shape,d);let N=x;i&&(N=vn(x,h));const O=$7(L,v,N,a);for(const E of w)a.disposeIntermediateTensorInfo(E);return O}};class B7{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((d,m)=>d[0]+e[m]+d[1]);const s=e.length,i=Rt(s),o=t.map(d=>d[0]).join(","),a=t.map((d,m)=>d[0]+e[m]).join(","),c=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),h=n==="reflect"?0:1;if(s===1){this.userCode=`
int start = ${o};
int end = ${a};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${h};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${h};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${i} start = ${i}(${o});
${i} end = ${i}(${a});
void main() {
${i} outC = getOutputCoords();
for (int i = 0; i < ${s}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${h};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${h};
}
}
${i} coords = outC - start;
setOutput(getX(${c}));
}
`}}class M7{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((w,L)=>w[0]+e[L]+w[1]);const s=e.length,i=Rt(s),o=t.map(w=>w[0]).join(","),a=t.map((w,L)=>w[0]+e[L]).join(","),c=Mn("rc",s),h=Mn("source",s),d=`${c[s-1]} < ${this.outputShape[s-1]}`,m=s===1?"source":`vec2(${h.slice(-2).join()})`,f=n==="reflect"?0:1;let b="";if(s===1){const w=`
${i} source = rc;
if (source < start) {
source = start * 2 - source - ${f};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${f};
}
source -= start;
`;b=`
${i} rc = outputLoc;
${w}
result[0] = getChannel(getX(${h.join()}), ${m});
${c[s-1]} += 1;
if(${d}) {
${w}
result[1] = getChannel(getX(${h.join()}), ${m});
}
`}else{const w=`
${i} source = rc;
${i} lt = ${i}(lessThan(source, start));
${i} gte = ${i}(greaterThanEqual(source, end));
${i} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${f}) +
gte * ((end - 1) * 2 - source + ${f});
source -= start;
`;b=`
${i} rc = outputLoc;
${w}
result[0] = getChannel(getX(${h.join()}), ${m});
${c[s-1]} += 1;
if(${d}) {
${w}
result[1] = getChannel(getX(${h.join()}), ${m});
}
rc = outputLoc;
${c[s-2]} += 1;
if(${c[s-2]} < ${this.outputShape[s-2]}) {
${w}
result[2] = getChannel(getX(${h.join()}), ${m});
${c[s-1]} += 1;
if(${d}) {
${w}
result[3] = getChannel(getX(${h.join()}), ${m});
}
}
`}this.userCode=`
const ${i} start = ${i}(${o});
const ${i} end = ${i}(${a});
void main() {
${i} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${b}
setOutput(result);
}
`}}const P7=({inputs:e,backend:t,attrs:n})=>{const{x:s}=e,{paddings:i,mode:o}=n,a=oe().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new M7(s.shape,i,o):new B7(s.shape,i,o),c=t.runWebGLProgram(a,[s],s.dtype);return c},z7={kernelName:El,backendName:"webgl",kernelFunc:P7};const YC={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class HC{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=nt(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}}const qC="return a * b;";function V7(e){const{inputs:t,backend:n}=e,{a:s,b:i}=t,o=$n(s.dtype,i.dtype);if(s.dtype==="complex64"){const c=n.texData.get(s.dataId),h=n.texData.get(i.dataId),d=new HC(YC.REAL,s.shape,i.shape),m=new HC(YC.IMAG,s.shape,i.shape),f=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:s.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape}],b=n.runWebGLProgram(d,f,"float32"),w=n.runWebGLProgram(m,f,"float32"),L=Lc({inputs:{real:b,imag:w},backend:n});return n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(w),L}if(n.shouldExecuteOnCPU([s,i])){const c=n.texData.get(s.dataId),h=n.texData.get(i.dataId),[d,m]=JK(s.shape,i.shape,c.values,h.values,o),f=n.makeTensorInfo(m,o),b=n.texData.get(f.dataId);return b.values=d,f}let a;return oe().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new lr(qC,s.shape,i.shape):a=new _n(qC,s.shape,i.shape),n.runWebGLProgram(a,[s,i],o)}const G7={kernelName:Ta,backendName:"webgl",kernelFunc:V7};const Y7={kernelName:fy,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Za("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=n,h=t,d=h.readSync(s.dataId),m=h.readSync(i.dataId),f=o,b=a,w=c;return Dp(d,m,f,b,w)}};const H7=kp,q7={kernelName:Ld,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Za("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,padToMaxOutputSize:h}=n,d=t,m=d.readSync(s.dataId),f=d.readSync(i.dataId),{selectedIndices:b,validOutputs:w}=H7(m,f,o,a,c,h);return[b,w]}};const j7=Fp,K7={kernelName:Sd,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Za("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}=n,d=t,m=d.readSync(s.dataId),f=d.readSync(i.dataId),b=o,w=a,L=c,x=h,{selectedIndices:v,selectedScores:N}=j7(m,f,b,w,L,x);return[v,N]}};class X7{constructor(e,t,n,s){this.variableNames=["Image"],this.outputShape=[];const i=e[1],o=e[2],a=Math.sin(t).toFixed(3),c=Math.cos(t).toFixed(3);this.outputShape=e;const[h,d]=Jb(s,i,o),m=h.toFixed(3),f=d.toFixed(3);let b="";typeof n=="number"?b=`float outputValue = ${n.toFixed(2)};`:b=`
vec3 fill = vec3(${n.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) - ${m}) * ${c} - (float(y) - ${f}) * ${a};
float coordYFloat = (float(x) - ${m}) * ${a} + (float(y) - ${f}) * ${c};
int coordX = int(round(coordXFloat + ${m}));
int coordY = int(round(coordYFloat + ${f}));
${b}
if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${i}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
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return sin(x);
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Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(u)}`);const[g,I,S,T]=y?[u.x,u.y,u.width,u.height]:[u.left,u.top,u.right-u.left,u.bottom-u.top];_t.assertIsValidBox({x:g,y:I,width:S,height:T},"Box.constructor",l),this._x=g,this._y=I,this._width=S,this._height=T}get x(){return this._x}get y(){return this._y}get width(){return this._width}get height(){return this._height}get left(){return this.x}get top(){return this.y}get right(){return this.x+this.width}get bottom(){return this.y+this.height}get area(){return this.width*this.height}get topLeft(){return new Qe(this.left,this.top)}get topRight(){return new Qe(this.right,this.top)}get bottomLeft(){return new Qe(this.left,this.bottom)}get bottomRight(){return new Qe(this.right,this.bottom)}round(){const[r,l,u,p]=[this.x,this.y,this.width,this.height].map(y=>Math.round(y));return new _t({x:r,y:l,width:u,height:p})}floor(){const[r,l,u,p]=[this.x,this.y,this.width,this.height].map(y=>Math.floor(y));return new _t({x:r,y:l,width:u,height:p})}toSquare(){let{x:r,y:l,width:u,height:p}=this;const y=Math.abs(u-p);return u<p&&(r-=y/2,u+=y),p<u&&(l-=y/2,p+=y),new _t({x:r,y:l,width:u,height:p})}rescale(r){const l=Gm(r)?r.width:r,u=Gm(r)?r.height:r;return new _t({x:this.x*l,y:this.y*u,width:this.width*l,height:this.height*u})}pad(r,l){let[u,p,y,g]=[this.x-r/2,this.y-l/2,this.width+r,this.height+l];return new _t({x:u,y:p,width:y,height:g})}clipAtImageBorders(r,l){const{x:u,y:p,right:y,bottom:g}=this,I=Math.max(u,0),S=Math.max(p,0),T=y-I,C=g-S,D=Math.min(T,r-I),_=Math.min(C,l-S);return new _t({x:I,y:S,width:D,height:_}).floor()}shift(r,l){const{width:u,height:p}=this,y=this.x+r,g=this.y+l;return new _t({x:y,y:g,width:u,height:p})}padAtBorders(r,l){const u=this.width+1,p=this.height+1;let y=1,g=1,I=u,S=p,T=this.left,C=this.top,D=this.right,_=this.bottom;return D>l&&(I=-D+l+u,D=l),_>r&&(S=-_+r+p,_=r),T<1&&(S=2-T,T=1),C<1&&(S=2-C,C=1),{dy:g,edy:S,dx:y,edx:I,y:C,ey:_,x:T,ex:D,w:u,h:p}}calibrate(r){return new _t({left:this.left+r.left*this.width,top:this.top+r.top*this.height,right:this.right+r.right*this.width,bottom:this.bottom+r.bottom*this.height}).toSquare().round()}}class gu extends _t{constructor(r,l,u,p,y=!1){super({left:r,top:l,right:u,bottom:p},y)}}class Nc{constructor(r,l,u,p,y){this._imageDims=new ms(y.width,y.height),this._score=r,this._classScore=l,this._className=u,this._box=new _t(p).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new _t(this._box).rescale(this.imageDims.reverse())}forSize(r,l){return new Nc(this.score,this.classScore,this.className,this.relativeBox,{width:r,height:l})}}class Jt extends Nc{constructor(r,l,u){super(r,r,"",l,u)}forSize(r,l){const{score:u,relativeBox:p,imageDims:y}=super.forSize(r,l);return new Jt(u,p,y)}}function WS(r,l,u=!0){const p=Math.max(0,Math.min(r.right,l.right)-Math.max(r.left,l.left)),y=Math.max(0,Math.min(r.bottom,l.bottom)-Math.max(r.top,l.top)),g=p*y;return u?g/(r.area+l.area-g):g/Math.min(r.area,l.area)}function $S(r){const l=r.map(S=>S.x),u=r.map(S=>S.y),p=l.reduce((S,T)=>T<S?T:S,Infinity),y=u.reduce((S,T)=>T<S?T:S,Infinity),g=l.reduce((S,T)=>S<T?T:S,0),I=u.reduce((S,T)=>S<T?T:S,0);return new gu(p,y,g,I)}function US(r,l,u,p=!0){let y=l.map((I,S)=>({score:I,boxIndex:S})).sort((I,S)=>I.score-S.score).map(I=>I.boxIndex);const g=[];for(;y.length>0;){const I=y.pop();g.push(I);const S=y,T=[];for(let C=0;C<S.length;C++){const D=S[C],_=r[I],A=r[D];T.push(WS(_,A,p))}y=y.filter((C,D)=>T[D]<=u)}return g}const Wi=Je(Ze());function di(r,l){return Wi.tidy(()=>{const[u,p,y]=l,g=Wi.fill([...r.shape.slice(0,3),1],u,"float32"),I=Wi.fill([...r.shape.slice(0,3),1],p,"float32"),S=Wi.fill([...r.shape.slice(0,3),1],y,"float32"),T=Wi.concat([g,I,S],3);return Wi.sub(r,T)})}const so=Je(Ze());function BS(r,l=!1){return so.tidy(()=>{const[u,p]=r.shape.slice(1);if(u===p)return r;const y=Math.abs(u-p),g=Math.round(y*(l?.5:1)),I=u>p?2:1,S=A=>{const B=r.shape.slice();return B[I]=A,so.fill(B,0,"float32")},T=S(g),C=y-T.shape[I],D=l&&C?S(C):null,_=[D,r,T].filter(A=>!!A).map(A=>so.cast(A,"float32"));return so.concat(_,I)})}function vJ(r){const l=r.slice();for(let u=l.length-1;u>0;u--){const p=Math.floor(Math.random()*(u+1)),y=l[u];l[u]=l[p],l[p]=y}return l}function yu(r){return 1/(1+Math.exp(-r))}function NJ(r){return Math.log(r/(1-r))}class bu extends _t{constructor(r,l,u,p,y=!1){super({x:r,y:l,width:u,height:p},y)}}const CJ=.5,RJ=.43,OJ=.45;class Gs{constructor(r,l,u=new Qe(0,0)){const{width:p,height:y}=l;this._imgDims=new ms(p,y),this._shift=u,this._positions=r.map(g=>g.mul(new Qe(p,y)).add(u))}get shift(){return new Qe(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(r=>r.sub(this._shift).div(new Qe(this.imageWidth,this.imageHeight)))}forSize(r,l){return new this.constructor(this.relativePositions,{width:r,height:l})}shiftBy(r,l){return new this.constructor(this.relativePositions,this._imgDims,new Qe(r,l))}shiftByPoint(r){return this.shiftBy(r.x,r.y)}align(r,l={}){if(r){const y=r instanceof Jt?r.box.floor():new _t(r);return this.shiftBy(y.x,y.y).align(null,l)}const{useDlibAlignment:u,minBoxPadding:p}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},l);return u?this.alignDlib():this.alignMinBbox(p)}alignDlib(){const r=this.getRefPointsForAlignment(),[l,u,p]=r,y=D=>p.sub(D).magnitude(),g=(y(l)+y(u))/2,I=Math.floor(g/OJ),S=Xo(r),T=Math.floor(Math.max(0,S.x-CJ*I)),C=Math.floor(Math.max(0,S.y-RJ*I));return new bu(T,C,Math.min(I,this.imageWidth+T),Math.min(I,this.imageHeight+C))}alignMinBbox(r){const l=$S(this.positions);return l.pad(l.width*r,l.height*r)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class EJ extends Gs{getRefPointsForAlignment(){const r=this.positions;return[r[0],r[1],Xo([r[3],r[4]])]}}class wu extends Gs{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Xo)}}class Ym{constructor(r,l){this._label=r,this._distance=l}get label(){return this._label}get distance(){return this._distance}toString(r=!0){return`${this.label}${r?` (${Ko(this.distance)})`:""}`}}class Hm extends _t{static assertIsValidLabeledBox(r,l){if(_t.assertIsValidBox(r,l),!ui(r.label))throw new Error(`${l} - expected property label (${r.label}) to be a number`)}constructor(r,l){super(r);this._label=l}get label(){return this._label}}class Jo{constructor(r,l){if(!(typeof r=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(l)||l.some(u=>!