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

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Int32Array(n));if(t==="bool")return ys(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function Vn(){return C().platform.now()}function cy(e){e.forEach(t=>{k(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function $x(e,t){return C().platform.fetch(e,t)}function ly(e,t="utf-8"){return t=t||"utf-8",C().platform.encode(e,t)}function Dl(e,t="utf-8"){return t=t||"utf-8",C().platform.decode(e,t)}function Js(e,t,n){if(t===0)return 0;if(t===1)return e[0];let s=e[e.length-1];for(let i=0;i<e.length-1;++i)s+=n[i]*e[i];return s}function La(e,t,n){if(t===0)return[];if(t===1)return[e];const s=new Array(t);for(let i=0;i<s.length-1;++i)s[i]=Math.floor(e/n[i]),e-=s[i]*n[i];return s[s.length-1]=e,s}var wD=Object.freeze({__proto__:null,shuffle:ty,clamp:El,nearestLargerEven:ny,sum:Ox,randUniform:pD,distSquared:mD,assert:k,assertShapesMatch:dt,assertNonNull:ao,flatten:Yi,sizeFromShape:we,isScalarShape:fD,arraysEqual:ot,isInt:Ut,tanh:gD,sizeToSquarishShape:sd,createShuffledIndices:yD,rightPad:co,repeatedTry:sy,inferFromImplicitShape:id,parseAxisParam:ft,squeezeShape:Sr,getTypedArrayFromDType:bn,getArrayFromDType:lo,checkConversionForErrors:Ex,isValidDtype:Dx,hasEncodingLoss:iy,isTypedArray:wn,bytesPerElement:ry,bytesFromStringArray:kx,isString:Ir,isBoolean:Fx,isNumber:rd,inferDtype:ba,isFunction:xr,nearestDivisor:od,computeStrides:Ot,createScalarValue:_x,toTypedArray:Tr,toNestedArray:ys,makeOnesTypedArray:oy,makeZerosTypedArray:wa,makeZerosNestedTypedArray:ay,now:Vn,assertNonNegativeIntegerDimensions:cy,fetch:$x,encodeString:ly,decodeString:Dl,locToIndex:Js,indexToLoc:La});class LD{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new ID)}profileKernel(e,t,n){let 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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 LD(this.backendInstance),!0}setupRegisteredKernels(){const e=td(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=td(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 g)&&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 $l.nextTensorId++}nextVariableId(){return $l.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},h={dtype:a};return V.runKernelFunc(d=>d.cast(o,a),c,null,Jc,h)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${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,y=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=Zg(s,this.backendName);let L;if(w!=null)b=()=>{const A=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,A,N);const E=N.map(({dataId:D,shape:F,dtype:_})=>this.makeTensorFromDataId(D,F,_));if(d){let D=this.getTensorsForGradient(s,t,E);if(D==null){a==null&&(a=[]);const F=E.filter((_,B)=>a[B]);D=(o||[]).slice().concat(F)}h=this.saveTensorsForBackwardMode(D)}return E};else{const A=N=>{if(!d)return;h=N.map(E=>this.keep(this.clone(E)))};b=()=>{const N=this.backend.numDataIds();L=this.tidy(()=>e(this.backend,A));const E=Array.isArray(L)?L:[L];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,E),E}}let T;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(T=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(T),c=T.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-y,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(A=>t[A]!=null?t[A].shape:null),outputShapes:c.map(A=>A.shape),kernelTimeMs:T.timeMs,extraInfo:T.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=Qg(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(k(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"&&Ir(e[0])&&(i=e.map(c=>ly(c)));const o=s.write(i,t,n),a=new Q(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),h=kx(i);this.state.numBytes+=h-c.bytes,c.bytes=h}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new Q(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 Wl(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*ry(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 Wl||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=Qg(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=h=>(h=h.map((d,m)=>{if(d==null){const y=n[m],b=wa(y.size,y.dtype);return this.makeTensor(b,y.shape,y.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(k(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));k(i instanceof Q,()=>"The result y returned by f() must be a tensor.");const o=xD(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?kD(i.shape):n,TD(a,o,h=>this.tidy(h),FD);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 k(xr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{k(t.every(i=>i instanceof Q),()=>"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),k(n.value instanceof Q,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),k(xr(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];k(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(...)."),k(c.every(d=>d instanceof Q),()=>"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=Vn(),n=await this.backend.time(e);return n.wallMs=Vn()-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 Gx;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}}$l.nextTensorId=0,$l.nextVariableId=0;function kD(e){const t=oy(we(e),"float32");return V.makeTensor(t,e,"float32")}function Vx(){const e=ne();if(e._tfengine==null){const t=new S(e);e._tfengine=new $l(t)}return U(e._tfengine.ENV),ND(()=>e._tfengine),e._tfengine}const V=Vx();function FD(e,t){const n={a:e,b:t};return V.runKernelFunc((s,i)=>{const o=s.add(e,t);return i([e,t]),o},n,null,Te)}function _D(){return typeof navigator!="undefined"&&navigator!=null}function Yx(){if(_D()){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 fy(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var WD=Object.freeze({__proto__:null,isMobile:Yx,isBrowser:fy});const qi=C();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",()=>fy()),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(wn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];const s=[];for(;Array.isArray(n)||wn(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&C().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&Hx(e,s,[]),s}function Hx(e,t,n){if(n=n||[],!Array.isArray(e)&&!wn(e)){k(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}k(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),k(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);const s=t.slice(1);for(let i=0;i<e.length;++i)Hx(e[i],s,n.concat(i))}function qx(e,t,n,s){if(e==null)return;if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}function W(e,t,n,s="numeric"){if(e instanceof Q)return qx(s,e.dtype,t,n),e;let i=ba(e);if(i!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(i=s),qx(s,i,t,n),e==null||!wn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){const h=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${h}'`)}const o=Ii(e,i);!wn(e)&&!Array.isArray(e)&&(e=[e]);const a=!0,c=i!=="string"?Tr(e,i):Yi(e,[],a);return V.makeTensor(c,o,i)}function Ul(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 jx="__op";function P(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+jx;const i=(...o)=>{V.startScope(n);try{const a=s(...o);return a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),V.endScope(a),a}catch(a){throw V.endScope(null),a}};return Object.defineProperty(i,"name",{value:n,configurable:!0}),i}function $D(e,t){const n=W(e,"real","complex"),s=W(t,"imag","complex");dt(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);const i=a=>a.complex(n,s),o={real:n,imag:s};return V.runKernelFunc(i,o,null,hg)}const xi=P({complex_:$D});function vr(e,t,n,s){if(s==null&&(s=ba(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!wn(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){cy(t);const i=we(t),o=we(n);k(i===o,()=>`Based on the provided shape, [${t}], the tensor should have ${i} values but has ${o}`);for(let a=0;a<n.length;++a){const c=n[a],h=a===n.length-1?c!==we(t.slice(a)):!0;k(n[a]===t[a]||!h,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!wn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?Tr(e,s):Yi(e,[],!0),V.makeTensor(e,t,s)}function en(e,t,n){const s=Ii(e,n);return vr(e,t,s,n)}const gy={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};const hd=4;async function yy(e,t){const n=[],s=[],i=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);for(let a=0;a<i.length;++a){const c=i[a],h=Array.isArray(e)?e[a].tensor:e[c];if(h.dtype!=="float32"&&h.dtype!=="int32"&&h.dtype!=="bool"&&h.dtype!=="string"&&h.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${c}': ${h.dtype}`);const d={name:c,shape:h.shape,dtype:h.dtype};if(h.dtype==="string"){const m=new Promise(async y=>{const b=await h.bytes(),w=b.reduce((A,N)=>A+N.length,0)+hd*b.length,L=new Uint8Array(w);let T=0;for(let A=0;A<b.length;A++){const N=b[A],E=new Uint8Array(new Uint32Array([N.length]).buffer);L.set(E,T),T+=hd,L.set(N,T),T+=N.length}y(L)});s.push(m)}else s.push(h.data());t!=null&&(d.group=t),n.push(d)}const o=await Promise.all(s);return{data:UD(o),specs:n}}function ud(e,t){const n={};let s,i=0;for(const o of t){const a=o.name,c=o.dtype,h=o.shape,d=we(h);let m;if("quantization"in o){const y=o.quantization;if(y.dtype==="uint8"||y.dtype==="uint16"){if(!("min"in y&&"scale"in y))throw new Error(`Weight ${o.name} with quantization ${y.dtype} doesn't have corresponding metadata min and scale.`)}else if(y.dtype==="float16"){if(c!=="float32")throw new Error(`Weight ${o.name} is quantized with ${y.dtype} which only supports weights of type float32 not ${c}.`)}else throw new Error(`Weight ${o.name} has unknown quantization dtype ${y.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=gy[y.dtype],w=e.slice(i,i+d*b),L=y.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(y.dtype==="uint8"||y.dtype==="uint16"){m=new Float32Array(L.length);for(let T=0;T<L.length;T++){const A=L[T];m[T]=A*y.scale+y.min}}else if(y.dtype==="float16")s===void 0&&(s=VD()),m=s(L);else throw new Error(`Unsupported quantization type ${y.dtype} for weight type float32.`);else if(c==="int32"){if(y.dtype!=="uint8"&&y.dtype!=="uint16")throw new Error(`Unsupported quantization type ${y.dtype} for weight type int32.`);m=new Int32Array(L.length);for(let T=0;T<L.length;T++){const A=L[T];m[T]=Math.round(A*y.scale+y.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*b}else if(c==="string"){const y=we(o.shape);m=[];for(let b=0;b<y;b++){const w=new Uint32Array(e.slice(i,i+hd))[0];i+=hd;const L=new Uint8Array(e.slice(i,i+w));m.push(L),i+=w}}else{const y=gy[c],b=e.slice(i,i+d*y);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),L=new Float32Array(m.length/2);for(let N=0;N<w.length;N++)w[N]=m[N*2],L[N]=m[N*2+1];const T=en(w,h,"float32"),A=en(L,h,"float32");n[a]=xi(T,A)}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*y}c!=="complex64"&&(n[a]=en(m,h,c))}return n}function UD(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 by=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function Kx(e){return by?Buffer.byteLength(e):new Blob([e]).size}function BD(e){if(by)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 MD(e){if(by){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 dd(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 Xx(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 Bl(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:Kx(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:Kx(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function PD(){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 zD(){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 GD(){const e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function VD(){const e=PD(),t=zD(),n=GD();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 Xt{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Xt.instance==null&&(Xt.instance=new Xt),Xt.instance}static registerSaveRouter(e){Xt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Xt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Xt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Xt.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?Xt.getInstance().loadRouters:Xt.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const YD=e=>Xt.registerSaveRouter(e),HD=e=>Xt.registerLoadRouter(e),wy=e=>Xt.getSaveHandlers(e),Ly=(e,t)=>Xt.getLoadHandlers(e,t);const pd="tensorflowjs",Sy=1,ho="models_store",Nr="model_info_store";async function kZ(){const e=Iy();return new Promise((t,n)=>{const s=e.deleteDatabase(pd);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function Iy(){if(!C().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 xy(e){const t=e.result;t.createObjectStore(ho,{keyPath:"modelPath"}),t.createObjectStore(Nr,{keyPath:"modelPath"})}class uo{constructor(e){if(this.indexedDB=Iy(),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(pd,Sy);i.onupgradeneeded=()=>xy(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(ho,"readonly"),c=a.objectStore(ho),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=Bl(t),c=o.transaction(Nr,"readwrite");let h=c.objectStore(Nr);const d=h.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;d.onsuccess=()=>{m=o.transaction(ho,"readwrite");const y=m.objectStore(ho),b=y.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{h=c.objectStore(Nr);const L=h.delete(this.modelPath);L.onsuccess=()=>(o.close(),s(b.error)),L.onerror=T=>(o.close(),s(b.error))}},d.onerror=y=>(o.close(),s(d.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}uo.URL_SCHEME="indexeddb://";const Jx=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(uo.URL_SCHEME))?qD(e.slice(uo.URL_SCHEME.length)):null;Xt.registerSaveRouter(Jx),Xt.registerLoadRouter(Jx);function qD(e){return new uo(e)}function jD(e){return e.startsWith(uo.URL_SCHEME)?e.slice(uo.URL_SCHEME.length):e}class KD{constructor(){this.indexedDB=Iy()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(pd,Sy);n.onupgradeneeded=()=>xy(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(Nr,"readonly"),o=i.objectStore(Nr),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=jD(e),new Promise((t,n)=>{const s=this.indexedDB.open(pd,Sy);s.onupgradeneeded=()=>xy(s),s.onsuccess=()=>{const i=s.result,o=i.transaction(Nr,"readwrite"),a=o.objectStore(Nr),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(ho,"readwrite");const y=h.objectStore(ho),b=y.delete(e);b.onsuccess=()=>t(c.result.modelArtifactsInfo),b.onerror=w=>n(c.error)};d.onsuccess=m,d.onerror=y=>(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 Ti="/",po="tensorflowjs_models",Zx="info",XD="model_topology",JD="weight_specs",ZD="weight_data",QD="model_metadata";function FZ(){if(!C().