(u instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=r,this._descriptors=l}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(r=>Array.from(r))}}static fromJSON(r){const l=r.descriptors.map(u=>new Float32Array(u));return new Jo(r.label,l)}}class DJ extends Hm{static assertIsValidPredictedBox(r,l){if(Hm.assertIsValidLabeledBox(r,l),!vc(r.score)||!vc(r.classScore))throw new Error(`${l} - expected properties score (${r.score}) and (${r.classScore}) to be a number between [0, 1]`)}constructor(r,l,u,p){super(r,l);this._score=u,this._classScore=p}get score(){return this._score}get classScore(){return this._classScore}}function $i(r){return r.detection instanceof Jt}function Zo(r,l){const u={detection:l};return Object.assign({},r,u)}function MS(){const r=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},l=function(){throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:r,readFile:l}}function qm(r){let l="";if(!r)try{r=require("fs")}catch(p){l=p.toString()}const u=r?function(p){return new Promise((y,g)=>{r.readFile(p,function(I,S){return I?g(I):y(S)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${l}`)};return{readFile:u}}function PS(){const r=global.Canvas||global.HTMLCanvasElement,l=global.Image||global.HTMLImageElement,u=function(){if(r)return new r;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},p=function(){if(l)return new l;throw new Error("createImageElement - missing Image implementation for nodejs environment")},y=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},g=qm();return{Canvas:r||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:l||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:u,createImageElement:p,fetch:y,...g}}function zS(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}const VS=Je(t2());let In;function kJ(){if(!In)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return In}function GS(r){In=r}function YS(){if(zS())return GS(MS());if(VS.isNodejs())return GS(PS())}function FJ(r){if(In||YS(),!In)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");const{Canvas:l=In.Canvas,Image:u=In.Image}=r;In.Canvas=l,In.Image=u,In.createCanvasElement=r.createCanvasElement||(()=>new l),In.createImageElement=r.createImageElement||(()=>new u),In.ImageData=r.ImageData||In.ImageData,In.Video=r.Video||In.Video,In.fetch=r.fetch||In.fetch,In.readFile=r.readFile||In.readFile}const St={getEnv:kJ,setEnv:GS,initialize:YS,createBrowserEnv:MS,createFileSystem:qm,createNodejsEnv:PS,monkeyPatch:FJ,isBrowser:zS,isNodejs:VS.isNodejs};YS();function Qo(r){return!St.isNodejs()&&typeof r=="string"?document.getElementById(r):r}function is(r){const{Canvas:l,CanvasRenderingContext2D:u}=St.getEnv();if(r instanceof u)return r;const p=Qo(r);if(!(p instanceof l))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const y=p.getContext("2d");if(!y)throw new Error("resolveContext2d - canvas 2d context is null");return y}var Ui;(function(r){r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Ui||(Ui={}));class jm{constructor(r={}){const{anchorPosition:l,backgroundColor:u,fontColor:p,fontSize:y,fontStyle:g,padding:I}=r;this.anchorPosition=l||Ui.TOP_LEFT,this.backgroundColor=u||"rgba(0, 0, 0, 0.5)",this.fontColor=p||"rgba(255, 255, 255, 1)",this.fontSize=y||14,this.fontStyle=g||"Georgia",this.padding=I||4}}class Cc{constructor(r,l,u={}){this.text=typeof r=="string"?[r]:r instanceof Cc?r.text:r,this.anchor=l,this.options=new jm(u)}measureWidth(r){const{padding:l}=this.options;return this.text.map(u=>r.measureText(u).width).reduce((u,p)=>u<p?p:u,0)+2*l}measureHeight(){const{fontSize:r,padding:l}=this.options;return this.text.length*r+2*l}getUpperLeft(r,l){const{anchorPosition:u}=this.options,p=u===Ui.BOTTOM_RIGHT||u===Ui.TOP_RIGHT,y=u===Ui.BOTTOM_LEFT||u===Ui.BOTTOM_RIGHT,g=this.measureWidth(r),I=this.measureHeight(),S=p?this.anchor.x-g:this.anchor.x,T=y?this.anchor.y-I:this.anchor.y;if(l){const{width:C,height:D}=l,_=Math.max(Math.min(S,C-g),0),A=Math.max(Math.min(T,D-I),0);return{x:_,y:A}}return{x:S,y:T}}draw(r){const l=Qo(r),u=is(l),{backgroundColor:p,fontColor:y,fontSize:g,fontStyle:I,padding:S}=this.options;u.font=`${g}px ${I}`;const T=this.measureWidth(u),C=this.measureHeight();u.fillStyle=p;const D=this.getUpperLeft(u,l);u.fillRect(D.x,D.y,T,C),u.fillStyle=y,this.text.forEach((_,A)=>{const B=S+D.x,ne=S+D.y+(A+1)*g;u.fillText(_,B,ne)})}}class s2{constructor(r={}){const{boxColor:l,lineWidth:u,label:p,drawLabelOptions:y}=r;this.boxColor=l||"rgba(0, 0, 255, 1)",this.lineWidth=u||2,this.label=p;const g={anchorPosition:Ui.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new jm(Object.assign({},g,y))}}class HS{constructor(r,l={}){this.box=new _t(r),this.options=new s2(l)}draw(r){const l=is(r),{boxColor:u,lineWidth:p}=this.options,{x:y,y:g,width:I,height:S}=this.box;l.strokeStyle=u,l.lineWidth=p,l.strokeRect(y,g,I,S);const{label:T}=this.options;T&&new Cc([T],{x:y-p/2,y:g},this.options.drawLabelOptions).draw(r)}}function _J(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const y=p instanceof Jt?p.score:$i(p)?p.detection.score:void 0,g=p instanceof Jt?p.box:$i(p)?p.detection.box:new _t(p),I=y?`${Ko(y)}`:void 0;new HS(g,{label:I}).draw(r)})}function Lu(r){const{Image:l,Video:u}=St.getEnv();return r instanceof l&&r.complete||r instanceof u&&r.readyState>=3}function qS(r){return new Promise((l,u)=>{if(r instanceof St.getEnv().Canvas||Lu(r))return l(null);function p(g){if(!g.currentTarget)return;g.currentTarget.removeEventListener("load",p),g.currentTarget.removeEventListener("error",y),l(g)}function y(g){if(!g.currentTarget)return;g.currentTarget.removeEventListener("load",p),g.currentTarget.removeEventListener("error",y),u(g)}r.addEventListener("load",p),r.addEventListener("error",y)})}function jS(r){return new Promise((l,u)=>{if(!(r instanceof Blob))return u("bufferToImage - expected buf to be of type: Blob");const p=new FileReader;p.onload=()=>{if(typeof p.result!="string")return u("bufferToImage - expected reader.result to be a string, in onload");const y=St.getEnv().createImageElement();y.onload=()=>l(y),y.onerror=u,y.src=p.result},p.onerror=u,p.readAsDataURL(r)})}function ea(r){const{Image:l,Video:u}=St.getEnv();return r instanceof l?new ms(r.naturalWidth,r.naturalHeight):r instanceof u?new ms(r.videoWidth,r.videoHeight):new ms(r.width,r.height)}function Rc({width:r,height:l}){const{createCanvasElement:u}=St.getEnv(),p=u();return p.width=r,p.height=l,p}function Su(r,l){const{ImageData:u}=St.getEnv();if(!(r instanceof u)&&!Lu(r))throw new Error("createCanvasFromMedia - media has not finished loading yet");const{width:p,height:y}=l||ea(r),g=Rc({width:p,height:y});return r instanceof u?is(g).putImageData(r,0,0):is(g).drawImage(r,0,0,p,y),g}const Km=Je(Ze());async function KS(r,l){const u=l||St.getEnv().createCanvasElement(),[p,y,g]=r.shape.slice(Rs(r)?1:0),I=Km.tidy(()=>r.as3D(p,y,g).toInt());return await Km.browser.toPixels(I,u),I.dispose(),u}function Xm(r){const{Image:l,Canvas:u,Video:p}=St.getEnv();return r instanceof l||r instanceof u||r instanceof p}const WJ=1e-7,$J=1e-4;class i2{time(r){return se("time")}read(r){return se("read")}readSync(r){return se("readSync")}numDataIds(){return se("numDataIds")}disposeData(r){return se("disposeData")}write(r,l,u){return se("write")}move(r,l,u,p){return se("move")}memory(){return se("memory")}floatPrecision(){return se("floatPrecision")}epsilon(){return this.floatPrecision()===32?WJ:$J}batchMatMul(r,l,u,p){return se("batchMatMul")}fusedBatchMatMul({a:r,b:l,transposeA:u,transposeB:p,bias:y,activation:g,preluActivationWeights:I}){return se("fusedBatchMatMul")}slice(r,l,u){return se("slice")}stridedSlice(r,l,u,p){return se("stridedSlice")}unstack(r,l){return se("unstack")}reverse(r,l){return se("reverse")}concat(r,l){return se("concat")}neg(r){return se("neg")}add(r,l){return se("add")}addN(r){return se("addN")}subtract(r,l){return se("subtract")}multiply(r,l){return se("multiply")}realDivide(r,l){return se("realDivide")}floorDiv(r,l){return se("floorDiv")}sum(r,l){return se("sum")}prod(r,l){return se("prod")}unsortedSegmentSum(r,l,u){return se("unsortedSegmentSum")}argMin(r,l){return se("argMin")}argMax(r,l){return se("argMax")}equal(r,l){return se("equal")}notEqual(r,l){return se("notEqual")}less(r,l){return se("less")}lessEqual(r,l){return se("lessEqual")}greater(r,l){return se("greater")}greaterEqual(r,l){return se("greaterEqual")}logicalNot(r){return se("logicalNot")}logicalAnd(r,l){return se("logicalAnd")}logicalOr(r,l){return se("logicalOr")}where(r){return se("where")}select(r,l,u){return se("select")}topk(r,l,u){return se("topk")}min(r,l){return se("min")}minimum(r,l){return se("minimum")}mod(r,l){return se("mod")}max(r,l){return se("max")}maximum(r,l){return se("maximum")}all(r,l){return se("all")}any(r,l){return se("any")}squaredDifference(r,l){return se("squaredDifference")}ceil(r){return se("ceil")}floor(r){return se("floor")}round(r){return se("round")}sign(r){return se("sign")}isNaN(r){return se("isNaN")}isInf(r){return se("isInf")}isFinite(r){return se("isFinite")}pow(r,l){return se("pow")}exp(r){return se("exp")}expm1(r){return se("expm1")}softmax(r,l){return se("softmax")}log(r){return se("log")}log1p(r){return se("log1p")}sqrt(r){return se("sqrt")}rsqrt(r){return se("rsqrt")}square(r){return se("square")}reciprocal(r){return se("reciprocal")}relu(r){return se("relu")}relu6(r){return se("relu6")}prelu(r,l){return se("prelu")}elu(r){return 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Buffer shape=${this.shape}`;throw new Error(y)}l++}let u=r[r.length-1];for(let p=0;p<r.length-1;++p)u+=this.strides[p]*r[p];return this.values[u]}locToIndex(r){if(this.rank===0)return 0;if(this.rank===1)return r[0];let l=r[r.length-1];for(let u=0;u<r.length-1;++u)l+=this.strides[u]*r[u];return l}indexToLoc(r){if(this.rank===0)return[];if(this.rank===1)return[r];const l=new Array(this.shape.length);for(let u=0;u<l.length-1;++u)l[u]=Math.floor(r/this.strides[u]),r-=l[u]*this.strides[u];return l[l.length-1]=r,l}get rank(){return this.shape.length}toTensor(){return Bi().makeTensor(this.values,this.shape,this.dtype)}}let Bi=null,Fc=null,KJ=null;function ZR(r){Bi=r}function QR(r){Fc=r}function eO(r){KJ=r}class En{constructor(r,l,u,p){this.kept=!1,this.isDisposedInternal=!1,this.shape=r.slice(),this.dtype=l||"float32",this.size=Zt(r),this.strides=Au(r),this.dataId=u,this.id=p,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const r=await this.data();return Fc.buffer(this.shape,this.dtype,r)}bufferSync(){return Fc.buffer(this.shape,this.dtype,this.dataSync())}async array(){const r=await this.data();return JS(this.shape,r)}arraySync(){return JS(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const r=Bi().read(this.dataId);if(this.dtype==="string"){const l=await r;try{return l.map(u=>oI(u))}catch(u){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return r}dataSync(){this.throwIfDisposed();const r=Bi().readSync(this.dataId);if(this.dtype==="string")try{return r.map(l=>oI(l))}catch(l){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return r}async bytes(){this.throwIfDisposed();const r=await Bi().read(this.dataId);return this.dtype==="string"?r:new Uint8Array(r.buffer)}dispose(){if(this.isDisposed)return;Bi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(r=!1){return Fc.print(this,r)}clone(){return this.throwIfDisposed(),Fc.clone(this)}toString(r=!1){const l=this.dataSync();return KR(l,this.shape,this.dtype,r)}cast(r){return this.throwIfDisposed(),Fc.cast(this,r)}variable(r=!0,l,u){return this.throwIfDisposed(),Bi().makeVariable(this,r,l,u)}}Object.defineProperty(En,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});class eg extends En{constructor(r,l,u,p){super(r.shape,r.dtype,r.dataId,p);this.trainable=l,this.name=u}assign(r){if(r.dtype!==this.dtype)throw new Error(`dtype of the new value (${r.dtype}) and previous value (${this.dtype}) must match`);if(!Iu(r.shape,this.shape))throw new Error(`shape of the new value (${r.shape}) and previous value (${this.shape}) must match`);Bi().disposeTensor(this),this.dataId=r.dataId,Bi().incRef(this,null)}dispose(){Bi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(eg,Symbol.hasInstance,{value:r=>r instanceof En&&r.assign!=null&&r.assign instanceof Function});var tO;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(tO||(tO={}));var cI;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(cI||(cI={}));var lI;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(lI||(lI={}));var hI;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(hI||(hI={}));var uI;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(uI||(uI={}));const XJ={float32:hI,int32:cI,bool:lI,complex64:uI};function nO(r,l){if(r==="string"||l==="string"){if(r==="string"&&l==="string")return"string";throw new Error(`Can not upcast ${r} with ${l}`)}return XJ[r][l]}function Lt(r,l){if(r.dtype===l.dtype)return[r,l];const u=nO(r.dtype,l.dtype);return[r.cast(u),l.cast(u)]}function tg(r){const l=[],u=new Set;return sO(r,l,u),l}function sO(r,l,u){if(r==null)return;if(r instanceof En){l.push(r);return}if(!JJ(r))return;const p=r;for(const y in p){const g=p[y];u.has(g)||(u.add(g),sO(g,l,u))}}function JJ(r){return Array.isArray(r)||typeof r=="object"}class iO{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const r in this.registeredVariables)this.registeredVariables[r].dispose()}}class Ru{constructor(r){this.ENV=r,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new iO}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const u=r[l],p=await this.initializeBackend(u).success;if(p){await this.setBackend(u);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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:r,asyncInit:l}=this.initializeBackendsAndReturnBest();if(l)throw new Error(`The highest priority backend '${r}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(r)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(r){if(!(r in this.registry))if(r in this.registryFactory){const{asyncInit:l}=this.initializeBackend(r);if(l)return null}else return null;return this.registry[r]}findBackendFactory(r){return r in this.registryFactory?this.registryFactory[r].factory:null}registerBackend(r,l,u=1){return r in this.registryFactory?(console.warn(`${r} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[r]={factory:l,priority:u},!0)}async setBackend(r){if(this.registryFactory[r]==null)throw new Error(`Backend name '${r}' not found in registry`);if(this.backendName=r,this.registry[r]==null){this.backendInstance=null;const{success:l,asyncInit:u}=this.initializeBackend(r),p=u?await l:l;if(!p)return!1}return this.backendInstance=this.registry[r],this.setupRegisteredKernels(),this.profiler=new YR(this.backendInstance),!0}setupRegisteredKernels(){const r=iI(this.backendName);r.forEach(l=>{l.setupFunc!=null&&l.setupFunc(this.backendInstance)})}disposeRegisteredKernels(r){const l=iI(r);l.forEach(u=>{u.disposeFunc!=null&&u.disposeFunc(this.registry[r])})}initializeBackend(r){const l=this.registryFactory[r];if(l==null)throw new Error(`Cannot initialize backend ${r}, no registration found.`);try{const u=l.factory();if(u&&!(u instanceof i2)&&typeof u.then=="function"){const p=++this.pendingBackendInitId,y=u.then(g=>p<this.pendingBackendInitId?!1:(this.registry[r]=g,this.pendingBackendInit=null,!0)).catch(g=>(p<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${r} failed`),console.warn(g.stack||g.message)),!1));return this.pendingBackendInit=y,{success:y,asyncInit:!0}}else return this.registry[r]=u,{success:!0,asyncInit:!1}}catch(u){return console.warn(`Initialization of backend ${r} failed`),console.warn(u.stack||u.message),{success:!1,asyncInit:!1}}}removeBackend(r){if(!(r in this.registryFactory))throw new Error(`${r} backend not found in registry`);this.backendName===r&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,r in this.registry&&(this.disposeRegisteredKernels(r),this.registry[r].dispose(),delete this.registry[r]),delete this.registryFactory[r],this.backendName===r&&(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((r,l)=>this.registryFactory[l].priority-this.registryFactory[r].priority)}initializeBackendsAndReturnBest(){const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const u=r[l],{success:p,asyncInit:y}=this.initializeBackend(u);if(y||p)return{name:u,asyncInit:y}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(r,l){const u=this.state.tensorInfo.get(l),p=u.backend,y=this.readSync(l);p.disposeData(l),u.backend=r,r.move(l,y,u.shape,u.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(r,l){let u=null;if(l==null){if(typeof r!="function")throw new Error("Please provide a function to tidy()");l=r}else{if(typeof r!="string"&&!(r instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof l!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");u=r}let p;return this.scopedRun(()=>this.startScope(u),()=>this.endScope(p),()=>(p=l(),p instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),p))}scopedRun(r,l,u){r();try{const p=u();return l(),p}catch(p){throw l(),p}}nextTensorId(){return Ru.nextTensorId++}nextVariableId(){return Ru.nextVariableId++}clone(r){const l=this.makeTensorFromDataId(r.dataId,r.shape,r.dtype),u={x:r},p=g=>({x:()=>{const I="float32",S={x:g},T={dtype:I};return H.runKernelFunc(C=>C.cast(g,I),S,null,kc,T)}}),y=[];return this.addTapeNode(this.state.activeScope.name,u,[l],p,y,{}),l}runKernel(r,l,u,p,y){const g=null,I=null;return this.runKernelFunc(g,l,I,r,u,p,y)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(r,l,u){const p=this.backend.numDataIds();let y=0;u.forEach(S=>{y+=S.dtype==="complex64"?3:1});const g=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],I=p-l-y-g;if(I>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${I} data ids) after running '${r}'`)}runKernelFunc(r,l,u,p,y,g,I){let S,T=[];const C=this.isTapeOn();p==null&&(p=this.state.activeScope!=null?this.state.activeScope.name:"");const D=this.state.numBytes,_=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let A;const B=Jf(p,this.backendName);let ne;if(B!=null)A=()=>{const P=this.backend.numDataIds();ne=B.kernelFunc({inputs:l,attrs:y,backend:this.backend});const ge=Array.isArray(ne)?ne:[ne];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,P,ge);const ae=ge.map(({dataId:Le,shape:ve,dtype:Ve})=>this.makeTensorFromDataId(Le,ve,Ve));if(C){let Le=this.getTensorsForGradient(p,l,ae);if(Le==null){I==null&&(I=[]);const ve=ae.filter((Ve,at)=>I[at]);Le=(g||[]).slice().concat(ve)}T=this.saveTensorsForBackwardMode(Le)}return ae};else{const P=ge=>{if(!C)return;T=ge.map(ae=>this.keep(this.clone(ae)))};A=()=>{const ge=this.backend.numDataIds();ne=this.tidy(()=>r(this.backend,P));const ae=Array.isArray(ne)?ne:[ne];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,ge,ae),ae}}let te;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?S=A():(te=this.profiler.profileKernel(p,l,()=>A()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(te),S=te.outputs)}),C&&this.addTapeNode(p,l,S,u,T,y),this.state.profiling&&this.state.activeProfile.kernels.push({name:p,bytesAdded:this.state.numBytes-D,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-_,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(P=>l[P]!=null?l[P].shape:null),outputShapes:S.map(P=>P.shape),kernelTimeMs:te.timeMs,extraInfo:te.extraInfo}),Array.isArray(ne)?S:S[0]}saveTensorsForBackwardMode(r){const l=r.map(u=>this.keep(this.clone(u)));return l}getTensorsForGradient(r,l,u){const p=sI(r);if(p!=null){const y=p.inputsToSave||[],g=p.outputsToSave||[];let I;p.saveAllInputs?(J(Array.isArray(l),()=>"saveAllInputs is true, expected inputs to be an array."),I=Object.keys(l).map(T=>l[T])):I=y.map(T=>l[T]);const S=u.filter((T,C)=>g[C]);return I.concat(S)}return null}makeTensor(r,l,u,p){if(r==null)throw new Error("Values passed to engine.makeTensor() are null");u=u||"float32",p=p||this.backend;let y=r;u==="string"&&xu(r[0])&&(y=r.map(S=>GR(S)));const g=p.write(y,l,u),I=new En(l,u,g,this.nextTensorId());if(this.incRef(I,p),u==="string"){const S=this.state.tensorInfo.get(g),T=u2(y);this.state.numBytes+=T-S.bytes,S.bytes=T}return I}makeTensorFromDataId(r,l,u,p){u=u||"float32";const y=new En(l,u,r,this.nextTensorId());return this.incRef(y,p),y}makeVariable(r,l=!0,u,p){u=u||this.nextVariableId().toString(),p!=null&&p!==r.dtype&&(r=r.cast(p));const y=new eg(r,l,u,this.nextTensorId());if(this.state.registeredVariables[y.name]!=null)throw new Error(`Variable with name ${y.name} was already registered`);return this.state.registeredVariables[y.name]=y,this.incRef(y,this.backend),y}incRef(r,l){const u=this.state.tensorInfo.has(r.dataId)?this.state.tensorInfo.get(r.dataId).refCount:0;if(this.state.numTensors++,r.dtype==="string"&&this.state.numStringTensors++,u===0){this.state.numDataBuffers++;let p=0;r.dtype!=="complex64"&&r.dtype!=="string"&&(p=r.size*h2(r.dtype)),this.state.tensorInfo.set(r.dataId,{backend:l||this.backend,dtype:r.dtype,shape:r.shape,bytes:p,refCount:0}),this.state.numBytes+=p}this.state.tensorInfo.get(r.dataId).refCount++,r instanceof eg||this.track(r)}disposeTensor(r){if(!this.state.tensorInfo.has(r.dataId))return;this.state.numTensors--,r.dtype==="string"&&this.state.numStringTensors--;const l=this.state.tensorInfo.get(r.dataId),u=l.refCount;u<=1?(r.dtype!=="complex64"&&(this.state.numBytes-=l.bytes),this.state.numDataBuffers--,l.backend.disposeData(r.dataId),this.state.tensorInfo.delete(r.dataId)):this.state.tensorInfo.get(r.dataId).refCount--}disposeVariables(){for(const r in this.state.registeredVariables){const l=this.state.registeredVariables[r];this.disposeVariable(l)}}disposeVariable(r){this.disposeTensor(r),this.state.registeredVariables[r.name]!=null&&delete this.state.registeredVariables[r.name]}memory(){const r=this.backend.memory();return r.numTensors=this.state.numTensors,r.numDataBuffers=this.state.numDataBuffers,r.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(r.unreliable=!0,r.reasons==null&&(r.reasons=[]),r.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),r}async profile(r){this.state.profiling=!0;const l=this.state.numBytes,u=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await r(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(p=>p.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-l,this.state.activeProfile.newTensors=this.state.numTensors-u;for(const p of this.state.activeProfile.kernels)p.kernelTimeMs=await p.kernelTimeMs,p.extraInfo=await p.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(r,l,u,p,y,g){const I={id:this.state.nextTapeNodeId++,kernelName:r,inputs:l,outputs:u,saved:y},S=sI(r);S!=null&&(p=S.gradFunc),p!=null&&(I.gradient=T=>(T=T.map((C,D)=>{if(C==null){const _=u[D],A=na(_.size,_.dtype);return this.makeTensor(A,_.shape,_.dtype)}return C}),p(T.length>1?T:T[0],y,g))),this.state.activeTape.push(I)}keep(r){return r.kept=!0,r}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(r){const l={track:[],name:"unnamed scope",id:this.state.nextScopeId++};r&&(l.name=r),this.state.scopeStack.push(l),this.state.activeScope=l}endScope(r){const l=tg(r),u=new Set(l.map(y=>y.id));for(let y=0;y<this.state.activeScope.track.length;y++){const g=this.state.activeScope.track[y];!g.kept&&!u.has(g.id)&&g.dispose()}const p=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],l.forEach(y=>{!y.kept&&y.scopeId===p.id&&this.track(y)})}gradients(r,l,u,p=!1){if(J(l.length>0,()=>"gradients() received an empty list of xs."),u!