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=po+Ti;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=eT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function Qx(e){return{info:[po,e,Zx].join(Ti),topology:[po,e,XD].join(Ti),weightSpecs:[po,e,JD].join(Ti),weightData:[po,e,ZD].join(Ti),modelMetadata:[po,e,QD].join(Ti)}}function eT(e){const t=e.split(Ti);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ti)}function ek(e){return e.startsWith(mo.URL_SCHEME)?e.slice(mo.URL_SCHEME.length):e}class mo{constructor(e){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=Qx(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=Bl(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,BD(e.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,userDefinedMetadata:e.userDefinedMetadata})),{modelArtifactsInfo:s}}catch(i){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=MD(o),t}}mo.URL_SCHEME="localstorage://";const tT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(mo.URL_SCHEME))?tk(e.slice(mo.URL_SCHEME.length)):null;Xt.registerSaveRouter(tT),Xt.registerLoadRouter(tT);function tk(e){return new mo(e)}class nk{constructor(){k(C().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),k(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=po+Ti,n=Ti+Zx;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=eT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=ek(e);const t=Qx(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${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 Ia="://";class bs{constructor(){this.managers={}}static getInstance(){return bs.instance==null&&(bs.instance=new bs),bs.instance}static registerManager(e,t){k(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Ia)&&(e=e.slice(0,e.indexOf(Ia))),k(e.length>0,()=>"scheme must not be an empty string.");const n=bs.getInstance();k(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 md(e){if(e.indexOf(Ia)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${bs.getSchemes().join(",")}`);return{scheme:e.split(Ia)[0],path:e.split(Ia)[1]}}async function nT(e,t,n=!1){k(e!==t,()=>`Old path and new path are the same: '${e}'`);const s=Xt.getLoadHandlers(e);k(s.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),k(s.length<2,()=>`Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);const i=s[0],o=Xt.getSaveHandlers(t);k(o.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),k(o.length<2,()=>`Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);const a=o[0],c=md(e).scheme,h=md(e).path,d=c===md(e).scheme,m=await i.load();n&&d&&await bs.getManager(c).removeModel(h);const y=await a.save(m);return n&&!d&&await bs.getManager(c).removeModel(h),y.modelArtifactsInfo}async function sk(){const e=bs.getSchemes(),t={};for(const n of e){const s=await bs.getManager(n).listModels();for(const i in s){const o=n+Ia+i;t[o]=s[i]}}return t}async function ik(e){const t=md(e),n=bs.getManager(t.scheme);return n.removeModel(t.path)}async function rk(e,t){const n=!1;return nT(e,t,n)}async function ok(e,t){const n=!0;return nT(e,t,n)}class ak{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(C().get("IS_BROWSER")){C().setPlatform("browser",new ak);try{bs.registerManager(mo.URL_SCHEME,new nk)}catch(e){}try{bs.registerManager(uo.URL_SCHEME,new KD)}catch(e){}}const ck={importFetch:()=>IC()};let xa;function _Z(){xa=null}function WZ(e){xa=e}function $Z(){return xa}class lk{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return C().global.fetch!=null?C().global.fetch(e,t):(xa==null&&(xa=ck.importFetch()),xa(e,t))}now(){const e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}}C().get("IS_NODE")&&C().setPlatform("node",new lk);function Ze(e,t="float32",n){return t=t||"float32",cy(e),new Ar(e,t,n)}function hk(e,t){const n=W(e,"x","cast");if(!Dx(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");const s={x:n},i={dtype:t};return V.runKernelFunc(o=>o.cast(n,t),s,null,Jc,i)}const ve=P({cast_:hk});function uk(e){const t=W(e,"x","clone",null),n=()=>V.makeTensorFromDataId(t.dataId,t.shape,t.dtype),s={x:t};return V.runKernelFunc(n,s,null,al)}const Cr=P({clone_:uk});function sT(e,t=!1){console.log(e.toString(t))}Vx();const dk={buffer:Ze,cast:ve,clone:Cr,print:sT};CD(dk);const pk="model",mk=".json",fk=".weights.bin";function iT(e){return new Promise(t=>setTimeout(t)).then(e)}class Ta{constructor(e){if(!C().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Ta.URL_SCHEME)&&(e=e.slice(Ta.URL_SCHEME.length)),(e==null||e.length===0)&&(e=pk),this.modelTopologyFileName=e+mk,this.weightDataFileName=e+fk}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");const t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],s={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),o=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(o.download=this.modelTopologyFileName,o.href=i,await iT(()=>o.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){const a=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;a.download=this.weightDataFileName,a.href=t,await iT(()=>a.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Bl(e)}}}}Ta.URL_SCHEME="downloads://";class gk{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){const e=this.files[0],t=this.files.slice(1);return new Promise((n,s)=>{const i=new FileReader;i.onload=o=>{const 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ql=P({batchToSpaceND_:FF});function _F(e){let t;return e.rank===0||e.rank===1?t=K(e,[1,1,1,e.size]):e.rank===2?t=K(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=K(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function WF(e,t,n,s,i,o){o==null&&(o=.001);const a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),h=W(n,"variance","batchNorm");let d;i!=null&&(d=W(i,"scale","batchNorm"));let m;s!=null&&(m=W(s,"offset","batchNorm")),k(c.rank===h.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k(m==null||c.rank===m.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k(d==null||c.rank===d.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");const y=_F(a),b=(A,N)=>(N([y,c,h,d]),A.batchNorm(y,vd(c),vd(h),vd(m),vd(d),o)),w={x:y,scale:d,offset:m,mean:c,variance:h},L={varianceEpsilon:o},T=V.runKernelFunc(b,w,null,ol,L);return K(T,a.shape)}function vd(e){return 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a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),h=W(n,"variance","batchNorm");let d;i!=null&&(d=W(i,"scale","batchNorm"));let m;return s!=null&&(m=W(s,"offset","batchNorm")),k(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),k(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${c.rank}.`),k(h.rank===3||h.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${h.rank}.`),d!=null&&k(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${d.rank}.`),m!=null&&k(m.rank===3||m.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${m.rank}.`),bo(a,c,h,m,d,o)}const RT=P({batchNorm3d_:UF});function BF(e,t,n,s,i,o){const a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),h=W(n,"variance","batchNorm");let d;i!=null&&(d=W(i,"scale","batchNorm"));let m;return s!=null&&(m=W(s,"offset","batchNorm")),k(a.rank===4,()=>`Error in 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B=(J,re)=>{const[ce,ue,he,de]=re,le=Xd(K(J,he.shape),he,o);let ye,pe;if(!n&&!s?(ye=at(le,ue,!1,!0),pe=at(ce,le,!0,!1)):!n&&s?(ye=at(le,ue,!1,!1),pe=at(le,ce,!0,!1)):n&&!s?(ye=at(ue,le,!1,!0),pe=at(ce,le,!1,!1)):(ye=at(ue,le,!0,!0),pe=at(le,ce,!0,!0)),i!=null){const Ie=Jd(de,le);return[ye,pe,Ie]}else return[ye,pe]},$=J=>{const re=J.fusedBatchMatMul({a:E,b:D,transposeA:n,transposeB:s,bias:F,activation:o,preluActivationWeights:_});return re},H={a:E,b:D,bias:F,preluActivationWeights:_},q={transposeA:n,transposeB:s,activation:o};if(i==null){const J=Ni((re,ce,ue)=>{const he=V.runKernelFunc($,H,null,Kg,q);return ue([re,ce,he]),{value:K(he,N),gradFunc:B}});return J(E,D)}else{const J=Ni((re,ce,ue,he)=>{const de=V.runKernelFunc($,H,null,Kg,q);return he([re,ce,de,ue]),{value:K(de,N),gradFunc:B}});return J(E,D,F)}}const ep=P({fusedMatMul_:IU});var xU=Object.freeze({__proto__:null,conv2d:vb,depthwiseConv2d:bA,matMul:ep});function TU(e){return Tb(e,.54,.46)}const AU=P({hammingWindow_:TU});function 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Ba(e,t,n,s,i,o){s==null&&(s=.5),i==null&&(i=Number.NEGATIVE_INFINITY),o==null&&(o=0);const a=e.shape[0];return n=Math.min(n,a),k(0<=s&&s<=1,()=>`iouThreshold must be in [0, 1], but was '${s}'`),k(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),k(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),k(t.rank===1,()=>"scores must be a 1D tensor"),k(t.shape[0]===a,()=>`scores has incompatible shape with boxes. Expected ${a}, but was ${t.shape[0]}`),k(0<=o&&o<=1,()=>`softNmsSigma must be in [0, 1], but was '${o}'`),{maxOutputSize:n,iouThreshold:s,scoreThreshold:i,softNmsSigma:o}}function WU(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY){const o=W(e,"boxes","nonMaxSuppression"),a=W(t,"scores","nonMaxSuppression"),c=Ba(o,a,n,s,i);n=c.maxOutputSize,s=c.iouThreshold,i=c.scoreThreshold;const h={maxOutputSize:n,iouThreshold:s,scoreThreshold:i};return V.runKernelFunc(d=>d.nonMaxSuppression(o,a,n,s,i),{boxes:o,scores:a},null,Og,h)}const $U=P({nonMaxSuppression_:WU});function UU(e,t,n){const s=BU(e,t,n),i=s<0?-(s+1):s;e.splice(i,0,t)}function BU(e,t,n){return PU(e,t,n||MU)}function MU(e,t){return e>t?1:e<t?-1:0}function PU(e,t,n){let s=0,i=e.length,o=0,a=!1;for(;s<i;){o=s+(i-s>>>1);const c=n(t,e[o]);c>0?s=o+1:(i=o,a=!c)}return a?s:-s-1}function tp(e,t,n,s,i){return Nb(e,t,n,s,i,0).selectedIndices}function np(e,t,n,s,i,o){return Nb(e,t,n,s,i,0,!1,o,!0)}function sp(e,t,n,s,i,o){return 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s=e.subarray(t*4,t*4+4),i=e.subarray(n*4,n*4+4),o=Math.min(s[0],s[2]),a=Math.min(s[1],s[3]),c=Math.max(s[0],s[2]),h=Math.max(s[1],s[3]),d=Math.min(i[0],i[2]),m=Math.min(i[1],i[3]),y=Math.max(i[0],i[2]),b=Math.max(i[1],i[3]),w=(c-o)*(h-a),L=(y-d)*(b-m);if(w<=0||L<=0)return 0;const T=Math.max(o,d),A=Math.max(a,m),N=Math.min(c,y),E=Math.min(h,b),D=Math.max(N-T,0)*Math.max(E-A,0);return D/(w+L-D)}function GU(e,t,n){const s=Math.exp(t*n*n);return n<=e?s:0}function SA(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function VU(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY){const o=W(e,"boxes","nonMaxSuppressionAsync"),a=W(t,"scores","nonMaxSuppressionAsync"),c=Ba(o,a,n,s,i);n=c.maxOutputSize,s=c.iouThreshold,i=c.scoreThreshold;const h=await Promise.all([o.data(),a.data()]),d=h[0],m=h[1],y=tp(d,m,n,s,i);return o!==e&&o.dispose(),a!==t&&a.dispose(),y}const YU=VU;function HU(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=0){const a=W(e,"boxes","nonMaxSuppression"),c=W(t,"scores","nonMaxSuppression"),h=Ba(a,c,n,s,i,o);n=h.maxOutputSize,s=h.iouThreshold,i=h.scoreThreshold,o=h.softNmsSigma;const d={boxes:a,scores:c},m={maxOutputSize:n,iouThreshold:s,scoreThreshold:i,softNmsSigma:o},y=V.runKernel(qu,d,m);return{selectedIndices:y[0],selectedScores:y[1]}}const qU=P({nonMaxSuppressionWithScore_:HU});async function jU(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=0){const a=W(e,"boxes","nonMaxSuppressionAsync"),c=W(t,"scores","nonMaxSuppressionAsync"),h=Ba(a,c,n,s,i,o);n=h.maxOutputSize,s=h.iouThreshold,i=h.scoreThreshold,o=h.softNmsSigma;const d=await Promise.all([a.data(),c.data()]),m=d[0],y=d[1],b=sp(m,y,n,s,i,o);return a!==e&&a.dispose(),c!==t&&c.dispose(),b}const KU=jU;function XU(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=!1){const a=W(e,"boxes","nonMaxSuppression"),c=W(t,"scores","nonMaxSuppression"),h=Ba(a,c,n,s,i,null),d=h.maxOutputSize,m=h.iouThreshold,y=h.scoreThreshold,b={boxes:a,scores:c},w={maxOutputSize:d,iouThreshold:m,scoreThreshold:y,padToMaxOutputSize:o},L=V.runKernel(Hu,b,w);return{selectedIndices:L[0],validOutputs:L[1]}}const JU=P({nonMaxSuppressionPadded_:XU});async function ZU(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=!1){const a=W(e,"boxes","nonMaxSuppressionAsync"),c=W(t,"scores","nonMaxSuppressionAsync"),h=Ba(a,c,n,s,i,null),d=h.maxOutputSize,m=h.iouThreshold,y=h.scoreThreshold,[b,w]=await Promise.all([a.data(),c.data()]),L=np(b,w,d,m,y,o);return a!==e&&a.dispose(),c!==t&&c.dispose(),L}const QU=ZU;function eB(e,t,n=!1){const s=W(e,"images","resizeBilinear");k(s.rank===3||s.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${s.rank}.`),k(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=K(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const[a,c]=t,h=(b,w)=>(w([i]),b.resizeBilinear(i,a,c,n)),d={images:i},m={alignCorners:n,size:t},y=V.runKernelFunc(h,d,null,Ug,m);return o?K(y,[y.shape[1],y.shape[2],y.shape[3]]):y}const IA=P({resizeBilinear_:eB});function tB(e,t,n=!1){const s=W(e,"images","resizeNearestNeighbor");k(s.rank===3||s.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${s.rank}.`),k(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),k(s.dtype==="float32"||s.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let i=s,o=!1;s.rank===3&&(o=!0,i=K(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const[a,c]=t,h={images:i},d={alignCorners:n,size:t},m=(b,w)=>(w([i]),b.resizeNearestNeighbor(i,a,c,n)),y=V.runKernelFunc(m,h,null,$g,d);return o?K(y,[y.shape[1],y.shape[2],y.shape[3]]):y}const xA=P({resizeNearestNeighbor_:tB});function nB(e,t,n){k(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),k(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);const s=W(e,"a","bandPart");k(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);const i=s.shape,[o,a]=s.shape.slice(-2);if(!(t<=o))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${o}).`);if(!(n<=a))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${a}).`);t<0&&(t=o),n<0&&(n=a);const c=K(sh(0,o,1,"int32"),[-1,1]),h=sh(0,a,1,"int32"),d=Ce(c,h),m=Ws(Dr(d,Ne(+t,"int32")),Ki(d,Ne(-n,"int32"))),y=ct([o,a],s.dtype);return K(is(Oi(K(s,[-1,o,a])).map(b=>_n(m,b,y))),i)}const sB=P({bandPart_:nB});function iB(e){let t;if(Array.isArray(e)){t=!1,k(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const i=e[0].shape[0];for(let o=1;o<e.length;++o)k(e[o].shape[0]===i,()=>`Gram-Schmidt: Non-unique lengths found in 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n=e.shape[0],s=e.shape[1];let i=Ed(n),o=Cr(e);const a=_r([[1]],[1,1]);let c=Cr(a);const h=n>=s?s:n;for(let d=0;d<h;++d){const m=o,y=c,b=i;[c,o,i]=V.tidy(()=>{const w=nt(o,[d,d],[n-d,1]),L=Kd(w),T=nt(o,[d,d],[1,1]),A=_n(Ss(T,0),_r([[-1]]),_r([[1]])),N=Ce(T,X(A,L)),E=_e(w,N);E.shape[0]===1?c=Cr(a):c=Mt([a,nt(E,[1,0],[E.shape[0]-1,E.shape[1]])],0);const D=Pt(_e(at(A,N),L)),F=nt(o,[d,0],[n-d,s]),_=X(D,c),B=Me(c);if(d===0)o=Ce(F,at(_,at(B,F)));else{const q=Ce(F,at(_,at(B,F)));o=Mt([nt(o,[0,0],[d,s]),q],0)}const $=Me(_),H=nt(i,[0,d],[n,i.shape[1]-d]);if(d===0)i=Ce(H,at(at(H,c),$));else{const q=Ce(H,at(at(H,c),$));i=Mt([nt(i,[0,0],[n,d]),q],1)}return[c,o,i]}),He([m,y,b])}return!t&&n>s&&(i=nt(i,[0,0],[n,s]),o=nt(o,[0,0],[s,s])),[i,o]})}const aB=P({qr_:oB});(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(r.Reduction||(r.Reduction={}));function cB(e,t,n=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const s=W(e,"losses","computeWeightedLoss");let i=null;t!=null&&(i=W(t,"weights","computeWeightedLoss"));const o=i==null?s:X(s,i);if(n===r.Reduction.NONE)return o;if(n===r.Reduction.SUM)return Ue(o);if(n===r.Reduction.MEAN){if(i==null)return zt(o);{const a=s.size/i.size,c=_e(Ue(o),Ue(i));return a>1?_e(c,Ne(a)):c}}if(n===r.Reduction.SUM_BY_NONZERO_WEIGHTS){if(i==null)return _e(Ue(o),Ne(s.size));{const a=X(i,Qs(s.shape)),c=ve(Ue(kr(a,Ne(0))),"float32");return _e(Ue(o),c)}}throw Error(`Unknown reduction: ${n}`)}const Xi=P({computeWeightedLoss_:cB});function lB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","absoluteDifference"),o=W(t,"predictions","absoluteDifference");let a=null;n!=null&&(a=W(n,"weights","absoluteDifference")),dt(i.shape,o.shape,"Error in absoluteDifference: ");const c=sn(Ce(i,o));return Xi(c,a,s)}const hB=P({absoluteDifference_:lB});function uB(e,t,n,s,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","cosineDistance"),a=W(t,"predictions","cosineDistance");let c=null;s!=null&&(c=W(s,"weights","cosineDistance")),dt(o.shape,a.shape,"Error in cosineDistance: ");const h=Ne(1),d=Ce(h,Ue(X(o,a),n,!0));return Xi(d,c,i)}const dB=P({cosineDistance_:uB});function pB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=W(e,"labels","hingeLoss");const o=W(t,"predictions","hingeLoss");let a=null;n!=null&&(a=W(n,"weights","hingeLoss")),dt(i.shape,o.shape,"Error in hingeLoss: ");const c=Ne(1);i=Ce(X(Ne(2),i),c);const h=Ri(Ce(c,X(i,o)));return Xi(h,a,s)}const mB=P({hingeLoss_:pB});function fB(e,t,n,s=1,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","huberLoss"),a=W(t,"predictions","huberLoss");let c=null;n!=null&&(c=W(n,"weights","huberLoss")),dt(o.shape,a.shape,"Error in huberLoss: ");const h=Ne(s),d=sn(Ce(a,o)),m=Io(d,h),y=Ce(d,m),b=be(X(Ne(.5),wt(m)),X(h,y));return Xi(b,c,i)}const gB=P({huberLoss_:fB});function yB(e,t,n,s=1e-7,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","logLoss"),a=W(t,"predictions","logLoss");let c=null;n!=null&&(c=W(n,"weights","logLoss")),dt(o.shape,a.shape,"Error in logLoss: ");const h=Ne(1),d=Ne(s),m=Pt(X(o,ts(be(a,d)))),y=X(Ce(h,o),ts(be(Ce(h,a),d))),b=Ce(m,y);return Xi(b,c,i)}const bB=P({logLoss_:yB});function wB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","meanSquaredError"),o=W(t,"predictions","meanSquaredError");let a=null;n!=null&&(a=W(n,"weights","meanSquaredError")),dt(i.shape,o.shape,"Error in meanSquaredError: ");const c=ah(i,o);return Xi(c,a,s)}const LB=P({meanSquaredError_:wB});function SB(e,t){const n=W(e,"labels","sigmoidCrossEntropyWithLogits"),s=W(t,"logits","sigmoidCrossEntropyWithLogits");dt(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const i=Ri(s),o=X(s,n),a=kd(Ls(Pt(sn(s))));return be(Ce(i,o),a)}function IB(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"multiClassLabels","sigmoidCrossEntropy");const a=W(t,"logits","sigmoidCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","sigmoidCrossEntropy")),dt(o.shape,a.shape,"Error in sigmoidCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(.5);o=be(X(o,Ce(m,d)),X(y,d))}const h=SB(o,a);return Xi(h,c,i)}const xB=P({sigmoidCrossEntropy_:IB});function TB(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);const s=Ni((i,o,a)=>{const c=!0,h=ab(o,[n],c),d=Ce(ve(o,"float32"),h);a([i,d]);const m=Pt(X(d,i)),y=Ue(m,[n]),b=(w,L)=>{const[T,A]=L,N=Rn(w.shape,[n]);return[X(K(w,N),Ce(ve(T,"float32"),Ls(A))),X(K(w,N),Ce(Ls(A),ve(T,"float32")))]};return{value:y,gradFunc:b}});return s(e,t)}function AB(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"onehotLabels","softmaxCrossEntropy");const a=W(t,"logits","softmaxCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","softmaxCrossEntropy")),dt(o.shape,a.shape,"Error in softmaxCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(o.shape[1]);o=be(X(o,Ce(m,d)),_e(d,y))}const h=TB(o,a);return Xi(h,c,i)}const vB=P({softmaxCrossEntropy_:AB});const NB={fft:rh,ifft:Wa,rfft:oh,irfft:Hd},CB={hammingWindow:AU,hannWindow:wA,frame:LA,stft:RU},Wr={flipLeftRight:kU,resizeNearestNeighbor:xA,resizeBilinear:IA,rotateWithOffset:_U,cropAndResize:EU,nonMaxSuppression:$U,nonMaxSuppressionAsync:YU,nonMaxSuppressionWithScore:qU,nonMaxSuppressionWithScoreAsync:KU,nonMaxSuppressionPadded:JU,nonMaxSuppressionPaddedAsync:QU},AA={bandPart:sB,gramSchmidt:rB,qr:aB},RB={absoluteDifference:hB,computeWeightedLoss:Xi,cosineDistance:dB,hingeLoss:mB,huberLoss:gB,logLoss:bB,meanSquaredError:LB,sigmoidCrossEntropy:xB,softmaxCrossEntropy:vB};class Ji extends go{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 ob(e,t)}dispose(){this.iterations_!=null&&He(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ne(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(Ji,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class lh extends Ji{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:ee(()=>Qe(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,h=this.accumulatedUpdates[s].variable;ee(()=>{const d=be(X(c,this.rho),X(wt(a),1-this.rho)),m=X(_e(Ln(be(h,this.epsilon)),Ln(be(c,this.epsilon))),a),y=be(X(h,this.rho),X(wt(m),1-this.rho));c.assign(d),h.assign(y);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(He(this.accumulatedGrads.map(e=>e.variable)),He(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}lh.className="Adadelta",me(lh);class hh extends Ji{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:ee(()=>Xl(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;ee(()=>{const c=be(a,wt(o));a.assign(c);const h=be(X(_e(o,Ln(be(c,V.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)}}hh.className="Adagrad",me(hh);class uh extends Ji{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ee(()=>{this.accBeta1=Ne(t).variable(),this.accBeta2=Ne(n).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=Ce(1,this.accBeta2);t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:ee(()=>Qe(a).variable(c))}),this.accumulatedSecondMoment[o]==null&&(this.accumulatedSecondMoment[o]={originalName:`${i}/v`,variable:ee(()=>Qe(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,y=be(X(d,this.beta1),X(h,1-this.beta1)),b=be(X(m,this.beta2),X(wt(h),1-this.beta2)),w=_e(y,n),L=_e(b,s);d.assign(y),m.assign(b);const T=be(X(_e(w,be(Ln(L),this.epsilon)),-this.learningRate),a);a.assign(T)}),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),ee(()=>{this.accBeta1.assign(ei(this.beta1,this.iterations_+1)),this.accBeta2.assign(ei(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)}}uh.className="Adam",me(uh);class dh extends Ji{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=[],ee(()=>{this.iteration=Ne(0).variable(),this.accBeta1=Ne(t).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=_e(-this.learningRate,be(X(this.iteration,this.decay),1));t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:Qe(a).variable(c)}),this.accumulatedWeightedInfNorm[o]==null&&(this.accumulatedWeightedInfNorm[o]={originalName:`${i}/v`,variable:Qe(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,y=be(X(d,this.beta1),X(h,1-this.beta1)),b=X(m,this.beta2),w=sn(h),L=_s(b,w);d.assign(y),m.assign(L);const T=be(X(_e(s,n),_e(y,be(L,this.epsilon))),a);a.assign(T)}),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)}}dh.className="Adamax",me(dh);class Ma extends Ji{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=V.registeredVariables[n];ee(()=>{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=Nn(Ne(-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)}}Ma.className="SGD",me(Ma);class ph extends Ma{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ne(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulations[s]==null){const c=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:ee(()=>Qe(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;ee(()=>{let c;const h=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(h,this.m))),i):c=be(X(this.c,h),i),o.assign(h),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&He(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}ph.className="Momentum",me(ph);class mh extends Ji{constructor(e,t=.9,n=0,s=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,s==null&&(this.epsilon=V.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:ee(()=>Qe(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;ee(()=>{const d=be(X(c,this.decay),X(wt(a),1-this.decay));if(this.centered){const m=this.accumulatedMeanGrads[s].variable,y=be(X(m,this.decay),X(a,1-this.decay)),b=_e(X(a,this.learningRate),Ln(Ce(d,be(wt(y),this.epsilon)))),w=be(X(h,this.momentum),b);c.assign(d),m.assign(y),h.assign(w);const L=Ce(i,w);i.assign(L)}else{const m=be(X(c,this.decay),X(wt(a),1-this.decay)),y=be(X(h,this.momentum),_e(X(a,this.learningRate),Ln(be(m,this.epsilon))));c.assign(m),h.assign(y);const b=Ce(i,y);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)}}mh.className="RMSProp",me(mh);class Co{static sgd(e){return new Ma(e)}static momentum(e,t,n=!1){return new ph(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,i=!1){return new mh(e,t,n,s,i)}static adam(e=.001,t=.9,n=.999,s=null){return new uh(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new lh(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,i=0){return new dh(e,t,n,s,i)}static adagrad(e,t=.1){return new hh(e,t)}}const Ro={sgd:Co.sgd,momentum:Co.momentum,adadelta:Co.adadelta,adagrad:Co.adagrad,rmsprop:Co.rmsprop,adamax:Co.adamax,adam:Co.adam};const OB=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function ip(){return new Promise(e=>OB(()=>e()))}function Cb(e,t,n){const s=n*(typeof e=="number"?e:e[0]),i=t*(typeof e=="number"?e:e[1]);return[s,i]}function fh(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 gh(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 yh(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 Rb(e,t){const n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function Ob(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 rp=1.7580993408473768,op=1.0507009873554805;const Eb=.3275911,Db=.254829592,kb=-.284496736,Fb=1.421413741,_b=-1.453152027,Wb=1.061405429;function Pa(...e){C().getBool("IS_TEST")||console.warn(...e)}function EB(...e){C().getBool("IS_TEST")||console.log(...e)}function Zi(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 vA(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 NA(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 CA(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 $b(e,t){const n=e[t*2],s=e[t*2+1];return{real:n,imag:s}}function RA(e,t,n,s){e[s*2]=t,e[s*2+1]=n}function OA(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 EA(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 DA(e,t,n){if(t==="complex64"){if(e.dtype==="complex64")return e.clone();const s=ct(e.shape),i=ve(e,"float32"),o=n.complex(i,s);return s.dispose(),i.dispose(),o}if(!iy(e.dtype,t))return V.makeTensorFromDataId(e.dataId,e.shape,t);if(e.dtype==="complex64"){const s=n.real(e),i=ve(s,t);return s.dispose(),i}if(t==="int32")return n.int(e);if(t==="bool"){const s=Ne(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 ${t}`)}function kA(e,t){return V.makeTensorFromDataId(e.dataId,t,e.dtype)}function Ub(e,t,n){const s=(t-e)/(n-1),i=wa(n,"float32");i[0]=e;for(let o=1;o<i.length;o++)i[o]=i[o-1]+s;return ns(i,"float32")}var Bb=Object.freeze({__proto__:null,slice_util:ST,segment_util:T_,castTensor:DA,reshapeTensor:kA,linspaceImpl:Ub,upcastType:vn,axesAreInnerMostDims:By,combineLocations:NT,computeOutAndReduceShapes:Cn,expandShapeToKeepDim:Rn,assertAxesAreInnerMostDims:es,getAxesPermutation:kn,getUndoAxesPermutation:Ml,getInnerMostAxes:ws,getBroadcastDims:Lo,getReductionAxes:on,assertAndGetBroadcastShape:tt,assertParamsConsistent:Ky,computeOutShape:Or,computeDilation2DInfo:Td,computePool2DInfo:Fn,computePool3DInfo:Gl,computeConv2DInfo:Ai,computeConv3DInfo:Vl,computeDefaultPad:Hy,tupleValuesAreOne:Rr,eitherStridesOrDilationsAreOne:rn,convertConv2DDataFormat:Yl,getFusedDyActivation:Xd,getFusedBiasGradient:Jd,applyActivation:Zd,shouldFuse:Qd,PARALLELIZE_THRESHOLD:ib,computeOptimalWindowSize:Jl,getImageCenter:Cb,getReshaped:fh,getPermuted:gh,getReshapedPermuted:yh,getSliceBeginCoords:Rb,getSliceSize:Ob,prepareAndValidate:gd,validateUpdateShape:Cy,validateInput:Ry,calculateShapes:va,SELU_SCALEALPHA:rp,SELU_SCALE:op,ERF_P:Eb,ERF_A1:Db,ERF_A2:kb,ERF_A3:Fb,ERF_A4:_b,ERF_A5:Wb,warn:Pa,log:EB,mergeRealAndImagArrays:Zi,splitRealAndImagArrays:vA,complexWithEvenIndex:NA,complexWithOddIndex:CA,getComplexWithIndex:$b,assignToTypedArray:RA,exponents:OA,exponent:EA,prepareSplitSize:jT});function 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Kn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Kn.className}}mp.className="GlorotNormal",me(mp);class fp extends Kn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Kn.className}}fp.className="HeNormal",me(fp);class gp extends Kn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Kn.className}}gp.className="HeUniform",me(gp);class yp extends Kn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Kn.className}}yp.className="LeCunNormal",me(yp);class bp extends Kn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Kn.className}}bp.className="LeCunNormal",me(bp);class aw extends Us{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 ze("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return ee(()=>{if(e.length<2)throw new ze("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=up(n,0,1,"float32");let i=AA.gramSchmidt(s);return e[0]>e[1]&&(i=i.transpose()),X(this.gain,i)})}getConfig(){return{gain:this.gain,seed:this.seed}}}aw.className="Orthogonal",me(aw);const nv={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 bh(e,ks.getMap().classNameMap,t,"initializer")}function Vt(e){return Gb(e)}function Ft(e){if(typeof e=="string"){const t=e in nv?nv[e]:e;if(t==="GlorotNormal")return new mp;if(t==="GlorotUniform")return new pp;if(t==="HeNormal")return new fp;if(t==="HeUniform")return new gp;if(t==="LeCunNormal")return new yp;if(t==="LeCunUniform")return new bp;{const n={};return n.className=t,n.config={},sv(n)}}else return e instanceof Us?e:sv(e)}function iz(){return new tw}function rz(){return new dp}function oz(e){return new nw(e)}function az(e){return new sw(e)}function cz(e){return new iw(e)}function lz(e){return new rw(e)}function hz(e){return new ow(e)}function uz(e){return new Kn(e)}function dz(e){return new pp(e)}function pz(e){return new mp(e)}function mz(e){return new fp(e)}function fz(e){return new gp(e)}function gz(e){return new yp(e)}function yz(e){return new bp(e)}function bz(e){return new aw(e)}var wz=Object.freeze({__proto__:null,zeros:iz,ones:rz,constant:oz,randomUniform:az,randomNormal:cz,truncatedNormal:lz,identity:hz,varianceScaling:uz,glorotUniform:dz,glorotNormal:pz,heNormal:mz,heUniform:fz,leCunNormal:gz,leCunUniform:yz,orthogonal:bz});let Lz=0;function iv(){return Lz++}const wp={};function Lp(e=""){return e in wp||(wp[e]=0),wp[e]+=1,e+wp[e].toString()}function cw(e){return Array.isArray(e)&&Array.isArray(e[0])}function Sp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function je(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new j(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function 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e.write(be(e.read(),t))}function gQ(e,t){return e.write(Ce(e.read(),t))}function lw(e){return e.map(t=>t.read())}function hw(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function yQ(e,t){const n=t.map(i=>i.read()),s=ob(e,n);return t.map(i=>s.grads[i.name])}class mn{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 ri{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=KA(o),this.name=XA(this.originalName)),this.rank=t.length}}let Iz=0;class xp{constructor(e,t){this.callArgs=t,this.id=Iz++,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 xz=0;class lt extends go{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=xz++,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=er(n)+"_"+Lp(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 ni(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return jn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return jn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Qi(`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 Qi(`Layer ${this.name} is not connected, no input to return.