=null&&u.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${u.dtype}'`);const y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",r));J(y instanceof En,()=>"The result y returned by f() must be a tensor.");const g=HR(this.state.activeTape,l,y);if(!p&&g.length===0&&l.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{const I={};I[y.id]=u==null?ZJ(y.shape):u,qR(I,g,T=>this.tidy(T),QJ);const S=l.map(T=>I[T.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(T=>{for(const C of T.saved)C.dispose()}),this.state.activeTape=null),{value:y,grads:S}})}customGrad(r){return J(XS(r),()=>"The f passed in customGrad(f) must be a function."),(...l)=>{J(l.every(y=>y instanceof En),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let u;const p={};return l.forEach((y,g)=>{p[g]=y}),this.runKernelFunc((y,g)=>(u=r(...l,g),J(u.value instanceof En,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),J(XS(u.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),u.value),p,(y,g)=>{const I=u.gradFunc(y,g),S=Array.isArray(I)?I:[I];J(S.length===l.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),J(S.every(C=>C instanceof En),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");const T={};return S.forEach((C,D)=>{T[D]=()=>C}),T})}}readSync(r){const l=this.state.tensorInfo.get(r);return l.backend.readSync(r)}read(r){const l=this.state.tensorInfo.get(r);return l.backend.read(r)}async time(r){const l=rI(),u=await this.backend.time(r);return u.wallMs=rI()-l,u}track(r){return this.state.activeScope!=null&&(r.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(r)),r}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new iO;for(const r in this.registry)this.disposeRegisteredKernels(r),this.registry[r].dispose(),delete this.registry[r];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}Ru.nextTensorId=0;Ru.nextVariableId=0;function ZJ(r){const l=Jm(Zt(r),"float32");return H.makeTensor(l,r,"float32")}function dI(){const r=QS();if(r._tfengine==null){const l=new m2(r);r._tfengine=new Ru(l)}return g2(r._tfengine.ENV),ZR(()=>r._tfengine),r._tfengine}const H=dI();function QJ(r,l){const u={a:r,b:l};return H.runKernelFunc((p,y)=>{const g=p.add(r,l);return y([r,l]),g},u,null,Dc)}function rO(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}const yr=Es();yr.registerFlag("DEBUG",()=>!1,r=>{r&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. 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I=M(r,"boxes","nonMaxSuppression"),S=M(l,"scores","nonMaxSuppression"),T=Hs(I,S,u,p,y,null),C=T.maxOutputSize,D=T.iouThreshold,_=T.scoreThreshold,A={boxes:I,scores:S},B={maxOutputSize:C,iouThreshold:D,scoreThreshold:_,padToMaxOutputSize:g},ne=H.runKernel(IR,A,B);return{selectedIndices:ne[0],validOutputs:ne[1]}}const UO=V({nonMaxSuppressionPadded_:KZ});async function XZ(r,l,u,p=.5,y=Number.NEGATIVE_INFINITY,g=!1){const I=M(r,"boxes","nonMaxSuppressionAsync"),S=M(l,"scores","nonMaxSuppressionAsync"),T=Hs(I,S,u,p,y,null),C=T.maxOutputSize,D=T.iouThreshold,_=T.scoreThreshold,[A,B]=await Promise.all([I.data(),S.data()]),ne=DO(A,B,C,D,_,g);return I!==r&&I.dispose(),S!==l&&S.dispose(),ne}const BO=XZ;function JZ(r,l,u=!1){const p=M(r,"images","resizeBilinear");J(p.rank===3||p.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${p.rank}.`),J(l.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${l}.`);let y=p,g=!1;p.rank===3&&(g=!0,y=re(p,[1,p.shape[0],p.shape[1],p.shape[2]]));const[I,S]=l,T=(A,B)=>(B([y]),A.resizeBilinear(y,I,S,u)),C={images:y},D={alignCorners:u,size:l},_=H.runKernelFunc(T,C,null,Ef,D);return g?re(_,[_.shape[1],_.shape[2],_.shape[3]]):_}const MO=V({resizeBilinear_:JZ});function ZZ(r,l,u=!1){const p=M(r,"images","resizeNearestNeighbor");J(p.rank===3||p.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${p.rank}.`),J(l.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${l}.`),J(p.dtype==="float32"||p.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let y=p,g=!1;p.rank===3&&(g=!0,y=re(p,[1,p.shape[0],p.shape[1],p.shape[2]]));const[I,S]=l,T={images:y},C={alignCorners:u,size:l},D=(A,B)=>(B([y]),A.resizeNearestNeighbor(y,I,S,u)),_=H.runKernelFunc(D,T,null,Of,C);return g?re(_,[_.shape[1],_.shape[2],_.shape[3]]):_}const PO=V({resizeNearestNeighbor_:ZZ});function 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QI(r,l){const{manifestUri:u,modelBaseUri:p}=bg(r,l);let y=await ZI(u);return jE.io.loadWeights(y,p)}function xQ(r,l,u=!1){const{width:p,height:y}=u?ea(l):l;return r.width=p,r.height=y,{width:p,height:y}}const Sr=Je(Ze());class Wn{constructor(r){this._name=r;this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(r){const{obj:l,objProp:u}=this.traversePropertyPath(r);return l[u]}reassignParamFromPath(r,l){const{obj:u,objProp:p}=this.traversePropertyPath(r);u[p].dispose(),u[p]=l}getParamList(){return this._paramMappings.map(({paramPath:r})=>({path:r,tensor:this.getParamFromPath(r)}))}getTrainableParams(){return this.getParamList().filter(r=>r.tensor instanceof Sr.Variable)}getFrozenParams(){return this.getParamList().filter(r=>!(r.tensor instanceof Sr.Variable))}variable(){this.getFrozenParams().forEach(({path:r,tensor:l})=>{this.reassignParamFromPath(r,l.variable())})}freeze(){this.getTrainableParams().forEach(({path:r,tensor:l})=>{const u=Sr.tensor(l.dataSync());l.dispose(),this.reassignParamFromPath(r,u)})}dispose(r=!0){this.getParamList().forEach(l=>{if(r&&l.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${l.path}`);l.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:r})=>Array.from(r.dataSync())).reduce((r,l)=>r.concat(l)))}async load(r){if(r instanceof Float32Array){this.extractWeights(r);return}await this.loadFromUri(r)}async loadFromUri(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const l=await QI(r,this.getDefaultModelName());this.loadFromWeightMap(l)}async loadFromDisk(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:l}=St.getEnv(),{manifestUri:u,modelBaseUri:p}=bg(r,this.getDefaultModelName()),y=T=>Promise.all(T.map(C=>l(C).then(D=>D.buffer))),g=Sr.io.weightsLoaderFactory(y),I=JSON.parse((await l(u)).toString()),S=await g(I,p);this.loadFromWeightMap(S)}loadFromWeightMap(r){const{paramMappings:l,params:u}=this.extractParamsFromWeigthMap(r);this._paramMappings=l,this._params=u}extractWeights(r){const{paramMappings:l,params:u}=this.extractParams(r);this._paramMappings=l,this._params=u}traversePropertyPath(r){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const l=r.split("/").reduce((y,g)=>{if(!y.nextObj.hasOwnProperty(g))throw new Error(`traversePropertyPath - object does not have property ${g}, for path ${r}`);return{obj:y.nextObj,objProp:g,nextObj:y.nextObj[g]}},{nextObj:this.params}),{obj:u,objProp:p}=l;if(!u||!p||!(u[p]instanceof Sr.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${r}`);return{obj:u,objProp:p}}}const Gc=Je(Ze());function os(r,l,u){return Gc.tidy(()=>{let p=Gc.separableConv2d(r,l.depthwise_filter,l.pointwise_filter,u,"same");return p=Gc.add(p,l.bias),p})}const Bt=Je(Ze());function wg(r,l,u=!1){return Bt.tidy(()=>{const p=Bt.relu(u?Bt.add(Bt.conv2d(r,l.conv0.filters,[2,2],"same"),l.conv0.bias):os(r,l.conv0,[2,2])),y=os(p,l.conv1,[1,1]),g=Bt.relu(Bt.add(p,y)),I=os(g,l.conv2,[1,1]);return Bt.relu(Bt.add(p,Bt.add(y,I)))})}function Uu(r,l,u=!1,p=!0){return Bt.tidy(()=>{const y=Bt.relu(u?Bt.add(Bt.conv2d(r,l.conv0.filters,p?[2,2]:[1,1],"same"),l.conv0.bias):os(r,l.conv0,p?[2,2]:[1,1])),g=os(y,l.conv1,[1,1]),I=Bt.relu(Bt.add(y,g)),S=os(I,l.conv2,[1,1]),T=Bt.relu(Bt.add(y,Bt.add(g,S))),C=os(T,l.conv3,[1,1]);return Bt.relu(Bt.add(y,Bt.add(g,Bt.add(S,C))))})}const uo=Je(Ze());function ua(r,l,u="same",p=!1){return uo.tidy(()=>{const y=uo.add(uo.conv2d(r,l.filters,[1,1],u),l.bias);return p?uo.relu(y):y})}function Yn(r,l){Object.keys(r).forEach(u=>{l.some(p=>p.originalPath===u)||r[u].dispose()})}const Lg=Je(Ze());function Yc(r,l){return function(u,p,y,g){const I=Lg.tensor4d(r(u*p*y*y),[y,y,u,p]),S=Lg.tensor1d(r(p));return l.push({paramPath:`${g}/filters`},{paramPath:`${g}/bias`}),{filters:I,bias:S}}}const Sg=Je(Ze());function Ig(r,l){return function(u,p,y){const g=Sg.tensor2d(r(u*p),[u,p]),I=Sg.tensor1d(r(p));return l.push({paramPath:`${y}/weights`},{paramPath:`${y}/bias`}),{weights:g,bias:I}}}class ex{constructor(r,l,u){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=u}}const Bu=Je(Ze());function Hc(r,l){return function(u,p,y){const g=Bu.tensor4d(r(3*3*u),[3,3,u,1]),I=Bu.tensor4d(r(u*p),[1,1,u,p]),S=Bu.tensor1d(r(p));return l.push({paramPath:`${y}/depthwise_filter`},{paramPath:`${y}/pointwise_filter`},{paramPath:`${y}/bias`}),new ex(g,I,S)}}function qc(r){return function(l){const u=r(`${l}/depthwise_filter`,4),p=r(`${l}/pointwise_filter`,4),y=r(`${l}/bias`,1);return new ex(u,p,y)}}function gs(r,l){return function(u,p,y){const g=r[u];if(!jo(g,p))throw new Error(`expected weightMap[${u}] to be a Tensor${p}D, instead have ${g}`);return l.push({originalPath:u,paramPath:y||u}),g}}function Hn(r){let l=r;function u(y){const g=l.slice(0,y);return l=l.slice(y),g}function p(){return l}return{extractWeights:u,getRemainingWeights:p}}function xg(r,l){const u=Yc(r,l),p=Hc(r,l);function y(I,S,T,C=!1){const D=C?u(I,S,3,`${T}/conv0`):p(I,S,`${T}/conv0`),_=p(S,S,`${T}/conv1`),A=p(S,S,`${T}/conv2`);return{conv0:D,conv1:_,conv2:A}}function g(I,S,T,C=!1){const{conv0:D,conv1:_,conv2:A}=y(I,S,T,C),B=p(S,S,`${T}/conv3`);return{conv0:D,conv1:_,conv2:A,conv3:B}}return{extractDenseBlock3Params:y,extractDenseBlock4Params:g}}function KE(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Hn(r),{extractDenseBlock4Params:y}=xg(u,l),g=y(3,32,"dense0",!0),I=y(32,64,"dense1"),S=y(64,128,"dense2"),T=y(128,256,"dense3");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:g,dense1:I,dense2:S,dense3:T}}}function Tg(r){return function(l){const u=r(`${l}/filters`,4),p=r(`${l}/bias`,1);return{filters:u,bias:p}}}function Ag(r,l){const u=gs(r,l),p=Tg(u),y=qc(u);function g(S,T=!1){const C=T?p(`${S}/conv0`):y(`${S}/conv0`),D=y(`${S}/conv1`),_=y(`${S}/conv2`);return{conv0:C,conv1:D,conv2:_}}function I(S,T=!1){const C=T?p(`${S}/conv0`):y(`${S}/conv0`),D=y(`${S}/conv1`),_=y(`${S}/conv2`),A=y(`${S}/conv3`);return{conv0:C,conv1:D,conv2:_,conv3:A}}return{extractDenseBlock3Params:g,extractDenseBlock4Params:I}}function XE(r){const l=[],{extractDenseBlock4Params:u}=Ag(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2"),dense3:u("dense3")};return Yn(r,l),{params:p,paramMappings:l}}const po=Je(Ze());class vg extends Wn{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return po.tidy(()=>{const u=po.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],y=di(u,p).div(po.scalar(255));let g=Uu(y,l.dense0,!0);return g=Uu(g,l.dense1),g=Uu(g,l.dense2),g=Uu(g,l.dense3),g=po.avgPool(g,[7,7],[2,2],"valid"),g})}async forward(r){return this.forwardInput(await Wt(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return XE(r)}extractParams(r){return KE(r)}}const jc=Je(Ze());function Mu(r,l){return jc.tidy(()=>jc.add(jc.matMul(r,l.weights),l.bias))}function JE(r,l,u){const p=[],{extractWeights:y,getRemainingWeights:g}=Hn(r),I=Ig(y,p),S=I(l,u,"fc");if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:p,params:{fc:S}}}function ZE(r){const l=[],u=gs(r,l);function p(g){const I=u(`${g}/weights`,2),S=u(`${g}/bias`,1);return{weights:I,bias:S}}const y={fc:p("fc")};return Yn(r,l),{params:y,paramMappings:l}}function Ng(r){const l={},u={};return Object.keys(r).forEach(p=>{const y=p.startsWith("fc")?u:l;y[p]=r[p]}),{featureExtractorMap:l,classifierMap:u}}const QE=Je(Ze());class Cg extends Wn{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return QE.tidy(()=>{const u=r instanceof ho?this.faceFeatureExtractor.forwardInput(r):r;return Mu(u.as2D(u.shape[0],-1),l.fc)})}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return JE(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Ng(r);return this.faceFeatureExtractor.loadFromWeightMap(l),ZE(u)}extractParams(r){const l=this.getClassifierChannelsIn(),u=this.getClassifierChannelsOut(),p=u*l+u,y=r.slice(0,r.length-p),g=r.slice(r.length-p);return this.faceFeatureExtractor.extractWeights(y),this.extractClassifierParams(g)}}const tx=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class da{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);tx.forEach((l,u)=>{this[l]=r[u]})}asSortedArray(){return tx.