`);return jn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new Qi(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Qi(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return jn(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=Nt(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=Nt(this.inputSpec);if(e.length!==t.length)throw new j(`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 j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${o}`);if(i.maxNDim!=null&&o>i.maxNDim)throw new j(`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 j(`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 j(`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 j(`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 j(`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=Nt(e);let s=!0;for(const o of n)if(!(o instanceof ri)){s=!1;break}let i=!0;for(const o of n)if(o instanceof ri){i=!1;break}if(s===i)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Do(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of Nt(e))o.push(a.shape);this.build(jn(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=Nt(o),c=[];for(let h of a)n.indexOf(h)!==-1&&(h=h.clone()),c.push(h);if(o=jn(c),this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}else{const o=Tz(e),a=this.computeOutputShape(o);let c;const h=Az(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 ri(h,d,this,Nt(e),t,this.name,m)):c=new ri(h,a,this,Nt(e),t,this.name),this.addInboundNode(e,c,null,null,o,a,t),this._refCount++,this.activityRegularizer!=null)throw new ze("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 Qi(`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 Qi(`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 ni(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Ip(this.weights)}build(e){this.built=!0}getWeights(e=!1){return lw(e?this.trainableWeights:this.weights)}setWeights(e){ee(()=>{const t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${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=lw(t);for(let i=0;i<s.length;++i){const o=s[i],a=t[i],c=e[i];if(!ot(o.shape,c.shape))throw new j(`Layer weight shape ${o.shape} not compatible with provided weight shape ${c.shape}`);n.push([a,c])}hw(n)})}addWeight(e,t,n,s,i,o,a){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Ft("zeros"));const c=s.apply(t,n),h=new ii(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=Nt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=Nt(e);t=Nt(t),n=Nt(n),s=Nt(s),i=Sp(i),o=Sp(o);const h=[],d=[],m=[];for(const y of c)h.push(y.sourceLayer),d.push(y.nodeIndex),m.push(y.tensorIndex);new xp({outboundLayer:this,inboundLayers:h,nodeIndices:d,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let y=0;y<t.length;y++)t[y].sourceLayer=this,t[y].nodeIndex=this.inboundNodes.length-1,t[y].tensorIndex=y}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 Tz(e){e=Nt(e);const t=[];for(const n of e)t.push(n.shape);return jn(t)}function Az(e){return"float32"}function ov(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=ov(a,c,h);for(const m of d)i.indexOf(m)===-1&&i.push(m)}return i}}}class Ya extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Lp("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 j("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 j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("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 ri(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new xp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`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}}}Ya.className="InputLayer",me(Ya);function av(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new j("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;n==null&&(n="float32");const s=new Ya({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}),i=s.inboundNodes[0].outputTensors;return i[0]}async function Mr(e){if(e==null)return;const t=[],n=[],s=[];for(const i in e){const o=e[i];if(typeof o!="number"){const a=o;t.push(a.data()),n.push(i),s.push(a)}}if(t.length>0){const i=await Promise.all(t);for(let o=0;o<i.length;++o)e[n[o]]=i[o][0];He(s)}}function cv(e){if(e==null)return;for(const t in e){const n=e[t];typeof n!="number"&&n.dispose()}}var lv;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(lv||(lv={}));const vz=125;class Ha{constructor(){this.validationData=null}setParams(e){this.params=e}async onEpochBegin(e,t){}async onEpochEnd(e,t){}async onBatchBegin(e,t){}async onBatchEnd(e,t){}async onTrainBegin(e){}async onTrainEnd(e){}setModel(e){}}class hv{constructor(e,t=10){e==null&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(const t of this.callbacks)t.setParams(e)}setModel(e){for(const t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onBatchEnd(e,t)}async onTrainBegin(e){e==null&&(e={});for(const t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){e==null&&(e={});for(const t of this.callbacks)await t.onTrainEnd(e)}}class Nz extends Ha{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});const n=t.size==null?0:t.size;this.seen+=n;for(const s in t){const i=t[s];if(typeof i=="number")this.totals.hasOwnProperty(s)||(this.totals[s]=0),this.totals[s]=this.totals[s]+i*n;else{let o;s in this.totals?o=this.totals[s]:this.totals[s]=0;const a=ee(()=>be(this.totals[s],X(i,n)));this.totals[s]=a,o!=null&&o.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(const n of this.params.metrics){if(this.totals[n]==null)continue;typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:ee(()=>{const s=X(_e(1,this.seen),this.totals[n]);t[n]=s,this.totals[n].dispose(),Nn(t[n])})}}}class uv extends Ha{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(const n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){const e=[],t=[],n=[];for(const i in this.history){const o=this.history[i];for(let a=0;a<o.length;++a)if(typeof o[a]!="number"){const c=o[a];e.push(c.data()),t.push(i),n.push(a)}}const s=await Promise.all(e);for(let i=0;i<s.length;++i){const o=this.history[t[i]][n[i]];o.dispose(),this.history[t[i]][n[i]]=s[i][0]}}}class dv extends Ha{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=vz),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. 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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 ki extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=Lp(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],$r(this.inputs).length!==this.inputs.length)throw new j(`The list of inputs passed to the model is redundant. 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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 E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;this.outputLayers.push(E),this.outputLayersNodeIndices.push(D),this.outputLayersTensorIndices.push(F)}for(const N of this.inputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;xs(D===0,"input layer has >1 nodes"),xs(F===0,"input layer has >1 tensors"),this.inputLayers.push(E),this.inputLayersNodeIndices.push(D),this.inputLayersTensorIndices.push(F)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let N=0;N<this.inputLayers.length;N++){const E=this.inputLayers[N];if(!(E instanceof Ya))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${N} (0-based) originates from layer type ${E.getClassName()}.`);this.inputNames.push(E.name),this.feedInputShapes.push(E.batchInputShape),this.feedInputNames.push(E.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,E,D,F,_,B)=>{(F==null||_==null||B==null)&&(F=N.sourceLayer,_=N.nodeIndex,B=N.tensorIndex);const $=F.inboundNodes[_];if(D.indexOf($)!==-1)throw new ni(`The tensor ${N.name} at layer "${F.name}" is part of a cycle.`);if(E.indexOf($)!==-1)return;this.containerNodes.add(ki.nodeKey(F,_)),F.id in o||(o[F.id]=Object.keys(o).length),D.indexOf($)===-1&&D.push($);const H=$.inboundLayers.length;for(let q=0;q<H;q++){const J=$.inputTensors[q],re=$.inboundLayers[q],ce=$.nodeIndices[q],ue=$.tensorIndices[q];c(J,E,D,re,ce,ue)}for(E.push($);D.indexOf($)>=0;)D.splice(D.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 E=t[N.id];const D=s[N.outboundLayer.id]==null?0:s[N.outboundLayer.id];E=Math.max(E,D),s[N.outboundLayer.id]=E,i[N.outboundLayer.id]=N.outboundLayer,t[N.id]=E;for(let F=0;F<N.inboundLayers.length;F++){const _=N.inboundLayers[F],B=N.nodeIndices[F],$=_.inboundNodes[B],H=t[$.id]==null?0:t[$.id];t[$.id]=Math.max(E+1,H),n[$.id]=$}}const y={};for(const N in t){const E=t[N];E in y||(y[E]=[]),y[E].push(n[N])}const b={};for(const N in s){const E=s[N];E in b||(b[E]=[]),b[E].push(i[N])}let w=Object.keys(b).map(N=>parseInt(N,10)).sort(cp);this.layers=[];for(const N of w){const E=b[N];E.sort((D,F)=>{const _=o[D.id],B=o[F.id];return _<B?-1:_>B?1:0});for(const D of E)D instanceof ki&&this.internalContainerRefs.push(D),this.layers.push(D)}this.layersByDepth=b,w=Object.keys(y).map(N=>parseInt(N,10)).sort(cp);const L=this.inputs.slice(),T=[];for(const N of w)for(const E of y[N]){const D=E.outboundLayer;if(D!=null){for(const F of E.inputTensors)if(L.indexOf(F)===-1)throw new ni(`Graph disconnected: cannot obtain value for tensor ${F} at layer "${D.name}". The following previous layers were accessed without issue: ${T}`);for(const F of E.outputTensors)L.push(F);T.push(D.name)}}this.nodesByDepth=y;const A=this.layers.map(N=>N.name);for(const N of A){const E=A.filter(D=>D===N).length;if(E!==1)throw new ni(`The name "${N}" is used ${E} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new xp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 j("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 j(`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 j(`Provided weight data has no target variable: ${o}`);delete n[a]}if(t){const o=[];for(const a in n)o.push(a);if(o.length>0)throw new j(`${o.length} of ${s} weights are not set: ${o}`)}hw(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Dp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=bw(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ee(()=>{e=Nt(e);const n=new Fo;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Nh(this.outputs,n,t)})}computeMask(e,t){return ee(()=>{e=Nt(e);let n;return t==null?n=Oo(null,e.length):n=Nt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=Sp(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],h=t[a],d=c.name+"_0_0";n[d]=h}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(cp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const h of c){const d=h.outboundLayer;if(this.inputLayers.map(L=>L.id).indexOf(d.id)!==-1)continue;const m=[];for(let L=0;L<h.inboundLayers.length;L++){const T=h.inboundLayers[L],A=h.nodeIndices[L],N=h.tensorIndices[L],E=`${T.name}_${A}_${N}`,D=n[E];m.push(D)}const y=d.computeOutputShape(jn(m)),b=Sp(y),w=d.inboundNodes.indexOf(h);for(let L=0;L<b.length;L++){const T=`${d.name}_${w}_${L}`;n[T]=b[L]}}}const i=[],o=[];for(let a=0;a<this.outputLayers.length;a++){const c=this.outputLayers[a],h=this.outputLayersNodeIndices[a],d=this.outputLayersTensorIndices[a],m=`${c.name}_${h}_${d}`;o.push(m)}for(let a=0;a<o.length;a++){const c=o[a];xs(c in n),i.push(n[c])}return jn(i)}runInternalGraph(e,t){t==null&&(t=Oo(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(cp);for(const c of s){const h=this.nodesByDepth[c];for(const d of h){const m=d.outboundLayer,y=d.inputTensors,b=d.outputTensors,w=new Array;for(const L of y)L.id in n&&w.push(n[L.id]);if(w.length===y.length){let L={},T,A,N,E;if(d.callArgs!=null&&(L=d.callArgs),w.length===1){const[D,F]=w[0];L.mask==null&&(L.mask=F),N=Nt(m.call(D,L)),E=Nt(m.computeMask(D,F)),T=[D],A=[F]}else T=w.map(D=>D[0]),A=w.map(D=>D[1]),L.mask==null&&(L.mask=A),N=Nt(m.call(T,L)),E=Nt(m.computeMask(T,A));if(m.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let D=0;D<b.length;++D){const F=b[D],_=N[D],B=E[D];n[F.id]=[_,B]}}}}const i=[],o=[],a=[];for(const c of this.outputs){xs(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 ki?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=ki.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 j(`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 j("Provide either a layer name or layer index");for(const n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return ee(()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=ki.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 y=o.inboundNodes[m],b=ki.nodeKey(o,m);let w={};if(this.containerNodes.has(b)){if(y.callArgs)try{JSON.stringify(y.callArgs),w=y.callArgs}catch(L){console.warn(`Layer ${o.name} was passed non-serializable keyword arguments: ${y.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),w={}}if(y.inboundLayers.length>0){const L=[];for(let T=0;T<y.inboundLayers.length;T++){const A=y.inboundLayers[T],N=y.nodeIndices[T],E=y.tensorIndices[T],D=ki.nodeKey(A,N);let F=t[D];F==null&&(F=0),L.push([A.name,F,E,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=ki.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=ki.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(T,A){T.name in o?o[T.name].push(A):o[T.name]=[A]}function c(T,A){const N=[];let E;for(const D of A){const F=D[0],_=D[1],B=D[2];if(E=D[3]==null?{}:D[3],!(F in i)){a(T,A);return}const $=i[F];if($.inboundNodes.length<=_){a(T,A);return}const H=$.inboundNodes[_];N.push(H.outputTensors[B])}N.length>0&&T.apply(jn(N),E)}function h(T){const A=T.name,N=oi(T,t.customObjects!=null?t.customObjects:{});N.setFastWeightInitDuringBuild(s),i[A]=N;const E=T.inboundNodes;E.forEach(D=>{if(!