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const Kc=Je(Ze());class nx extends Cg{constructor(r=new vg){super("FaceExpressionNet",r)}forwardInput(r){return Kc.tidy(()=>Kc.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Wt(r))}async predictExpressions(r){const l=await Wt(r),u=await this.forwardInput(l),p=await Promise.all(Kc.unstack(u).map(async g=>{const I=await g.data();return g.dispose(),I}));u.dispose();const y=p.map(g=>new da(g));return l.isBatchInput?y:y[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function sx(r){return r.expressions instanceof da}function Rg(r,l){const u={expressions:l};return Object.assign({},r,u)}function TQ(r,l,u=.1,p){const y=Array.isArray(l)?l:[l];y.forEach(g=>{const I=g instanceof da?g:sx(g)?g.expressions:void 0;if(!I)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const S=I.asSortedArray(),T=S.filter(_=>_.probability>u),C=$i(g)?g.detection.box.bottomLeft:p||new Qe(0,0),D=new Cc(T.map(_=>`${_.expression} (${Ko(_.probability)})`),C);D.draw(r)})}function pa(r){return $i(r)&&r.landmarks instanceof Gs&&r.unshiftedLandmarks instanceof Gs&&r.alignedRect instanceof Jt}function Xc(r,l){const{box:u}=r.detection,p=l.shiftBy(u.x,u.y),y=p.align(),{imageDims:g}=r.detection,I=new Jt(r.detection.score,y.rescale(g.reverse()),g),S={landmarks:p,unshiftedLandmarks:l,alignedRect:I};return Object.assign({},r,S)}class eD{constructor(r={}){const{drawLines:l=!0,drawPoints:u=!0,lineWidth:p,lineColor:y,pointSize:g,pointColor:I}=r;this.drawLines=l,this.drawPoints=u,this.lineWidth=p||1,this.pointSize=g||2,this.lineColor=y||"rgba(0, 255, 255, 1)",this.pointColor=I||"rgba(255, 0, 255, 1)"}}class tD{constructor(r,l={}){this.faceLandmarks=r,this.options=new eD(l)}draw(r){const l=is(r),{drawLines:u,drawPoints:p,lineWidth:y,lineColor:g,pointSize:I,pointColor:S}=this.options;if(u&&this.faceLandmarks instanceof wu&&(l.strokeStyle=g,l.lineWidth=y,fr(l,this.faceLandmarks.getJawOutline()),fr(l,this.faceLandmarks.getLeftEyeBrow()),fr(l,this.faceLandmarks.getRightEyeBrow()),fr(l,this.faceLandmarks.getNose()),fr(l,this.faceLandmarks.getLeftEye(),!0),fr(l,this.faceLandmarks.getRightEye(),!0),fr(l,this.faceLandmarks.getMouth(),!0)),p){l.strokeStyle=S,l.fillStyle=S;const T=C=>{l.beginPath(),l.arc(C.x,C.y,I,0,2*Math.PI),l.fill()};this.faceLandmarks.positions.forEach(T)}}}function AQ(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const y=p instanceof Gs?p:pa(p)?p.landmarks:void 0;if(!y)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new tD(y).draw(r)})}const ix={};Pm(ix,{AnchorPosition:()=>Ui,DrawBox:()=>HS,DrawBoxOptions:()=>s2,DrawFaceLandmarks:()=>tD,DrawFaceLandmarksOptions:()=>eD,DrawTextField:()=>Cc,DrawTextFieldOptions:()=>jm,drawContour:()=>fr,drawDetections:()=>_J,drawFaceExpressions:()=>TQ,drawFaceLandmarks:()=>AQ});function vQ(r,l){const u=Yc(r,l),p=Hc(r,l);function y(I,S,T){const C=p(I,S,`${T}/separable_conv0`),D=p(S,S,`${T}/separable_conv1`),_=u(I,S,1,`${T}/expansion_conv`);return{separable_conv0:C,separable_conv1:D,expansion_conv:_}}function g(I,S){const T=p(I,I,`${S}/separable_conv0`),C=p(I,I,`${S}/separable_conv1`),D=p(I,I,`${S}/separable_conv2`);return{separable_conv0:T,separable_conv1:C,separable_conv2:D}}return{extractConvParams:u,extractSeparableConvParams:p,extractReductionBlockParams:y,extractMainBlockParams:g}}function nD(r,l){const u=[],{extractWeights:p,getRemainingWeights:y}=Hn(r),{extractConvParams:g,extractSeparableConvParams:I,extractReductionBlockParams:S,extractMainBlockParams:T}=vQ(p,u),C=g(3,32,3,"entry_flow/conv_in"),D=S(32,64,"entry_flow/reduction_block_0"),_=S(64,128,"entry_flow/reduction_block_1"),A={conv_in:C,reduction_block_0:D,reduction_block_1:_},B={};_i(l,0,1).forEach(ge=>{B[`main_block_${ge}`]=T(128,`middle_flow/main_block_${ge}`)});const ne=S(128,256,"exit_flow/reduction_block"),te=I(256,512,"exit_flow/separable_conv"),P={reduction_block:ne,separable_conv:te};if(y().length!==0)throw new Error(`weights remaing after extract: ${y().length}`);return{paramMappings:u,params:{entry_flow:A,middle_flow:B,exit_flow:P}}}function NQ(r,l){const u=gs(r,l),p=Tg(u),y=qc(u);function g(S){const T=y(`${S}/separable_conv0`),C=y(`${S}/separable_conv1`),D=p(`${S}/expansion_conv`);return{separable_conv0:T,separable_conv1:C,expansion_conv:D}}function I(S){const T=y(`${S}/separable_conv0`),C=y(`${S}/separable_conv1`),D=y(`${S}/separable_conv2`);return{separable_conv0:T,separable_conv1:C,separable_conv2:D}}return{extractConvParams:p,extractSeparableConvParams:y,extractReductionBlockParams:g,extractMainBlockParams:I}}function sD(r,l){const u=[],{extractConvParams:p,extractSeparableConvParams:y,extractReductionBlockParams:g,extractMainBlockParams:I}=NQ(r,u),S=p("entry_flow/conv_in"),T=g("entry_flow/reduction_block_0"),C=g("entry_flow/reduction_block_1"),D={conv_in:S,reduction_block_0:T,reduction_block_1:C},_={};_i(l,0,1).forEach(te=>{_[`main_block_${te}`]=I(`middle_flow/main_block_${te}`)});const A=g("exit_flow/reduction_block"),B=y("exit_flow/separable_conv"),ne={reduction_block:A,separable_conv:B};return Yn(r,u),{params:{entry_flow:D,middle_flow:_,exit_flow:ne},paramMappings:u}}const on=Je(Ze());function iD(r,l,u){return on.add(on.conv2d(r,l.filters,u,"same"),l.bias)}function rx(r,l,u=!0){let p=u?on.relu(r):r;return p=os(p,l.separable_conv0,[1,1]),p=os(on.relu(p),l.separable_conv1,[1,1]),p=on.maxPool(p,[3,3],[2,2],"same"),p=on.add(p,iD(r,l.expansion_conv,[2,2])),p}function CQ(r,l){let u=os(on.relu(r),l.separable_conv0,[1,1]);return u=os(on.relu(u),l.separable_conv1,[1,1]),u=os(on.relu(u),l.separable_conv2,[1,1]),u=on.add(u,r),u}class rD extends Wn{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return on.tidy(()=>{const u=on.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],y=di(u,p).div(on.scalar(256));let g=on.relu(iD(y,l.entry_flow.conv_in,[2,2]));return g=rx(g,l.entry_flow.reduction_block_0,!1),g=rx(g,l.entry_flow.reduction_block_1),_i(this._numMainBlocks,0,1).forEach(I=>{g=CQ(g,l.middle_flow[`main_block_${I}`])}),g=rx(g,l.exit_flow.reduction_block),g=on.relu(os(g,l.exit_flow.separable_conv,[1,1])),g})}async forward(r){return this.forwardInput(await Wt(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return sD(r,this._numMainBlocks)}extractParams(r){return nD(r,this._numMainBlocks)}}function oD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Hn(r),y=Ig(u,l),g=y(512,1,"fc/age"),I=y(512,2,"fc/gender");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{fc:{age:g,gender:I}}}}function aD(r){const l=[],u=gs(r,l);function p(g){const I=u(`${g}/weights`,2),S=u(`${g}/bias`,1);return{weights:I,bias:S}}const y={fc:{age:p("fc/age"),gender:p("fc/gender")}};return Yn(r,l),{params:y,paramMappings:l}}var Ir;(function(r){r.FEMALE="female",r.MALE="male"})(Ir||(Ir={}));const Gi=Je(Ze());class ox extends Wn{constructor(r=new rD(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Gi.tidy(()=>{const u=r instanceof ho?this.faceFeatureExtractor.forwardInput(r):r,p=Gi.avgPool(u,[7,7],[2,2],"valid").as2D(u.shape[0],-1),y=Mu(p,l.fc.age).as1D(),g=Mu(p,l.fc.gender);return{age:y,gender:g}})}forwardInput(r){return Gi.tidy(()=>{const{age:l,gender:u}=this.runNet(r);return{age:l,gender:Gi.softmax(u)}})}async forward(r){return this.forwardInput(await Wt(r))}async predictAgeAndGender(r){const l=await Wt(r),u=await this.forwardInput(l),p=Gi.unstack(u.age),y=Gi.unstack(u.gender),g=p.map((S,T)=>({ageTensor:S,genderTensor:y[T]})),I=await Promise.all(g.map(async({ageTensor:S,genderTensor:T})=>{const C=(await S.data())[0],D=(await T.data())[0],_=D>.5,A=_?Ir.MALE:Ir.FEMALE,B=_?D:1-D;return S.dispose(),T.dispose(),{age:C,gender:A,genderProbability:B}}));return u.age.dispose(),u.gender.dispose(),l.isBatchInput?I:I[0]}getDefaultModelName(){return"age_gender_model"}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return oD(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Ng(r);return this.faceFeatureExtractor.loadFromWeightMap(l),aD(u)}extractParams(r){const l=512*1+1+(512*2+2),u=r.slice(0,r.length-l),p=r.slice(r.length-l);return this.faceFeatureExtractor.extractWeights(u),this.extractClassifierParams(p)}}const ys=Je(Ze());class Og extends Cg{postProcess(r,l,u){const p=u.map(({width:g,height:I})=>{const S=l/Math.max(I,g);return{width:g*S,height:I*S}}),y=p.length;return ys.tidy(()=>{const g=(D,_)=>ys.stack([ys.fill([68],D,"float32"),ys.fill([68],_,"float32")],1).as2D(1,136).as1D(),I=(D,_)=>{const{width:A,height:B}=p[D];return _(A,B)?Math.abs(A-B)/2:0},S=D=>I(D,(_,A)=>_<A),T=D=>I(D,(_,A)=>A<_),C=r.mul(ys.fill([y,136],l,"float32")).sub(ys.stack(Array.from(Array(y),(D,_)=>g(S(_),T(_))))).div(ys.stack(Array.from(Array(y),(D,_)=>g(p[_].width,p[_].height))));return C})}forwardInput(r){return ys.tidy(()=>{const l=this.runNet(r);return this.postProcess(l,r.inputSize,r.inputDimensions.map(([u,p])=>({height:u,width:p})))})}async forward(r){return this.forwardInput(await Wt(r))}async detectLandmarks(r){const l=await Wt(r),u=ys.tidy(()=>ys.unstack(this.forwardInput(l))),p=await Promise.all(u.map(async(y,g)=>{const I=Array.from(await y.data()),S=I.filter((C,D)=>Vm(D)),T=I.filter((C,D)=>!Vm(D));return new wu(Array(68).fill(0).map((C,D)=>new Qe(S[D],T[D])),{height:l.getInputHeight(g),width:l.getInputWidth(g)})}));return u.forEach(y=>y.dispose()),l.isBatchInput?p:p[0]}getClassifierChannelsOut(){return 136}}class Pu extends Og{constructor(r=new vg){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function cD(r){const l=[],{extractDenseBlock3Params:u}=Ag(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return Yn(r,l),{params:p,paramMappings:l}}function lD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Hn(r),{extractDenseBlock3Params:y}=xg(u,l),g=y(3,32,"dense0",!0),I=y(32,64,"dense1"),S=y(64,128,"dense2");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:g,dense1:I,dense2:S}}}const mo=Je(Ze());class hD extends Wn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return mo.tidy(()=>{const u=mo.