(D instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${D}`);a(N,D)})}const d=t.name,m=t.layers;for(const T of m)h(T);for(;!EP(o);)for(const T of m){const A=i[T.name];if(A.name in o){const N=o[A.name];delete o[A.name];for(const E of N)c(A,E)}}const y=[],b=[],w=t.inputLayers;for(const T of w){const A=T[0],N=T[1],E=T[2];xs(A in i);const D=i[A],F=D.inboundNodes[N].outputTensors;y.push(F[E])}const L=t.outputLayers;for(const T of L){const A=T[0],N=T[1],E=T[2];xs(A in i);const D=i[A],F=D.inboundNodes[N].outputTensors;b.push(F[E])}return new e({inputs:y,outputs:b,name:d})}get stateful(){if(this._stateful)throw new j("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(){ee(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function Av(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: ${JSON.stringify(e)}`)}function vv(e,t){return Av(e,t,"classWeight")}function EQ(e,t){return Av(e,t,"sampleWeight")}async function Nv(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){const i=ee(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const c=1;return e.argMax(c)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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])}),ns(a,"float32")}else return null}function t3(e,t){return X(e,t)}const n3=32;function Cv(e,t){let n,s;const i=t;n=i.xs,s=i.ys,k(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=Rv("input",e.inputNames,n),a=Rv("output",e.outputNames,s),c=o[0].shape[0];k(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)})`),k(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++)k(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++)k(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 Rv(e,t,n){if(n instanceof Q)return[n];if(Array.isArray(n))return k(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 j(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);s.push(n[i])}return s}}function s3(e){if(e.length===3)throw new ze("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function i3(e,t,n){const s=n.batchesPerEpoch!=null;if(k(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k(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}`),k(!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}`),k(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(Ov(n.validationData))k(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 A=s3(n.validationData);o=A.xs,a=A.ys}const c=e.makeTrainFunction(),h=e.getDedupedMetricsNames();let d;i?d=h.slice().concat(h.map(A=>"val_"+A)):d=h.slice();const m=pv(n.callbacks,n.yieldEvery),y=n.verbose==null?1:n.verbose,{callbackList:b,history:w}=mv(m,y,n.epochs,null,null,r3(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,T=await t.iterator();for(;L<n.epochs;){const A={};await b.onEpochBegin(L);let N=0,E=0;for(s||(T=await t.iterator());s?N<n.batchesPerEpoch:!0;){const D=await T.next();if(s&&D.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(D.value!=null){const{xs:F,ys:_}=Cv(e,D.value),B={};B.batch=E,B.size=F[0].shape[0],await b.onBatchBegin(E,B);const $=[];if(n.classWeight!=null){const J=vv(n.classWeight,e.outputNames);for(let re=0;re<J.length;++re)$.push(await Nv(_[re],null,J[re]))}const H=F.concat(_).concat($),q=c(H);He(H);for(let J=0;J<h.length;++J){const re=h[J],ce=q[J];B[re]=ce,Nn(ce)}await b.onBatchEnd(E,B),cv(B),E++,N++}if(s?N>=n.batchesPerEpoch:D.done){if(i){let F;Ov(n.validationData)?F=Nt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):F=Nt(e.evaluate(o,a,{batchSize:n.validationBatchSize==null?n3:n.validationBatchSize,verbose:0}));for(let _=0;_<e.metricsNames.length;++_)A[`val_${e.metricsNames[_]}`]=F[_]}break}if(e.stopTraining_)break}if(await b.onEpochEnd(L,A),L++,e.stopTraining_)break}return await b.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function r3(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function Ov(e){return typeof e.iterator=="function"}function o3(e){return typeof e.next=="function"}async function a3(e,t,n){n=n||{};const s=n.batches!=null,i=e.testFunction;let o=[];if(n.verbose>0)throw new ze("Verbose mode is not implemented yet.");k(!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=o3(t)?t:await t.iterator();let c=0,h=0;for(;s?h<n.batches:!0;){const d=await a.next();if(o=ee(()=>{if(d.value){const{xs:m,ys:y}=Cv(e,d.value),b=m.concat(y),w=ee(()=>i(b));if(He(b),h===0)for(let T=0;T<w.length;++T)o.push(Ne(0));const L=b[0].shape[0];for(let T=0;T<w.length;++T){const A=w[T],N=o[T];o[T]=ee(()=>be(o[T],X(L,A))),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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Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,i=!0,o){const[a,c]=this.standardizeUserDataXY(e,t,i,o);if(n!=null)throw new Error("sample weight is not supported yet.");let h=null;if(s!=null){const d=vv(s,this.outputNames);h=[];for(let m=0;m<d.length;++m)h.push(await Nv(c[m],null,d[m]))}return[a,c,h]}testLoop(e,t,n,s=0,i){return ee(()=>{const o=this.checkNumSamples(t,n,i,"steps"),a=[];if(s>0)throw new ze("Verbose mode is not implemented yet.");if(i!=null)throw new ze("steps mode in testLoop() is not implemented yet");{const c=Iw(o,n),h=ns(si(0,o));for(let d=0;d<c.length;++d){const m=c[d][0],y=c[d][1],b=ko(h,m,y-m),w=Sw(t,b),L=e(w);if(d===0)for(let T=0;T<L.length;++T)a.push(Ne(0));for(let T=0;T<L.length;++T){const A=L[T];a[T]=be(a[T],X(y-m,A))}}for(let d=0;d<a.length;++d)a[d]=_e(a[d],o)}return a})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let i=s;if(zA(e,s)>1){const o=zA(e.slice(0,n),s);i+=`_${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 y=new Fo(m),b=Nh(this.outputs,y,{training:!0});let w;for(let L=0;L<this.lossFunctions.length;++L){const T=this.lossFunctions[L];let A=T(s[L],b[L]);i[L]!=null&&(A=t3(A,i[L]));const N=zt(A);t.push(N),L===0?w=A:w=be(w,A)}for(let L=0;L<this.metricsTensors.length;++L){let T;if(this.outputs.length>1&&L<this.outputs.length)T=t[L];else{const A=this.metricsTensors[L][0],N=this.metricsTensors[L][1];T=zt(A(s[N],b[N]))}Nn(T),o.push(T)}return w=zt(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=>ee(()=>{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 Fo(o),c=Nh(this.outputs,a);for(let h=0;h<this.lossFunctions.length;++h){const d=this.lossFunctions[h],m=zt(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],y=zt(d(i[m],c[m]));t.push(y)}return t})}async fit(e,t,n={}){return l3(this,e,t,n)}async fitDataset(e,t){return i3(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),jn(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=Sd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Sd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=er(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=>er(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]=er(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[er(Op(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>er(Op(e)));{const e={};for(const t in this.metrics)e[t]=er(Op(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=vh(e.optimizer_config),n=oi(t);let s;if(typeof e.loss=="string")s=Eo(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>Eo(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=Eo(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>Eo(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=Eo(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const h=wy(e);if(h.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(h.length>1)throw new j(`Found more than one (${h.length}) save handlers for URL '${e}'`);e=h[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await yy(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:m3,generatedBy:`TensorFlow.js tfjs-layers v${Dp}`,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 yy(await this.optimizer.getWeights(),h);n.specs.push(...m),n.data=dd([n.data,d])}if(this.userDefinedMetadata!=null){const h=!0;Sv(this.userDefinedMetadata,this.name,h),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Sv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}nr.className="Model",me(nr);class _v extends nr{}_v.className="Functional",me(_v);async function f3(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=vh(n),i=oi(s,t);if(e.weightsManifest!=null){const o=await aT(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 g3(e,t){if(t==null&&(t={}),typeof e=="string"){const n=Ly(e,t);if(n.length===0)n.push(fd(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return y3(e,void 0,t)}async function y3(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("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=oi(vh(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 j("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:d,optimizerWeights:m}=b3(s.weightData,s.weightSpecs);c.loadWeights(d,o),c.optimizer!=null&&m.length>0&&await c.optimizer.setWeights(m),He(d),He(m.map(y=>y.tensor))}return c}function b3(e,t){const n=ud(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 ja extends nr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Lp("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 j(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof ja||e instanceof nr;let n;if(t){if(n=e,n.outputs.length!==1)throw new j("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 j("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 j("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 j(`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 j("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=ov(this.outputs[0])}this.inboundNodes=[],new xp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Oo(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(St(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 nr({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 ni("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 ni("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 ni("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 ni("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 j("Legacy serialization format not supported yet.");i=t}else k(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 ja))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const h=void 0,d=oi(c,h,s);s&&d.setFastWeightInitDuringBuild(!0),a.add(d)}return a}set stopTraining(e){if(this.model==null)throw new j("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 j("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}}}ja.className="Sequential",me(ja);function w3(e){return new nr(e)}function L3(e){return new ja(e)}function S3(e,t){return t==null&&(t={}),g3(e,t)}function Wv(e){return av(e)}function I3(e,t){Bs.registerCallbackConstructor(e,t)}class rs extends go{getConfig(){return{}}}class $v extends rs{apply(e,t=1){return XP(e,t)}}$v.className="elu",me($v);class Uv extends rs{apply(e){return Pd(e)}}Uv.className="selu",me(Uv);class Bv extends rs{apply(e){return Ri(e)}}Bv.className="relu",me(Bv);class Mv extends rs{apply(e){return ee(()=>Io(6,Ri(e)))}}Mv.className="relu6",me(Mv);class Pv extends rs{apply(e){return e}}Pv.className="linear",me(Pv);class zv extends rs{apply(e){return vi(e)}}zv.className="sigmoid",me(zv);class Gv extends rs{apply(e){return ZP(e)}}Gv.className="hardSigmoid",me(Gv);class Vv extends rs{apply(e){return Da(e)}}Vv.className="softplus",me(Vv);class Yv extends rs{apply(e){return JP(e)}}Yv.className="softsign",me(Yv);class Hv extends rs{apply(e){return Ca(e)}}Hv.className="tanh",me(Hv);class Tw extends rs{apply(e,t=-1){return No(e,t)}}Tw.className="softmax",me(Tw);class qv extends rs{apply(e,t=-1){return _d(e,t)}}qv.className="logSoftmax",me(qv);class jv extends rs{apply(e,t=1){return ee(()=>vi(e.mul(t)).mul(e))}}jv.className="swish",me(jv);function zr(e){return e.getClassName()}function Aw(e,t={}){return bh(e,ks.getMap().classNameMap,t,"activation")}function Gr(e){if(e==null){const t={};return t.className="linear",t.config={},Aw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Aw(t)}else return e instanceof rs?e:Aw(e)}function vw(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 Kv extends go{}class Rh extends Kv{constructor(e){super();vw(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 ee(()=>{let t=ct([1]);return this.hasL1&&(t=be(t,Ue(X(this.l1,sn(e))))),this.hasL2&&(t=be(t,Ue(X(this.l2,xh(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Rh.className="L1L2",me(Rh);function x3(e){return vw(e),new Rh({l1:e!=null?e.l1:null,l2:0})}function T3(e){return vw(e),new Rh({l2:e!=null?e.l2:null,l1:0})}const Xv={l1l2:"L1L2"};function It(e){return Gb(e)}function Jv(e,t={}){return bh(e,ks.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){const t=e in Xv?Xv[e]:e,n={className:t,config:{}};return Jv(n)}else return e instanceof Kv?e:Jv(e)}class Nw extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=je(e);let n=Ri(e);return this.maxValue!=null&&(n=Yn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Nw.className="ReLU",me(Nw);class Cw 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=je(e);return Dd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Cw.className="LeakyReLU",me(Cw);class Rw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ft(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=ln(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 j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=St(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new mn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=je(e),nh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Vt(this.alphaInitializer),alphaRegularizer:It(this.alphaRegularizer),alphaConstraint:cn(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}Rw.className="PReLU",me(Rw);class Ow extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${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=je(e);return So(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Ow.className="ELU",me(Ow);class Ew 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=je(e);return n.mul(Sh(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Ew.className="ThresholdedReLU",me(Ew);class Dw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Tw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=je(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}}Dw.className="Softmax",me(Dw);function Ka(e,t,n){if(typeof e=="number")return Oo(e,t);if(e.length!==t)throw new j(`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(!VP(i))throw new j(`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 ai(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 kp(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Br([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function kw(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Me(e,[0,2,3,1]):e))}function Zv(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Me(e,[0,2,3,4,1]):e))}function Qv(e,t,n,s=1,i="valid",o,a=1){return ee(()=>{if(o==null&&(o=ti()),Gt(o),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(o==="channelsFirst"&&(e=Me(e,[0,2,1])),i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=Nd(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=Di(c,n)),c})}function DQ(e,t,n=1,s="valid",i,o=1){return ee(()=>(Gt(i),Qv(e,t,null,n,s,i,o)))}function kQ(e,t,n=[1,1],s="valid",i,o){return ee(()=>(Gt(i),Fw(e,t,null,n,s,i,o)))}function Fw(e,t,n,s=[1,1],i="valid",o,a,c=null){return ee(()=>{if(o==null&&(o=ti()),Gt(o),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let h=kw(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return h=vb({x:h,filter:t,strides:s,pad:i==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:c}),o==="channelsFirst"&&(h=Me(h,[0,3,1,2])),h})}function FQ(e,t,n=[1,1,1],s="valid",i,o){return ee(()=>(Gt(i),eN(e,t,null,n,s,i,o)))}function eN(e,t,n,s=[1,1,1],i="valid",o,a){return ee(()=>{if(o==null&&(o=ti()),Gt(o),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let c=Zv(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=Zy(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=Di(c,n)),o==="channelsFirst"&&(c=Me(c,[0,4,1,2,3])),c})}class _w extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",_w.verifyArgs(t),this.rank=e,pn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ka(t.kernelSize,e,"kernelSize"),this.strides=Ka(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Ts(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Gt(this.dataFormat),this.activation=Gr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ln(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=Ka(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`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 j(`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 j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(xs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Yb(e.kernelSize,"number",1,3))throw new j(`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:zr(this.activation),useBias:this.useBias,biasInitializer:Vt(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:cn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Oh extends _w{constructor(e,t){super(e,t);this.kernel=null,Oh.verifyArgs(t),this.filters=t.filters,pn(this.filters,"filters"),this.kernelInitializer=Ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ln(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=St(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`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 ee(()=>{e=je(e);let n;const s=this.bias==null?null:this.bias.read(),i=VA(this.activation.getClassName());if(i!=null&&this.rank===2)n=Fw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=Qv(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Fw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=eN(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=St(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=ai(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:Vt(this.kernelInitializer),kernelRegularizer:It(this.kernelRegularizer),kernelConstraint:cn(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 j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Eh extends Oh{constructor(e){super(2,e);Eh.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Yb(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Eh.className="Conv2D",me(Eh);class Fp extends Oh{constructor(e){super(3,e);Fp.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 j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Fp.className="Conv3D",me(Fp);class Ww extends Eh{constructor(e){super(e);if(this.inputSpec=[new mn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=St(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new mn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{let n=je(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],y=this.strides[0],b=this.strides[1],w=kp(c,y,d,this.padding),L=kp(h,b,m,this.padding),T=[i,w,L,this.filters];this.dataFormat!=="channelsLast"&&(n=Me(n,[0,2,3,1]));let A=Cd(n,this.kernel.read(),T,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Me(A,[0,3,1,2])),this.bias!=null&&(A=Di(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=St(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]=kp(t[s],c,o,this.padding),t[i]=kp(t[i],h,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Ww.className="Conv2DTranspose",me(Ww);class tN extends Oh{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 j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("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 j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=ln(t.depthwiseConstraint),this.pointwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=ln(t.pointwiseConstraint)}build(e){if(e=St(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${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 j(`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 mn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{e=je(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Me(e,[0,2,3,1])),n=fb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Di(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Me(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=Vt(this.depthwiseInitializer),e.pointwiseInitializer=Vt(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=cn(this.depthwiseConstraint),e.pointwiseConstraint=cn(this.pointwiseConstraint),e}}tN.className="SeparableConv";class $w extends tN{constructor(e){super(2,e)}}$w.className="SeparableConv2D",me($w);class _p extends Oh{constructor(e){super(1,e);_p.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"&&!Yb(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}_p.className="Conv1D",me(_p);class Uw 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 ee(()=>{if(e=je(e),this.dataFormat==="channelsLast"){const n=hp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return hp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=hp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return hp(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}}Uw.className="Cropping2D",me(Uw);class Bw 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 ee(()=>{let n=je(e);const s=n.shape;if(this.dataFormat==="channelsFirst"){n=Me(n,[0,2,3,1]);const i=this.size[0]*s[2],o=this.size[1]*s[3],a=n.resizeNearestNeighbor([i,o]);return Me(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}}Bw.className="UpSampling2D",me(Bw);function A3(e,t,n=[1,1],s="valid",i,o){return ee(()=>{i==null&&(i=ti()),Gt(i);let a=kw(e,i);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return a=wo(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}class Mw extends _w{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ln(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=St(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 ee(()=>{e=je(e);let n=A3(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Di(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=St(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=ai(t,this.kernelSize[0],this.padding,this.strides[0]),o=ai(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=Vt(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=cn(this.depthwiseRegularizer),e}}Mw.className="DepthwiseConv2D",me(Mw);function nN(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("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 ee(()=>{const h=t.shape.length;if(h<3)throw new j(`Input should be at least 3D, but is ${h}D.`);const d=[1,0].concat(si(2,h));if(t=Me(t,d),o!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===h-1&&(i=Hn(i,-1)),i=Me(i,d)),s&&(t=Is(t,0),i!=null&&(i=Is(i,0)));const m=[];let y,b=n;const w=t.shape[0],L=Oi(t);let T;i!=null&&(T=Oi(i));for(let N=0;N<w;++N){const E=L[N],D=ee(()=>e(E,b));if(i==null)y=D[0],b=D[1];else{const F=ee(()=>{const _=T[N],B=On(_).sub(_),$=D[0].mul(_).add(b[0].mul(B)),H=b.map((q,J)=>D[1][J].mul(_).add(q.mul(B)));return{output:$,newStates:H}});y=F.output,b=F.newStates}c&&m.push(y)}let A;if(c){const N=1;A=is(m,N)}return[y,A,b]})}class Fi extends lt{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Up({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("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 mn({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 si(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){cw(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 ee(()=>{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 ze("Constants support is not implemented in RNN yet.");cw(e)&&(e=e[0]),e=e;const n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new mn({shape:[n,null,...s]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("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(!ot(this.stateSpec.map(a=>a.shape[a.shape.length-1]),o))throw new j(`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 mn({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new Qi("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(n==null)throw new j("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=>ct([n,s])):this.states_=[ct([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=>ct([n,s])):this.states_[0]=ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`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(!ot(i.shape,a))throw new j(`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=>Nn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=nN(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 mn({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 ri;if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const y=super.apply(h,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t==null?null:t.mask,s=t==null?null:t.training;let i=t==null?null:t.initialState;e=je(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 j(`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 T=this.cell.call([w].concat(L),a);return[T[0],T.slice(1)]},h=sN(c,e,i,this.goBackwards,n,null,this.unroll,this.returnSequences),d=h[0],m=h[1],y=h[2];this.stateful&&this.resetStates(y,s);const b=this.returnSequences?m:d;return this.returnState?[b].concat(y):b})}getInitialState(e){return ee(()=>{let t=ct(e.shape);return t=Ue(t,[1,2]),t=Ih(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Qb(t,[1,n]):t):this.cell.stateSize>1?[Qb(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()===Fi.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=oi(s,n);return new e(Object.assign(t,{cell:i}))}}Fi.className="RNN",me(Fi);class Xa extends lt{}class Wp extends Xa{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,pn(this.units,"units"),this.activation=Gr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Va([1,Br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Va([1,Br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=St(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 ee(()=>{if(e=e,e.length!==2)throw new j(`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=Vr({ones:()=>On(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Ei(X(e,o),this.kernel.read()):i=Ei(e,this.kernel.read()),this.bias!=null&&(i=Di(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Ei(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:zr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}Wp.className="SimpleRNNCell",me(Wp);class Pw extends Fi{constructor(e){e.cell=new Wp(e),super(e)}call(e,t){return ee(()=>{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)}}Pw.className="SimpleRNN",me(Pw);class $p extends Xa{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 j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,pn(this.units,"units"),this.activation=Gr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Gr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Va([1,Br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Va([1,Br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=St(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 ee(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training==null?!1:t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vr({ones:()=>On(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(s),rate:this.recurrentDropout,training:n,count:3}));const i=this.dropoutMask,o=this.recurrentDropoutMask;let a,c,h;0<this.dropout&&this.dropout<1&&(e=X(e,i[0]));let d=Ei(e,this.kernel.read());this.useBias&&(d=Di(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[y,b]=ss(m,[2*this.units,this.units],m.rank-1),w=Ei(s,y),[L,T,A]=ss(d,3,d.rank-1),[N,E]=ss(w,2,w.rank-1);a=this.recurrentActivation.apply(be(L,N)),c=this.recurrentActivation.apply(be(T,E));const D=Ei(X(c,s),b);h=this.activation.apply(be(A,D));const F=be(X(a,s),X(be(1,Pt(a)),h));return[F,F]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:zr(this.activation),recurrentActivation:zr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}$p.className="GRUCell",me($p);class zw extends Fi{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 $p(e),super(e)}call(e,t){return ee(()=>{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)}}zw.className="GRU",me(zw);class Dh extends Xa{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,pn(this.units,"units"),this.activation=Gr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Gr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Va([1,Br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Va([1,Br([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=St(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 Us{apply(c,h){const d=i.apply([o]),m=new dp().apply([o]),y=i.apply([o*2]);return QA(QA(d,m),y)}},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 ee(()=>{const n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`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=Vr({ones:()=>On(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(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 y=Ei(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),y=be(y,Ei(s,this.recurrentKernel.read())),this.useBias&&(y=Di(y,this.bias.read()));const[b,w,L,T]=ss(y,4,y.