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],y=di(u,p).div(mo.scalar(255));let g=wg(y,l.dense0,!0);return g=wg(g,l.dense1),g=wg(g,l.dense2),g=mo.avgPool(g,[14,14],[2,2],"valid"),g})}async forward(r){return this.forwardInput(await Wt(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return cD(r)}extractParams(r){return lD(r)}}class ax extends Og{constructor(r=new hD){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class RQ extends Pu{}const Eg=Je(Ze());function uD(r,l){return Eg.add(Eg.mul(r,l.weights),l.biases)}const Jc=Je(Ze());function cx(r,l,u,p,y="same"){const{filters:g,bias:I}=l.conv;let S=Jc.conv2d(r,g,u,y);return S=Jc.add(S,I),S=uD(S,l.scale),p?Jc.relu(S):S}function dD(r,l){return cx(r,l,[1,1],!0)}function lx(r,l){return cx(r,l,[1,1],!1)}function Dg(r,l){return cx(r,l,[2,2],!0,"valid")}const bs=Je(Ze());function OQ(r,l){function u(S,T,C){const D=r(S),_=D.length/(T*C*C);if(FS(_))throw new Error(`depth has to be an integer: ${_}, weights.length: ${D.length}, numFilters: ${T}, filterSize: ${C}`);return bs.tidy(()=>bs.transpose(bs.tensor4d(D,[T,_,C,C]),[2,3,1,0]))}function p(S,T,C,D){const _=u(S,T,C),A=bs.tensor1d(r(T));return l.push({paramPath:`${D}/filters`},{paramPath:`${D}/bias`}),{filters:_,bias:A}}function y(S,T){const C=bs.tensor1d(r(S)),D=bs.tensor1d(r(S));return l.push({paramPath:`${T}/weights`},{paramPath:`${T}/biases`}),{weights:C,biases:D}}function g(S,T,C,D){const _=p(S,T,C,`${D}/conv`),A=y(T,`${D}/scale`);return{conv:_,scale:A}}function I(S,T,C,D,_=!1){const A=g((_?.5:1)*S,T,C,`${D}/conv1`),B=g(S,T,C,`${D}/conv2`);return{conv1:A,conv2:B}}return{extractConvLayerParams:g,extractResidualLayerParams:I}}function pD(r){const{extractWeights:l,getRemainingWeights:u}=Hn(r),p=[],{extractConvLayerParams:y,extractResidualLayerParams:g}=OQ(l,p),I=y(4704,32,7,"conv32_down"),S=g(9216,32,3,"conv32_1"),T=g(9216,32,3,"conv32_2"),C=g(9216,32,3,"conv32_3"),D=g(36864,64,3,"conv64_down",!0),_=g(36864,64,3,"conv64_1"),A=g(36864,64,3,"conv64_2"),B=g(36864,64,3,"conv64_3"),ne=g(147456,128,3,"conv128_down",!0),te=g(147456,128,3,"conv128_1"),P=g(147456,128,3,"conv128_2"),ge=g(589824,256,3,"conv256_down",!0),ae=g(589824,256,3,"conv256_1"),Le=g(589824,256,3,"conv256_2"),ve=g(589824,256,3,"conv256_down_out"),Ve=bs.tidy(()=>bs.transpose(bs.tensor2d(l(256*128),[128,256]),[1,0]));if(p.push({paramPath:"fc"}),u().length!==0)throw new Error(`weights remaing after extract: ${u().length}`);const at={conv32_down:I,conv32_1:S,conv32_2:T,conv32_3:C,conv64_down:D,conv64_1:_,conv64_2:A,conv64_3:B,conv128_down:ne,conv128_1:te,conv128_2:P,conv256_down:ge,conv256_1:ae,conv256_2:Le,conv256_down_out:ve,fc:Ve};return{params:at,paramMappings:p}}function EQ(r,l){const u=gs(r,l);function p(I){const S=u(`${I}/scale/weights`,1),T=u(`${I}/scale/biases`,1);return{weights:S,biases:T}}function y(I){const S=u(`${I}/conv/filters`,4),T=u(`${I}/conv/bias`,1),C=p(I);return{conv:{filters:S,bias:T},scale:C}}function g(I){return{conv1:y(`${I}/conv1`),conv2:y(`${I}/conv2`)}}return{extractConvLayerParams:y,extractResidualLayerParams:g}}function mD(r){const l=[],{extractConvLayerParams:u,extractResidualLayerParams:p}=EQ(r,l),y=u("conv32_down"),g=p("conv32_1"),I=p("conv32_2"),S=p("conv32_3"),T=p("conv64_down"),C=p("conv64_1"),D=p("conv64_2"),_=p("conv64_3"),A=p("conv128_down"),B=p("conv128_1"),ne=p("conv128_2"),te=p("conv256_down"),P=p("conv256_1"),ge=p("conv256_2"),ae=p("conv256_down_out"),Le=r.fc;if(l.push({originalPath:"fc",paramPath:"fc"}),!kS(Le))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${Le}`);const ve={conv32_down:y,conv32_1:g,conv32_2:I,conv32_3:S,conv64_down:T,conv64_1:C,conv64_2:D,conv64_3:_,conv128_down:A,conv128_1:B,conv128_2:ne,conv256_down:te,conv256_1:P,conv256_2:ge,conv256_down_out:ae,fc:Le};return Yn(r,l),{params:ve,paramMappings:l}}const qn=Je(Ze());function gi(r,l){let u=dD(r,l.conv1);return u=lx(u,l.conv2),u=qn.add(u,r),u=qn.relu(u),u}function zu(r,l){let u=Dg(r,l.conv1);u=lx(u,l.conv2);let p=qn.avgPool(r,2,2,"valid");const y=qn.zeros(p.shape),g=p.shape[3]!==u.shape[3],I=p.shape[1]!==u.shape[1]||p.shape[2]!==u.shape[2];if(I){const S=[...u.shape];S[1]=1;const T=qn.zeros(S);u=qn.concat([u,T],1);const C=[...u.shape];C[2]=1;const D=qn.zeros(C);u=qn.concat([u,D],2)}return p=g?qn.concat([p,y],3):p,u=qn.add(p,u),u=qn.relu(u),u}const Fs=Je(Ze());class Vu extends Wn{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return Fs.tidy(()=>{const u=Fs.cast(r.toBatchTensor(150,!0),"float32"),p=[122.782,117.001,104.298],y=di(u,p).div(Fs.scalar(256));let g=Dg(y,l.conv32_down);g=Fs.maxPool(g,3,2,"valid"),g=gi(g,l.conv32_1),g=gi(g,l.conv32_2),g=gi(g,l.conv32_3),g=zu(g,l.conv64_down),g=gi(g,l.conv64_1),g=gi(g,l.conv64_2),g=gi(g,l.conv64_3),g=zu(g,l.conv128_down),g=gi(g,l.conv128_1),g=gi(g,l.conv128_2),g=zu(g,l.conv256_down),g=gi(g,l.conv256_1),g=gi(g,l.conv256_2),g=zu(g,l.conv256_down_out);const I=g.mean([1,2]),S=Fs.matMul(I,l.fc);return S})}async forward(r){return this.forwardInput(await Wt(r))}async computeFaceDescriptor(r){const l=await Wt(r),u=Fs.tidy(()=>Fs.unstack(this.forwardInput(l))),p=await Promise.all(u.map(y=>y.data()));return u.forEach(y=>y.dispose()),l.isBatchInput?p:p[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(r){return mD(r)}extractParams(r){return pD(r)}}function DQ(r){const l=new Vu;return l.extractWeights(r),l}function kg(r,l){const u={descriptor:l};return Object.assign({},r,u)}function kQ(r){return typeof r.age=="number"}function Fg(r,l){const u={age:l};return Object.assign({},r,u)}function FQ(r){return(r.gender===Ir.MALE||r.gender===Ir.FEMALE)&&vc(r.genderProbability)}function _g(r,l,u){const p={gender:l,genderProbability:u};return Object.assign({},r,p)}const yi=Je(Ze());function _Q(r,l){function u(T,C){const D=yi.tensor4d(r(3*3*T),[3,3,T,1]),_=yi.tensor1d(r(T)),A=yi.tensor1d(r(T)),B=yi.tensor1d(r(T)),ne=yi.tensor1d(r(T));return l.push({paramPath:`${C}/filters`},{paramPath:`${C}/batch_norm_scale`},{paramPath:`${C}/batch_norm_offset`},{paramPath:`${C}/batch_norm_mean`},{paramPath:`${C}/batch_norm_variance`}),{filters:D,batch_norm_scale:_,batch_norm_offset:A,batch_norm_mean:B,batch_norm_variance:ne}}function p(T,C,D,_,A){const B=yi.tensor4d(r(T*C*D*D),[D,D,T,C]),ne=yi.tensor1d(r(C));return l.push({paramPath:`${_}/filters`},{paramPath:`${_}/${A?"batch_norm_offset":"bias"}`}),{filters:B,bias:ne}}function y(T,C,D,_){const{filters:A,bias:B}=p(T,C,D,_,!0);return{filters:A,batch_norm_offset:B}}function g(T,C,D){const _=u(T,`${D}/depthwise_conv`),A=y(T,C,1,`${D}/pointwise_conv`);return{depthwise_conv:_,pointwise_conv:A}}function I(){const T=y(3,32,3,"mobilenetv1/conv_0"),C=g(32,64,"mobilenetv1/conv_1"),D=g(64,128,"mobilenetv1/conv_2"),_=g(128,128,"mobilenetv1/conv_3"),A=g(128,256,"mobilenetv1/conv_4"),B=g(256,256,"mobilenetv1/conv_5"),ne=g(256,512,"mobilenetv1/conv_6"),te=g(512,512,"mobilenetv1/conv_7"),P=g(512,512,"mobilenetv1/conv_8"),ge=g(512,512,"mobilenetv1/conv_9"),ae=g(512,512,"mobilenetv1/conv_10"),Le=g(512,512,"mobilenetv1/conv_11"),ve=g(512,1024,"mobilenetv1/conv_12"),Ve=g(1024,1024,"mobilenetv1/conv_13");return{conv_0:T,conv_1:C,conv_2:D,conv_3:_,conv_4:A,conv_5:B,conv_6:ne,conv_7:te,conv_8:P,conv_9:ge,conv_10:ae,conv_11:Le,conv_12:ve,conv_13:Ve}}function S(){const T=y(1024,256,1,"prediction_layer/conv_0"),C=y(256,512,3,"prediction_layer/conv_1"),D=y(512,128,1,"prediction_layer/conv_2"),_=y(128,256,3,"prediction_layer/conv_3"),A=y(256,128,1,"prediction_layer/conv_4"),B=y(128,256,3,"prediction_layer/conv_5"),ne=y(256,64,1,"prediction_layer/conv_6"),te=y(64,128,3,"prediction_layer/conv_7"),P=p(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),ge=p(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),ae=p(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),Le=p(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),ve=p(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Ve=p(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),at=p(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),pt=p(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),$t=p(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Vt=p(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),qe=p(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),ln=p(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),bt={box_encoding_predictor:P,class_predictor:ge},ws={box_encoding_predictor:ae,class_predictor:Le},Nr={box_encoding_predictor:ve,class_predictor:Ve},Cr={box_encoding_predictor:at,class_predictor:pt},ba={box_encoding_predictor:$t,class_predictor:Vt},hn={box_encoding_predictor:qe,class_predictor:ln};return{conv_0:T,conv_1:C,conv_2:D,conv_3:_,conv_4:A,conv_5:B,conv_6:ne,conv_7:te,box_predictor_0:bt,box_predictor_1:ws,box_predictor_2:Nr,box_predictor_3:Cr,box_predictor_4:ba,box_predictor_5:hn}}return{extractMobilenetV1Params:I,extractPredictionLayerParams:S}}function 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D=`mobilenetv1/conv_${C}`,_=`MobilenetV1/Conv2d_${C}_depthwise`,A=`${D}/depthwise_conv`,B=`${D}/pointwise_conv`,ne=u(`${_}/depthwise_weights`,4,`${A}/filters`),te=u(`${_}/BatchNorm/gamma`,1,`${A}/batch_norm_scale`),P=u(`${_}/BatchNorm/beta`,1,`${A}/batch_norm_offset`),ge=u(`${_}/BatchNorm/moving_mean`,1,`${A}/batch_norm_mean`),ae=u(`${_}/BatchNorm/moving_variance`,1,`${A}/batch_norm_variance`);return{depthwise_conv:{filters:ne,batch_norm_scale:te,batch_norm_offset:P,batch_norm_mean:ge,batch_norm_variance:ae},pointwise_conv:p("MobilenetV1",C,B)}}function g(){return{conv_0:p("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:y(1),conv_2:y(2),conv_3:y(3),conv_4:y(4),conv_5:y(5),conv_6:y(6),conv_7:y(7),conv_8:y(8),conv_9:y(9),conv_10:y(10),conv_11:y(11),conv_12:y(12),conv_13:y(13)}}function I(C,D){const _=u(`${C}/weights`,4,`${D}/filters`),A=u(`${C}/biases`,1,`${D}/bias`);return{filters:_,bias:A}}function S(C){const D=I(`Prediction/BoxPredictor_${C}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${C}/box_encoding_predictor`),_=I(`Prediction/BoxPredictor_${C}/ClassPredictor`,`prediction_layer/box_predictor_${C}/class_predictor`);return{box_encoding_predictor:D,class_predictor:_}}function T(){return{conv_0:p("Prediction",0,"prediction_layer/conv_0"),conv_1:p("Prediction",1,"prediction_layer/conv_1"),conv_2:p("Prediction",2,"prediction_layer/conv_2"),conv_3:p("Prediction",3,"prediction_layer/conv_3"),conv_4:p("Prediction",4,"prediction_layer/conv_4"),conv_5:p("Prediction",5,"prediction_layer/conv_5"),conv_6:p("Prediction",6,"prediction_layer/conv_6"),conv_7:p("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:S(0),box_predictor_1:S(1),box_predictor_2:S(2),box_predictor_3:S(3),box_predictor_4:S(4),box_predictor_5:S(5)}}return{extractMobilenetV1Params:g,extractPredictionLayerParams:T}}function gD(r){const l=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:p}=WQ(r,l),y=r["Output/extra_dim"];if(l.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!gr(y))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${y}`);const g={mobilenetv1:u(),prediction_layer:p(),output_layer:{extra_dim:y}};return Yn(r,l),{params:g,paramMappings:l}}const fo=Je(Ze());function qs(r,l,u){return fo.tidy(()=>{let p=fo.conv2d(r,l.filters,u,"same");return p=fo.add(p,l.batch_norm_offset),fo.clipByValue(p,0,6)})}const xr=Je(Ze()),$Q=.0010000000474974513;function UQ(r,l,u){return xr.tidy(()=>{let p=xr.depthwiseConv2d(r,l.filters,u,"same");return p=xr.batchNorm(p,l.batch_norm_mean,l.batch_norm_variance,l.batch_norm_offset,l.batch_norm_scale,$Q),xr.clipByValue(p,0,6)})}function BQ(r){return[2,4,6,12].some(l=>l===r)?