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(T);const A=X(m,this.activation.apply(d));return[A,A,d]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:zr(this.activation),recurrentActivation:zr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}Dh.className="LSTMCell",me(Dh);class Gw extends Fi{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 Dh(e),super(e)}call(e,t){return ee(()=>{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)}}Gw.className="LSTM",me(Gw);class Up extends Xa{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 ee(()=>{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){cw(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Do(`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(oi(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 lw(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]])}hw(t)}}Up.className="StackedRNNCells",me(Up);function Vr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>tv(t(),n),a=()=>Th(o,t,s);if(!i||i<=1)return Nn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(h=>Nn(h.clone()))}var v3=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 _Q extends Xa{}class iN extends Fi{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new mn({ndim:5})]}call(e,t){return ee(()=>{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 j("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 ee(()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=ct(i);return Array.isArray(t)?Array(t.length).fill(o):[o]})}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new Qi("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 j("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(()=>ct(i)):this.states_=[ct(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(()=>ct(i)):this.states_[0]=ct(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`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(!ot(c.shape,h))throw new j(`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=>Nn(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=ai(h,s[0],i,o[0],a[0]),y=ai(d,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,y]:[m,y,n]];return b}}iN.className="ConvRNN2D";class Bp extends Dh{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,pn(this.filters,"filters"),this.kernelSize=Ka(n,2,"kernelSize"),this.kernelSize.forEach(c=>pn(c,"kernelSize")),this.strides=Ka(s||1,2,"strides"),this.strides.forEach(c=>pn(c,"strides")),this.padding=i||"valid",Ts(this.padding),this.dataFormat=o||"channelsLast",Gt(this.dataFormat),this.dilationRate=Ka(a||1,2,"dilationRate"),this.dilationRate.forEach(c=>pn(c,"dilationRate"))}build(e){var t;e=St(e);const n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`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 Us{apply(y,b){const w=h.apply([d]),L=Qs([d]),T=h.apply([d*2]);return Zb([w,L,T])}},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 ee(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vr({ones:()=>On(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,h=(Ie,Se,Ee)=>!Se||!Se[Ee]?Ie:X(Se[Ee],Ie);let d=h(s,c,0),m=h(s,c,1),y=h(s,c,2),b=h(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let L=h(i,w,0),T=h(i,w,1),A=h(i,w,2),N=h(i,w,3);const E=3,[D,F,_,B]=ss(this.kernel.read(),a,E),[$,H,q,J]=this.useBias?ss(this.bias.read(),a):[null,null,null,null];d=this.inputConv(d,D,$,this.padding),m=this.inputConv(m,F,H,this.padding),y=this.inputConv(y,_,q,this.padding),b=this.inputConv(b,B,J,this.padding);const[re,ce,ue,he]=ss(this.recurrentKernel.read(),a,E);L=this.recurrentConv(L,re),T=this.recurrentConv(T,ce),A=this.recurrentConv(A,ue),N=this.recurrentConv(N,he);const de=this.recurrentActivation.apply(be(d,L)),le=this.recurrentActivation.apply(be(m,T)),ye=be(X(le,o),X(de,this.activation.apply(be(y,A)))),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=v3(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?Di(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return ji(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}Bp.className="ConvLSTM2DCell",me(Bp);class Vw extends iN{constructor(e){const t=new Bp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}Vw.className="ConvLSTM2D",me(Vw);class Mp 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 ee(()=>{this.invokeCallHook(e,t);const n=je(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Th(()=>tv(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()}}Mp.className="Dropout",me(Mp);class Yw extends Mp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}Yw.className="SpatialDropout1D",me(Yw);class Hw 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,pn(this.units,"units"),this.activation=Gr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ln(e.kernelConstraint),this.biasConstraint=ln(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=St(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=St(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=VA(this.activation.getClassName());let i;return s!=null?i=Ei(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Ei(n,this.kernel.read()),this.bias!=null&&(i=Di(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:zr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),biasConstraint:cn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}Hw.className="Dense",me(Hw);class qw extends lt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=St(e);for(const t of e.slice(1))if(t==null)throw new j(`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],Ur(e,1)]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=je(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 KP(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}qw.className="Flatten",me(qw);class jw extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Gr(e.activation)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e);return this.activation.apply(n)})}getConfig(){const e={activation:zr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}jw.className="Activation",me(jw);class Kw 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 ee(()=>(e=je(e),qP(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}Kw.className="RepeatVector",me(Kw);class Xw 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 j("Can only specifiy one unknown dimension.");else i*=h}const a=Ur(e);if(o!==null){if(i===0||a%i!==0)throw new j(n);s[o]=a/i}else if(a!==i)throw new j(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 ee(()=>{this.invokeCallHook(e,t);const n=je(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}}Xw.className="Reshape",me(Xw);class Jw 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=si(1,e.dims.length+1);if(!ot(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 mn({ndim:this.dims.length+1})]}computeOutputShape(e){e=St(e);const t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Me(je(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}Jw.className="Permute",me(Jw);class Zw 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=je(e),s=-1;return Pl(kr(n,this.maskValue),s)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=-1,i=!0,o=Pl(kr(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}Zw.className="Masking",me(Zw);class Qw 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(Nt(e.inputLength))}this.inputDim=e.inputDim,pn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,pn(this.outputDim,"outputDim"),this.embeddingsInitializer=Ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=ln(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 ee(()=>this.maskZero?(e=je(e),kr(e,Qe(e))):null)}computeOutputShape(e){if(e=St(e),this.inputLength==null)return[...e,this.outputDim];const t=Nt(this.inputLength);if(t.length!==e.length-1)throw new j(`"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 j(`"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 ee(()=>{this.invokeCallHook(e,t);let n=je(e);n.dtype!=="int32"&&(n=Sh(n,"int32"));const s=ev(this.embeddings.read(),n.as1D());return s.reshape(St(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Vt(this.embeddingsInitializer),embeddingsRegularizer:It(this.embeddingsRegularizer),activityRegularizer:It(this.activityRegularizer),embeddingsConstraint:cn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}Qw.className="Embedding",me(Qw);class Wo extends lt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}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 j("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=[St(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=$r(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&$r(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ee(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=Br(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=Ih(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],y=d.slice(1).concat([m]);let b=c.reshape([m].concat(Ur(d.slice(1))));b=Me(b,[1,0]),b=b.reshape(y),n.push(b),i=!0}else if(h>1){const d=si(1,h).concat([0]);n.push(Me(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=Me(o.reshape([-1,d]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(si(0,a-1));o=Me(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=$r(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return ee(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Hn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ws(n,t[s]);return n})}}class kh extends Wo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return t})}}kh.className="Add",me(kh);function WQ(e){if(Array.isArray(e)){const t=new kh({});return t.apply(e)}else return new kh(e)}class Fh extends Wo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}}Fh.className="Multiply",me(Fh);function $Q(e){if(Array.isArray(e)){const t=new Fh({});return t.apply(e)}else return new Fh(e)}class _h extends Wo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return X(1/e.length,t)})}}_h.className="Average",me(_h);function UQ(e){if(Array.isArray(e)){const t=new _h({});return t.apply(e)}else return new _h(e)}class Wh extends Wo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=_s(t,e[n]);return t})}}Wh.className="Maximum",me(Wh);function BQ(e){if(Array.isArray(e)){const t=new Wh({});return t.apply(e)}else return new Wh(e)}class $h extends Wo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Io(t,e[n]);return t})}}$h.className="Minimum",me($h);function MQ(e){if(Array.isArray(e)){const t=new $h({});return t.apply(e)}else return new $h(e)}class Uh extends Wo{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const s of e)if(s!=null){t=!1;break}if(t)return;const n=[];for(let s=0;s<e.length;++s){const i=e[s].slice();i.splice(this.axis,1);let o=!1;for(const a of n)if(ot(a,i)){o=!0;break}o||n.push(i)}if(n.length>1)throw new j("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return ee(()=>Zb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new j("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(const i of t.slice(1)){if(n[s]==null||i[s]==null){n[s]=null;break}n[s]+=i[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return ee(()=>{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(On(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(Hn(t[o],-1)):s.push(t[o]);const i=Mt(s,this.axis);return xd(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Uh.className="Concatenate",me(Uh);function PQ(e){if(Array.isArray(e)){const t=new Uh({});return t.apply(e)}else return new Uh(e)}function Bh(e,t){for(;e<0;)e+=t;return e}function N3(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ze("batchDot is not implemented for tensors of 4D or higher rank yet");if(k(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k(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 ze("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 ee(()=>{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 eL extends Wo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k(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 ze("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 j(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`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)=>Bh(i,e[o].shape.length)):s=[Bh(this.axes,t.shape.length),Bh(this.axes,n.shape.length)],this.normalize&&(t=Tp(t,s[0]),n=Tp(n,s[1])),N3(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Bh(this.axes,e.length),Bh(this.axes,t.length)],n}computeOutputShape(e){k(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 ze("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}}eL.className="Dot",me(eL);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 ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=()=>up(n.shape,0,this.stddev).add(n),i=Th(s,()=>n,t.training||!1);return i})}}tL.className="GaussianNoise",me(tL);class nL 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 ee(()=>{this.invokeCallHook(e,t);const n=je(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(up(n.shape,1,i))};return Th(s,()=>n,t.training||!1)}return n})}}nL.className="GaussianDropout",me(nL);class sL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||je(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 ee(()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const i=je(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let h=Ki(vo(n),this.rate);h=Sh(h,"float32");const d=((1-this.rate)*(1+this.rate*c**2))**-.5,m=-d*c*this.rate,y=i.mul(h).add(h.add(-1).mul(c));return y.mul(d).add(m)};return Th(s,()=>je(e),t.training||!1)}return e})}}sL.className="AlphaDropout",me(sL);function Mh(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=CT(e,t,n,s,i,o);else if(e.rank===3)a=RT(e,t,n,s,i,o);else if(e.rank===4)a=OT(e,t,n,s,i,o);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function C3(e,t,n,s,i=.001){return ee(()=>{const o=Ud(e,s),a=o.mean,c=o.variance,h=Mh(e,a,c,n,t,i);return[h,a,c]})}function R3(e,t,n,s,i=.001){return ee(()=>{const o=Ud(e,s),a=o.mean,c=o.variance,h=[];for(const L of si(0,e.rank))s.indexOf(L)!==-1?h.push(1):h.push(e.shape[L]);const d=a.reshape(h),m=c.reshape(h),y=t==null?null:t.reshape(h),b=n==null?null:n.reshape(h),w=Mh(e,d,m,b,y,i);return[w,a,c]})}function O3(e,t,n,s,i=.001){return ot(s.slice().sort(),si(0,e.rank-1))?C3(e,t,n,s,i):R3(e,t,n,s,i)}class iL 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=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=ln(e.betaConstraint),this.gammaConstraint=ln(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=St(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new mn({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 ee(()=>{const n=t.training==null?!1:t.training,s=je(e),i=s.shape,o=i.length,a=si(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const h=Oo(1,o);h[c]=i[c];const d=a.slice();d.sort();const m=!ot(d,si(0,o).slice(0,o-1)),y=()=>{if(m){const N=this.movingMean.read().reshape(h),E=this.movingVariance.read().reshape(h),D=this.center?this.beta.read().reshape(h):null,F=this.scale?this.gamma.read().reshape(h):null;return Mh(s,N,E,D,F,this.epsilon)}else return Mh(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 y();const[b,w,L]=O3(s,this.gamma.read(),this.beta.read(),a,this.epsilon),T=(N,E,D)=>{ee(()=>{const F=1-D,_=N.read(),B=_.sub(E).mul(F);N.write(_.sub(B))})},A=()=>{T(this.movingMean,w,this.momentum),T(this.movingVariance,L,this.momentum)};return A(),b})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),movingMeanInitializer:Vt(this.movingMeanInitializer),movingVarianceInitializer:Vt(this.movingVarianceInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer),betaConstraint:cn(this.betaConstraint),gammaConstraint:cn(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}iL.className="BatchNormalization",me(iL);class rL 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=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=St(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: ${i}`);if(this.axis.length!