[2,2]:[1,1]}function yD(r,l){return xr.tidy(()=>{let u,p=qs(r,l.conv_0,[2,2]);const y=[l.conv_1,l.conv_2,l.conv_3,l.conv_4,l.conv_5,l.conv_6,l.conv_7,l.conv_8,l.conv_9,l.conv_10,l.conv_11,l.conv_12,l.conv_13];if(y.forEach((g,I)=>{const S=I+1,T=BQ(S);p=UQ(p,g.depthwise_conv,T),p=qs(p,g.pointwise_conv,[1,1]),S===11&&(u=p)}),u===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:p,conv11:u}})}function bD(r,l,u,p,y){const g=r.shape[0],I=Math.min(u,g),S=l.map((D,_)=>({score:D,boxIndex:_})).filter(D=>D.score>y).sort((D,_)=>_.score-D.score),T=D=>D<=p?1:0,C=[];return S.forEach(D=>{if(C.length>=I)return;const _=D.score;for(let A=C.length-1;A>=0;--A){const B=MQ(r,D.boxIndex,C[A]);if(B===0)continue;if(D.score*=T(B),D.score<=y)break}_===D.score&&C.push(D.boxIndex)}),C}function MQ(r,l,u){const p=r.arraySync(),y=Math.min(p[l][0],p[l][2]),g=Math.min(p[l][1],p[l][3]),I=Math.max(p[l][0],p[l][2]),S=Math.max(p[l][1],p[l][3]),T=Math.min(p[u][0],p[u][2]),C=Math.min(p[u][1],p[u][3]),D=Math.max(p[u][0],p[u][2]),_=Math.max(p[u][1],p[u][3]),A=(I-y)*(S-g),B=(D-T)*(_-C);if(A<=0||B<=0)return 0;const ne=Math.max(y,T),te=Math.max(g,C),P=Math.min(I,D),ge=Math.min(S,_),ae=Math.max(P-ne,0)*Math.max(ge-te,0);return ae/(A+B-ae)}const ke=Je(Ze());function PQ(r){const l=ke.unstack(ke.transpose(r,[1,0])),u=[ke.sub(l[2],l[0]),ke.sub(l[3],l[1])],p=[ke.add(l[0],ke.div(u[0],ke.scalar(2))),ke.add(l[1],ke.div(u[1],ke.scalar(2)))];return{sizes:u,centers:p}}function zQ(r,l){const{sizes:u,centers:p}=PQ(r),y=ke.unstack(ke.transpose(l,[1,0])),g=ke.div(ke.mul(ke.exp(ke.div(y[2],ke.scalar(5))),u[0]),ke.scalar(2)),I=ke.add(ke.mul(ke.div(y[0],ke.scalar(10)),u[0]),p[0]),S=ke.div(ke.mul(ke.exp(ke.div(y[3],ke.scalar(5))),u[1]),ke.scalar(2)),T=ke.add(ke.mul(ke.div(y[1],ke.scalar(10)),u[1]),p[1]);return ke.transpose(ke.stack([ke.sub(I,g),ke.sub(T,S),ke.add(I,g),ke.add(T,S)]),[1,0])}function wD(r,l,u){return ke.tidy(()=>{const p=r.shape[0];let y=zQ(ke.reshape(ke.tile(u.extra_dim,[p,1,1]),[-1,4]),ke.reshape(r,[-1,4]));y=ke.reshape(y,[p,y.shape[0]/p,4]);const g=ke.sigmoid(ke.slice(l,[0,0,1],[-1,-1,-1]));let I=ke.slice(g,[0,0,0],[-1,-1,1]);I=ke.reshape(I,[p,I.shape[1]]);const S=ke.unstack(y),T=ke.unstack(I);return{boxes:S,scores:T}})}const Gu=Je(Ze());function ma(r,l){return Gu.tidy(()=>{const u=r.shape[0],p=Gu.reshape(ua(r,l.box_encoding_predictor),[u,-1,1,4]),y=Gu.reshape(ua(r,l.class_predictor),[u,-1,3]);return{boxPredictionEncoding:p,classPrediction:y}})}const Yu=Je(Ze());function LD(r,l,u){return Yu.tidy(()=>{const p=qs(r,u.conv_0,[1,1]),y=qs(p,u.conv_1,[2,2]),g=qs(y,u.conv_2,[1,1]),I=qs(g,u.conv_3,[2,2]),S=qs(I,u.conv_4,[1,1]),T=qs(S,u.conv_5,[2,2]),C=qs(T,u.conv_6,[1,1]),D=qs(C,u.conv_7,[2,2]),_=ma(l,u.box_predictor_0),A=ma(r,u.box_predictor_1),B=ma(y,u.box_predictor_2),ne=ma(I,u.box_predictor_3),te=ma(T,u.box_predictor_4),P=ma(D,u.box_predictor_5),ge=Yu.concat([_.boxPredictionEncoding,A.boxPredictionEncoding,B.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,P.boxPredictionEncoding],1),ae=Yu.concat([_.classPrediction,A.classPrediction,B.classPrediction,ne.classPrediction,te.classPrediction,P.classPrediction],1);return{boxPredictions:ge,classPredictions:ae}})}class bi{constructor({minConfidence:r,maxResults:l}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=r||.5,this._maxResults=l||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}const wi=Je(Ze());class Zc extends Wn{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return wi.tidy(()=>{const u=wi.cast(r.toBatchTensor(512,!1),"float32"),p=wi.sub(wi.mul(u,wi.scalar(.007843137718737125)),wi.scalar(1)),y=yD(p,l.mobilenetv1),{boxPredictions:g,classPredictions:I}=LD(y.out,y.conv11,l.prediction_layer);return wD(g,I,l.output_layer)})}async forward(r){return this.forwardInput(await Wt(r))}async locateFaces(r,l={}){const{maxResults:u,minConfidence:p}=new bi(l),y=await Wt(r),{boxes:g,scores:I}=this.forwardInput(y),S=g[0],T=I[0];for(let ae=1;ae<g.length;ae++)g[ae].dispose(),I[ae].dispose();const C=Array.from(await T.data()),D=.5,_=bD(S,C,u,D,p),A=y.getReshapedInputDimensions(0),B=y.inputSize,ne=B/A.width,te=B/A.height,P=S.arraySync(),ge=_.map(ae=>{const[Le,ve]=[Math.max(0,P[ae][0]),Math.min(1,P[ae][2])].map(pt=>pt*te),[Ve,at]=[Math.max(0,P[ae][1]),Math.min(1,P[ae][3])].map(pt=>pt*ne);return new Jt(C[ae],new bu(Ve,Le,at-Ve,ve-Le),{height:y.getInputHeight(0),width:y.getInputWidth(0)})});return S.dispose(),T.dispose(),ge}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(r){return gD(r)}extractParams(r){return fD(r)}}function SD(r){const l=new Zc;return l.extractWeights(r),l}function VQ(r){return SD(r)}class GQ extends Zc{}const ID=.4,xD=[new Qe(.738768,.874946),new Qe(2.42204,2.65704),new Qe(4.30971,7.04493),new Qe(10.246,4.59428),new Qe(12.6868,11.8741)],TD=[new Qe(1.603231,2.094468),new Qe(6.041143,7.080126),new Qe(2.882459,3.518061),new Qe(4.266906,5.178857),new Qe(9.041765,10.66308)],AD=[117.001,114.697,97.404],vD="tiny_yolov2_model",ND="tiny_yolov2_separable_conv_model";const Wg=r=>typeof r=="number";function hx(r){if(!r)throw new Error(`invalid config: ${r}`);if(typeof r.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${r.withSeparableConvs}`);if(!Wg(r.iouThreshold)||r.iouThreshold<0||r.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${r.iouThreshold}`);if(!Array.isArray(r.classes)||!r.classes.length||!r.classes.every(l=>typeof l=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(r.classes)}`);if(!Array.isArray(r.anchors)||!r.anchors.length||!r.anchors.map(l=>l||{}).every(l=>Wg(l.x)&&Wg(l.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(r.anchors)}`);if(r.meanRgb&&(!Array.isArray(r.meanRgb)||r.meanRgb.length!==3||!r.meanRgb.every(Wg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const js=Je(Ze());function Qc(r){return js.tidy(()=>{const l=js.mul(r,js.scalar(.10000000149011612));return js.add(js.relu(js.sub(r,l)),l)})}const Ks=Je(Ze());function Tr(r,l){return Ks.tidy(()=>{let u=Ks.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=Ks.conv2d(u,l.conv.filters,[1,1],"valid"),u=Ks.sub(u,l.bn.sub),u=Ks.mul(u,l.bn.truediv),u=Ks.add(u,l.conv.bias),Qc(u)})}const go=Je(Ze());function Ar(r,l){return go.tidy(()=>{let u=go.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=go.separableConv2d(u,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),u=go.add(u,l.bias),Qc(u)})}const ux=Je(Ze());function YQ(r,l){const u=Yc(r,l);function p(I,S){const T=ux.tensor1d(r(I)),C=ux.tensor1d(r(I));return l.push({paramPath:`${S}/sub`},{paramPath:`${S}/truediv`}),{sub:T,truediv:C}}function y(I,S,T){const C=u(I,S,3,`${T}/conv`),D=p(S,`${T}/bn`);return{conv:C,bn:D}}const g=Hc(r,l);return{extractConvParams:u,extractConvWithBatchNormParams:y,extractSeparableConvParams:g}}function CD(r,l,u,p){const{extractWeights:y,getRemainingWeights:g}=Hn(r),I=[],{extractConvParams:S,extractConvWithBatchNormParams:T,extractSeparableConvParams:C}=YQ(y,I);let D;if(l.withSeparableConvs){const[_,A,B,ne,te,P,ge,ae,Le]=p,ve=l.isFirstLayerConv2d?S(_,A,3,"conv0"):C(_,A,"conv0"),Ve=C(A,B,"conv1"),at=C(B,ne,"conv2"),pt=C(ne,te,"conv3"),$t=C(te,P,"conv4"),Vt=C(P,ge,"conv5"),qe=ae?C(ge,ae,"conv6"):void 0,ln=Le?C(ae,Le,"conv7"):void 0,bt=S(Le||ae||ge,5*u,1,"conv8");D={conv0:ve,conv1:Ve,conv2:at,conv3:pt,conv4:$t,conv5:Vt,conv6:qe,conv7:ln,conv8:bt}}else{const[_,A,B,ne,te,P,ge,ae,Le]=p,ve=T(_,A,"conv0"),Ve=T(A,B,"conv1"),at=T(B,ne,"conv2"),pt=T(ne,te,"conv3"),$t=T(te,P,"conv4"),Vt=T(P,ge,"conv5"),qe=T(ge,ae,"conv6"),ln=T(ae,Le,"conv7"),bt=S(Le,5*u,1,"conv8");D={conv0:ve,conv1:Ve,conv2:at,conv3:pt,conv4:$t,conv5:Vt,conv6:qe,conv7:ln,conv8:bt}}if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{params:D,paramMappings:I}}function HQ(r,l){const u=gs(r,l);function p(S){const T=u(`${S}/sub`,1),C=u(`${S}/truediv`,1);return{sub:T,truediv:C}}function y(S){const T=u(`${S}/filters`,4),C=u(`${S}/bias`,1);return{filters:T,bias:C}}function g(S){const T=y(`${S}/conv`),C=p(`${S}/bn`);return{conv:T,bn:C}}const I=qc(u);return{extractConvParams:y,extractConvWithBatchNormParams:g,extractSeparableConvParams:I}}function RD(r,l){const u=[],{extractConvParams:p,extractConvWithBatchNormParams:y,extractSeparableConvParams:g}=HQ(r,u);let I;if(l.withSeparableConvs){const S=l.filterSizes&&l.filterSizes.length||9;I={conv0:l.isFirstLayerConv2d?p("conv0"):g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:S>7?g("conv6"):void 0,conv7:S>8?g("conv7"):void 0,conv8:p("conv8")}}else I={conv0:y("conv0"),conv1:y("conv1"),conv2:y("conv2"),conv3:y("conv3"),conv4:y("conv4"),conv5:y("conv5"),conv6:y("conv6"),conv7:y("conv7"),conv8:p("conv8")};return Yn(r,u),{params:I,paramMappings:u}}var dx;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(dx||(dx={}));class vr{constructor({inputSize:r,scoreThreshold:l}={}){this._name="TinyYolov2Options";if(this._inputSize=r||416,this._scoreThreshold=l||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}const Mt=Je(Ze());class el extends Wn{constructor(r){super("TinyYolov2");hx(r),this._config=r}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(r,l){let u=Tr(r,l.conv0);return u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Tr(u,l.conv1),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Tr(u,l.conv2),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Tr(u,l.conv3),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Tr(u,l.conv4),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Tr(u,l.conv5),u=Mt.maxPool(u,[2,2],[1,1],"same"),u=Tr(u,l.conv6),u=Tr(u,l.conv7),ua(u,l.conv8,"valid",!1)}runMobilenet(r,l){let u=this.config.isFirstLayerConv2d?Qc(ua(r,l.conv0,"valid",!1)):Ar(r,l.conv0);return u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Ar(u,l.conv1),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Ar(u,l.conv2),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Ar(u,l.conv3),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Ar(u,l.conv4),u=Mt.maxPool(u,[2,2],[2,2],"same"),u=Ar(u,l.conv5),u=Mt.maxPool(u,[2,2],[1,1],"same"),u=l.conv6?Ar(u,l.conv6):u,u=l.conv7?Ar(u,l.conv7):u,ua(u,l.conv8,"valid",!1)}forwardInput(r,l){const{params:u}=this;if(!u)throw new Error("TinyYolov2 - load model before inference");return Mt.tidy(()=>{let p=Mt.cast(r.toBatchTensor(l,!1),"float32");return p=this.config.meanRgb?di(p,this.config.meanRgb):p,p=p.div(Mt.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(p,u):this.runTinyYolov2(p,u)})}async forward(r,l){return await this.forwardInput(await Wt(r),l)}async detect(r,l={}){const{inputSize:u,scoreThreshold:p}=new vr(l),y=await Wt(r),g=await this.forwardInput(y,u),I=Mt.tidy(()=>Mt.unstack(g)[0].expandDims()),S={width:y.getInputWidth(0),height:y.getInputHeight(0)},T=await this.extractBoxes(I,y.getReshapedInputDimensions(0),p);g.dispose(),I.dispose();const C=T.map(te=>te.box),D=T.map(te=>te.score),_=T.map(te=>te.classScore),A=T.map(te=>this.config.classes[te.label]),B=US(C.map(te=>te.rescale(u)),D,this.config.iouThreshold,!0),ne=B.map(te=>new Nc(D[te],_[te],A[te],C[te],S));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return RD(r,this.config)}extractParams(r){const l=this.config.filterSizes||el.DEFAULT_FILTER_SIZES,u=l?l.length:void 0;if(u!==7&&u!==8&&u!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${u} filterSizes in config`);return CD(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,u){const{width:p,height:y}=l,g=Math.max(p,y),I=g/p,S=g/y,T=r.shape[1],C=this.config.anchors.length,[D,_,A]=Mt.tidy(()=>{const P=r.reshape([T,T,C,this.boxEncodingSize]),ge=P.slice([0,0,0,0],[T,T,C,4]),ae=P.slice([0,0,0,4],[T,T,C,1]),Le=this.withClassScores?Mt.softmax(P.slice([0,0,0,5],[T,T,C,this.config.classes.length]),3):Mt.scalar(0);return[ge,ae,Le]}),B=[],ne=await _.array(),te=await D.array();for(let P=0;P<T;P++)for(let ge=0;ge<T;ge++)for(let ae=0;ae<C;ae++){const Le=yu(ne[P][ge][ae][0]);if(!u||Le>u){const ve=(ge+yu(te[P][ge][ae][0]))/T*I,Ve=(P+yu(te[P][ge][ae][1]))/T*S,at=Math.exp(te[P][ge][ae][2])*this.config.anchors[ae].x/T*I,pt=Math.exp(te[P][ge][ae][3])*this.config.anchors[ae].y/T*S,$t=ve-at/2,Vt=Ve-pt/2,qe={row:P,col:ge,anchor:ae},{classScore:ln,label:bt}=this.withClassScores?await this.extractPredictedClass(A,qe):{classScore:1,label:0};B.push({box:new gu($t,Vt,$t+at,Vt+pt),score:Le,classScore:Le*ln,label:bt,...qe})}}return D.dispose(),_.dispose(),A.dispose(),B}async extractPredictedClass(r,l){const{row:u,col:p,anchor:y}=l,g=await r.array();return Array(this.config.classes.length).fill(0).map((I,S)=>g[u][p][y][S]).map((I,S)=>({classScore:I,label:S})).reduce((I,S)=>I.classScore>S.classScore?I:S)}}el.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class Hu extends el{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:ID,classes:["face"]},r?{anchors:TD,meanRgb:AD}:{anchors:xD,withClassScores:!0});super(l)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Jt(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?ND:vD}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function qQ(r,l=!0){const u=new Hu(l);return u.extractWeights(r),u}class px extends vr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class Li{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const mx=Je(Ze());async function fa(r,l,u,p,y=({alignedRect:g})=>g){const g=r.map(T=>pa(T)?y(T):T.detection),I=p||(l instanceof mx.Tensor?await Vc(l,g):await zc(l,g)),S=await u(I);return I.forEach(T=>T instanceof mx.Tensor&&T.dispose()),S}async function tl(r,l,u,p,y){return fa([r],l,async g=>u(g[0]),p,y)}const OD=.4,ED=[new Qe(1.603231,2.094468),new Qe(6.041143,7.080126),new Qe(2.882459,3.518061),new Qe(4.266906,5.178857),new Qe(9.041765,10.66308)],DD=[117.001,114.697,97.404];class qu extends el{constructor(){const r={withSeparableConvs:!0,iouThreshold:OD,classes:["face"],anchors:ED,meanRgb:DD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(r)}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Jt(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const yt={ssdMobilenetv1:new Zc,tinyFaceDetector:new qu,tinyYolov2:new Hu,faceLandmark68Net:new Pu,faceLandmark68TinyNet:new ax,faceRecognitionNet:new Vu,faceExpressionNet:new nx,ageGenderNet:new ox},kD=(r,l)=>yt.ssdMobilenetv1.locateFaces(r,l),jQ=(r,l)=>yt.tinyFaceDetector.locateFaces(r,l),KQ=(r,l)=>yt.tinyYolov2.locateFaces(r,l),FD=r=>yt.faceLandmark68Net.detectLandmarks(r),XQ=r=>yt.faceLandmark68TinyNet.detectLandmarks(r),JQ=r=>yt.faceRecognitionNet.computeFaceDescriptor(r),ZQ=r=>yt.faceExpressionNet.predictExpressions(r),QQ=r=>yt.ageGenderNet.predictAgeAndGender(r),_D=r=>yt.ssdMobilenetv1.load(r),eee=r=>yt.tinyFaceDetector.load(r),tee=r=>yt.tinyYolov2.load(r),nee=r=>yt.faceLandmark68Net.load(r),see=r=>yt.faceLandmark68TinyNet.load(r),iee=r=>yt.faceRecognitionNet.load(r),ree=r=>yt.faceExpressionNet.load(r),oee=r=>yt.ageGenderNet.load(r),aee=_D,cee=kD,lee=FD;class WD extends Li{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class Xu extends WD{async run(){const r=await this.parentTask,l=await fa(r,this.input,async u=>await Promise.all(u.map(p=>yt.faceExpressionNet.predictExpressions(p))),this.extractedFaces);return r.map((u,p)=>Rg(u,l[p]))}withAgeAndGender(){return new ju(this,this.input)}}class Ju extends WD{async run(){const r=await this.parentTask;if(!r)return;const l=await tl(r,this.input,u=>yt.faceExpressionNet.predictExpressions(u),this.extractedFaces);return Rg(r,l)}withAgeAndGender(){return new Ku(this,this.input)}}class il extends Xu{withAgeAndGender(){return new nl(this,this.input)}withFaceDescriptors(){return new ga(this,this.input)}}class rl extends Ju{withAgeAndGender(){return new sl(this,this.input)}withFaceDescriptor(){return new ya(this,this.input)}}class $D extends Li{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class ju extends $D{async run(){const r=await this.parentTask,l=await fa(r,this.input,async u=>await Promise.all(u.map(p=>yt.ageGenderNet.predictAgeAndGender(p))),this.extractedFaces);return r.map((u,p)=>{const{age:y,gender:g,genderProbability:I}=l[p];return Fg(_g(u,g,I),y)})}withFaceExpressions(){return new Xu(this,this.input)}}class Ku extends $D{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:u,genderProbability:p}=await tl(r,this.input,y=>yt.ageGenderNet.predictAgeAndGender(y),this.extractedFaces);return Fg(_g(r,u,p),l)}withFaceExpressions(){return new Ju(this,this.input)}}class nl extends ju{withFaceExpressions(){return new il(this,this.input)}withFaceDescriptors(){return new ga(this,this.input)}}class sl extends Ku{withFaceExpressions(){return new rl(this,this.input)}withFaceDescriptor(){return new ya(this,this.input)}}class fx extends Li{constructor(r,l){super();this.parentTask=r;this.input=l}}class ga extends fx{async run(){const r=await this.parentTask,l=await fa(r,this.input,u=>Promise.all(u.map(p=>yt.faceRecognitionNet.computeFaceDescriptor(p))),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return l.map((u,p)=>kg(r[p],u))}withFaceExpressions(){return new il(this,this.input)}withAgeAndGender(){return new nl(this,this.input)}}class ya extends fx{async run(){const r=await this.parentTask;if(!r)return;const l=await tl(r,this.input,u=>yt.faceRecognitionNet.computeFaceDescriptor(u),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return kg(r,l)}withFaceExpressions(){return new rl(this,this.input)}withAgeAndGender(){return new sl(this,this.input)}}const Zu=Je(Ze());class gx extends Li{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=u}get landmarkNet(){return this.useTinyLandmarkNet?yt.faceLandmark68TinyNet:yt.faceLandmark68Net}}class yx extends gx{async run(){const r=await this.parentTask,l=r.map(y=>y.detection),u=this.input instanceof Zu.Tensor?await Vc(this.input,l):await zc(this.input,l),p=await Promise.all(u.map(y=>this.landmarkNet.detectLandmarks(y)));return u.forEach(y=>y instanceof Zu.Tensor&&y.dispose()),r.map((y,g)=>Xc(y,p[g]))}withFaceExpressions(){return new il(this,this.input)}withAgeAndGender(){return new nl(this,this.input)}withFaceDescriptors(){return new ga(this,this.input)}}class bx extends gx{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,u=this.input instanceof Zu.Tensor?await Vc(this.input,[l]):await zc(this.input,[l]),p=await this.landmarkNet.detectLandmarks(u[0]);return u.forEach(y=>y instanceof Zu.Tensor&&y.dispose()),Xc(r,p)}withFaceExpressions(){return new rl(this,this.input)}withAgeAndGender(){return new sl(this,this.input)}withFaceDescriptor(){return new ya(this,this.input)}}class wx extends Li{constructor(r,l=new bi){super();this.input=r;this.options=l}}class $g extends wx{async run(){const{input:r,options:l}=this,u=l instanceof px?p=>yt.tinyFaceDetector.locateFaces(p,l):l instanceof bi?p=>yt.ssdMobilenetv1.locateFaces(p,l):l instanceof vr?p=>yt.tinyYolov2.locateFaces(p,l):null;if(!u)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return u(r)}runAndExtendWithFaceDetections(){return new Promise(async r=>{const l=await this.run();return r(l.map(u=>Zo({},u)))})}withFaceLandmarks(r=!1){return new yx(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new Xu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ju(this.runAndExtendWithFaceDetections(),this.input)}}class Lx extends wx{async run(){const r=await new $g(this.input,this.options);let l=r[0];return r.forEach(u=>{u.score>l.score&&(l=u)}),l}runAndExtendWithFaceDetection(){return new Promise(async r=>{const l=await this.run();return r(l?Zo({},l):void 0)})}withFaceLandmarks(r=!1){return new bx(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new Ju(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ku(this.runAndExtendWithFaceDetection(),this.input)}}function hee(r,l=new bi){return new Lx(r,l)}function Ug(r,l=new bi){return new $g(r,l)}async function UD(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await Ug(r,new bi(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function uee(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await Ug(r,new vr(l)).withFaceLandmarks().withFaceDescriptors()}const dee=UD;function Sx(r,l){if(r.length!==l.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const u=Array.from(r),p=Array.from(l);return Math.sqrt(u.map((y,g)=>y-p[g]).reduce((y,g)=>y+Math.pow(g,2),0))}class BD{constructor(r,l=.6){this._distanceThreshold=l;const u=Array.isArray(r)?r:[r];if(!u.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let p=1;const y=()=>`person ${p++}`;this._labeledDescriptors=u.map(g=>{if(g instanceof Jo)return g;if(g instanceof Float32Array)return new Jo(y(),[g]);if(g.descriptor&&g.descriptor instanceof Float32Array)return new Jo(y(),[g.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(r,l){return l.map(u=>Sx(u,r)).reduce((u,p)=>u+p,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:u})=>new Ym(u,this.computeMeanDistance(r,l))).reduce((l,u)=>l.distance<u.distance?l:u)}findBestMatch(r){const l=this.matchDescriptor(r);return l.distance<this.distanceThreshold?l:new Ym("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(u=>Jo.fromJSON(u));return new BD(l,r.distanceThreshold)}}function pee(r){const l=new qu;return l.extractWeights(r),l}function MD(r,l){const{width:u,height:p}=new ms(l.width,l.height);if(u<=0||p<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:u,height:p})}`);if(Array.isArray(r))return r.map(y=>MD(y,{width:u,height:p}));if(pa(r)){const y=r.detection.forSize(u,p),g=r.unshiftedLandmarks.forSize(y.box.width,y.box.height);return Xc(Zo(r,y),g)}return $i(r)?Zo(r,r.detection.forSize(u,p)):r instanceof Gs||r instanceof Jt?r.forSize(u,p):r}var PD="0.8.6";const mee=Je(Ze()),fee=typeof process!="undefined",gee=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",yee={faceapi:PD,node:fee,browser:gee};export{ox as AgeGenderNet,gu as BoundingBox,_t as Box,Li as ComposableTask,ga as ComputeAllFaceDescriptorsTask,fx as ComputeFaceDescriptorsTaskBase,ya as ComputeSingleFaceDescriptorTask,yx as DetectAllFaceLandmarksTask,$g as DetectAllFacesTask,gx as DetectFaceLandmarksTaskBase,wx as DetectFacesTaskBase,bx as DetectSingleFaceLandmarksTask,Lx as DetectSingleFaceTask,ms as Dimensions,tx as FACE_EXPRESSION_LABELS,Jt as FaceDetection,GQ as FaceDetectionNet,nx as FaceExpressionNet,da as FaceExpressions,Pu as FaceLandmark68Net,ax as FaceLandmark68TinyNet,RQ as FaceLandmarkNet,Gs as FaceLandmarks,EJ as FaceLandmarks5,wu as FaceLandmarks68,Ym as FaceMatch,BD as FaceMatcher,Vu as FaceRecognitionNet,Ir as Gender,Hm as LabeledBox,Jo as LabeledFaceDescriptors,ho as NetInput,Wn as NeuralNetwork,Nc as ObjectDetection,Qe as Point,DJ as PredictedBox,bu as Rect,Zc as SsdMobilenetv1,bi as SsdMobilenetv1Options,qu as TinyFaceDetector,px as TinyFaceDetectorOptions,Hu as TinyYolov2,vr as TinyYolov2Options,dx as TinyYolov2SizeType,dee as allFaces,UD as allFacesSsdMobilenetv1,uee as allFacesTinyYolov2,qS as awaitMediaLoaded,jS as bufferToImage,JQ as computeFaceDescriptor,Rc as createCanvas,Su as createCanvasFromMedia,VQ as createFaceDetectionNet,DQ as createFaceRecognitionNet,SD as createSsdMobilenetv1,pee as createTinyFaceDetector,qQ as createTinyYolov2,Ug as detectAllFaces,FD as detectFaceLandmarks,XQ as detectFaceLandmarksTiny,lee as detectLandmarks,hee as detectSingleFace,ix as draw,St as env,Sx as euclideanDistance,Fg as extendWithAge,kg as extendWithFaceDescriptor,Zo as extendWithFaceDetection,Rg as extendWithFaceExpressions,Xc as extendWithFaceLandmarks,_g as extendWithGender,Vc as extractFaceTensors,zc as extractFaces,SQ as fetchImage,ZI as fetchJson,IQ as fetchNetWeights,ha as fetchOrThrow,is as getContext2dOrThrow,ea as getMediaDimensions,KS as imageTensorToCanvas,JI as imageToSquare,NJ as inverseSigmoid,WS as iou,Xm as isMediaElement,Lu as isMediaLoaded,kQ as isWithAge,$i as isWithFaceDetection,sx as isWithFaceExpressions,pa as isWithFaceLandmarks,FQ as isWithGender,oee as loadAgeGenderModel,aee as loadFaceDetectionModel,ree as loadFaceExpressionModel,nee as loadFaceLandmarkModel,see as loadFaceLandmarkTinyModel,iee as loadFaceRecognitionModel,_D as loadSsdMobilenetv1Model,eee as loadTinyFaceDetectorModel,tee as loadTinyYolov2Model,QI as loadWeightMap,cee as locateFaces,xQ as matchDimensions,$S as minBbox,yt as nets,US as nonMaxSuppression,di as normalize,BS as padToSquare,QQ as predictAgeAndGender,ZQ as recognizeFaceExpressions,MD as resizeResults,Qo as resolveInput,vJ as shuffleArray,yu as sigmoid,kD as ssdMobilenetv1,mee as tf,jQ as tinyFaceDetector,KQ as tinyYolov2,Wt as toNetInput,DS as utils,hx as validateConfig,yee as version};
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=face-api.esm.js.map
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