==$r(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=je(e),s=n.shape,i=s.length;return ee(()=>{const o=!0;let{mean:a,variance:c}=Ud(n,this.axis,o);const h=Oo(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()),y=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),y=y.tile(w),Mh(n,a,c,y,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}rL.className="LayerNormalization",me(rL);function zQ(e,t){return ee(()=>{if(e.rank!==3)throw new j(`temporalPadding expects input tensor to be 3-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new j(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);const n=[[0,0],t,[0,0]];return Ci(e,n)})}function E3(e,t,n){return ee(()=>{if(e.rank!==4)throw new j(`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 j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ti()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Ci(e,s)})}class oL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ti():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){e=St(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 ee(()=>E3(je(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}oL.className="ZeroPadding2D",me(oL);function Pp(e,t,n,s,i,o){return ee(()=>{Gt(i),qA(o),Ts(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=ti()),o==null&&(o="max"),e=kw(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=eh(e,t,n,c):a=Hl(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}function rN(e,t,n,s,i,o){return ee(()=>{Gt(i),qA(o),Ts(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=ti()),o==null&&(o="max"),e=Zv(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=cb(e,t,n,c):a=jy(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,4,1,2,3])),a})}class oN 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 j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(pn(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 j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ts(this.padding),this.inputSpec=[new mn({ndim:3})]}computeOutputShape(e){e=St(e);const t=ai(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return ee(()=>{this.invokeCallHook(e,t),e=Ih(je(e),2);const n=this.poolingFunction(je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Fr(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class aL extends oN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"max")}}aL.className="MaxPooling1D",me(aL);class cL extends oN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"avg")}}cL.className="AveragePooling1D",me(cL);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 j(`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];pn(this.poolSize,"poolSize"),pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ts(this.padding),this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){e=St(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ai(t,this.poolSize[0],this.padding,this.strides[0]),n=ai(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 ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(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 lL extends aN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"max")}}lL.className="MaxPooling2D",me(lL);class hL extends aN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"avg")}}hL.className="AveragePooling2D",me(hL);class cN 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 j(`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];pn(this.poolSize,"poolSize"),pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ts(this.padding),this.inputSpec=[new mn({ndim:5})]}computeOutputShape(e){e=St(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=ai(t,this.poolSize[0],this.padding,this.strides[0]),n=ai(n,this.poolSize[1],this.padding,this.strides[1]),s=ai(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 ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(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 uL extends cN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),rN(e,t,n,s,i,"max")}}uL.className="MaxPooling3D",me(uL);class dL extends cN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),rN(e,t,n,s,i,"avg")}}dL.className="AveragePooling3D",me(dL);class lN extends lt{constructor(e){super(e);this.inputSpec=[new mn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}}class pL extends lN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=je(e);return zt(n,1)})}}pL.className="GlobalAveragePooling1D",me(pL);class mL extends lN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=je(e);return qn(n,1)})}}mL.className="GlobalMaxPooling1D",me(mL);class hN extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class fL extends hN{call(e,t){return ee(()=>{const n=je(e);return this.dataFormat==="channelsLast"?zt(n,[1,2]):zt(n,[2,3])})}}fL.className="GlobalAveragePooling2D",me(fL);class gL extends hN{call(e,t){return ee(()=>{const n=je(e);return this.dataFormat==="channelsLast"?qn(n,[1,2]):qn(n,[2,3])})}}gL.className="GlobalMaxPooling2D",me(gL);class uN 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=oi(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class yL extends uN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=St(e),e.length<3)throw new j(`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=St(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 ee(()=>{e=je(e);const n=(o,a)=>{const c=je(this.layer.call(o,t));return[c,[]]},s=sN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}yL.className="TimeDistributed",me(yL);function D3(e){za(PP,"BidirectionalMergeMode",e)}const k3="concat";class bL extends uN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=oi(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=oi(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?k3:e.mergeMode,D3(this.mergeMode),e.weights)throw new ze("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()):jn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=nN(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 j("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 mn({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 ze("Support for constants in Bidirectional layers is not implemented yet.");const c=o[0]instanceof ri;for(const h of o)if(h instanceof ri!==c)throw new j("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 y=super.apply(h,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{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=Is(i,1));let a;return this.mergeMode==="concat"?a=Zb([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){Do(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Do(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=oi(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}bL.className="Bidirectional",me(bL);function F3(e){return new Ya(e)}function _3(e){return new Ow(e)}function W3(e){return new Nw(e)}function $3(e){return new Cw(e)}function U3(e){return new Rw(e)}function B3(e){return new Dw(e)}function M3(e){return new Ew(e)}function P3(e){return new _p(e)}function z3(e){return new Eh(e)}function G3(e){return new Ww(e)}function 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$V=Object.freeze({__proto__:null,json:WV});const 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n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}}function jV(e){const t=C().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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t.size=="number"?t.size:parseInt(t.size,10)):[]}function RL(e,t,n){const s=e[t];return s&&s.shape?TN(s.shape):n}function OL(e,t,n){const s=e[t];return s?((s.list.f&&s.list.f.length?s.list.f:s.list.i)||[]).map(i=>typeof i=="number"?i:parseInt(i,10)):n}function EL(e,t,n,s=!1){const i=e[t];return i&&i.list&&i.list.s?i.list.s.map(o=>IN(o,s)):n}function DL(e,t,n){const s=e[t];return s&&s.list&&s.list.shape?s.list.shape.map(i=>TN(i)):n}function kL(e,t,n){const s=e[t];return s&&s.list&&s.list.b?s.list.b:n}class KV{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(s=>this.getInput(s)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((s,i)=>(s[i]=this.getAttr(i),s),{}))}getInput(e){return Xn(e,this.tensorMap,this.context)}getAttr(e,t){const n=this.node.rawAttrs[e];if(n.tensor!=null)return Xn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return AL(this.node.rawAttrs,e,t);if(n.s!=null)return xL(this.node.rawAttrs,e,t);if(n.b!=null)return TL(this.node.rawAttrs,e,t);if(n.shape!=null)return RL(this.node.rawAttrs,e,t);if(n.type!=null)return NL(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return OL(this.node.rawAttrs,e,t);if(n.list.s!=null)return EL(this.node.rawAttrs,e,t);if(n.list.shape!=null)return DL(this.node.rawAttrs,e,t);if(n.list.b!=null)return kL(this.node.rawAttrs,e,t);if(n.list.type!=null)return CL(this.node.rawAttrs,e,t)}return t}}const XV=(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[vT(R("tensors",e,t,n))];case"FloorMod":case"Mod":return[$d(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[_e(R("a",e,t,n),R("b",e,t,n))];case"DivNoNan":return[tb(R("a",e,t,n),R("b",e,t,n))];case"FloorDiv":return[Id(R("a",e,t,n),R("b",e,t,n))];case"Sub":return[Ce(R("a",e,t,n),R("b",e,t,n))];case"Minimum":return[Io(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[ei(R("a",e,t,n),R("b",e,t,n))];case"SquaredDifference":return[ah(R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},VQ="arithmetic";const JV=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[sn(R("x",e,t,n))];case"Acos":return[$y(R("x",e,t,n))];case"Acosh":return[Uy(R("x",e,t,n))];case"Asin":return[Py(R("x",e,t,n))];case"Asinh":return[zy(R("x",e,t,n))];case"Atan":return[Gy(R("x",e,t,n))];case"Atan2":return[Vy(R("x",e,t,n),R("y",e,t,n))];case"Atanh":return[Yy(R("x",e,t,n))];case"Ceil":return[Xy(R("x",e,t,n))];case"Complex":return[xi(R("real",e,t,n),R("imag",e,t,n))];case"Cos":return[Kl(R("x",e,t,n))];case"Cosh":return[Rd(R("x",e,t,n))];case"Elu":return[So(R("x",e,t,n))];case"Erf":return[nb(R("x",e,t,n))];case"Exp":return[Ls(R("x",e,t,n))];case"Expm1":return[sb(R("x",e,t,n))];case"Floor":return[Ra(R("x",e,t,n))];case"Log":return[ts(R("x",e,t,n))];case"Log1p":return[kd(R("x",e,t,n))];case"Imag":return[Ea(R("x",e,t,n))];case"Neg":return[Pt(R("x",e,t,n))];case"Reciprocal":return[db(R("x",e,t,n))];case"Real":return[xo(R("x",e,t,n))];case"Relu":return[Ri(R("x",e,t,n))];case"Round":return[mb(R("x",e,t,n))];case"Selu":return[Pd(R("x",e,t,n))];case"Sigmoid":return[vi(R("x",e,t,n))];case"Sin":return[zd(R("x",e,t,n))];case"Sign":return[gb(R("x",e,t,n))];case"Sinh":return[Gd(R("x",e,t,n))];case"Softplus":return[Da(R("x",e,t,n))];case"Sqrt":return[Ln(R("x",e,t,n))];case"Square":return[wt(R("x",e,t,n))];case"Tanh":return[Ca(R("x",e,t,n))];case"Tan":return[wb(R("x",e,t,n))];case"Relu6":case"ClipByValue":return[Yn(R("x",e,t,n),R("clipValueMin",e,t,n),R("clipValueMax",e,t,n))];case"Rsqrt":return[Md(Xn(e.inputNames[0],t,n))];case"Prod":return[Bd(R("x",e,t,n),R("axes",e,t,n))];case"LeakyRelu":return[Dd(R("x",e,t,n),R("alpha",e,t,n))];case"Prelu":return[nh(R("x",e,t,n),R("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},YQ="basic_math";function Ms(e,t,n=""){k(ZV(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function ZV(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==-1&&t[n]!==-1&&e[n]!==t[n])return!1;return!0}class QV{constructor(e,t,n,s,i,o,a){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=i,this.dynamicSize=o,this.clearAfterRead=a,this.tensors=[],this.closed_=!1,this.idTensor=Ne(0),Nn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);const t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);const n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),Ms(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,Nn(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,s)=>this.write(n,t[s]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let s=0;s<this.size();s++)e.push(s)}if(e.length===0)return en([],[0].concat(this.elementShape));const n=this.readMany(e);return Ms(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),is(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return en([],[0].concat(this.elementShape));const t=[];for(let s=0;s<this.size();s++)t.push(s);const n=this.readMany(t);return Ms(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),Mt(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);const n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,Oi(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0;const s=e.map(c=>(n+=c,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const i=n===0?0:t.size/n,o=[];ee(()=>{t=K(t,[1,n,i]);for(let c=0;c<e.length;++c){const h=c===0?0:s[c-1],d=[0,h,0],m=[1,e[c],i];o[c]=K(nt(t,d,m),this.elementShape)}return o});const a=[];for(let c=0;c<e.length;c++)a[c]=c;this.writeMany(a,o)}}class Ph{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(i=>{if(n!==i.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${i.dtype}`);Ms(t,i.shape,"TensorList shape mismatch: "),Nn(i)}),this.idTensor=Ne(0),this.maxNumElements=s,Nn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Ph([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);return Ms(e,this.elementShape,"TensorList shape mismatch: "),ee(()=>{const s=this.tensors.map(i=>K(i,e));return is(s,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");const n=this.tensors.pop();return Ms(n.shape,e,"TensorList shape mismatch: "),K(n,e)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Ms(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Nn(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);return Ms(this.tensors[e].shape,t,"TensorList shape mismatch: "),this.tensors[e]}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Ms(this.elementShape,t.shape,"TensorList shape mismatch: "),Nn(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);return Ms(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?en([],[0].concat(this.elementShape)):ee(()=>{const s=e.map(i=>K(this.tensors[i],n));return is(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return Ms(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?en([],[0].concat(this.elementShape)):ee(()=>{const n=this.tensors.map(s=>K(s,t));return Mt(n,0)})}}function eY(e,t,n){const s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);const i=e.shape.slice(1);Ms(i,t,"TensorList shape mismatch: ");const o=Oi(e);return new Ph(o,t,s)}function tY(e,t,n){return new Ph([],e,t,n)}function nY(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);const i=Math.max(...t);if(s!=null&&s!==-1&&i>=s)throw new Error(`Max index must be < array size (${i} vs. ${s})`);const o=new Ph([],n,e.dtype,s),a=Oi(e,0);return t.forEach((c,h)=>{o.setItem(c,a[h])}),o}function sY(e,t,n){let s=0;const i=t.map(h=>(s+=h,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${s}, and tensor's shape is: ${e.shape}`);const o=s===0?0:e.size/s,a=ee(()=>{const h=[];e=K(e,[1,s,o]);for(let d=0;d<t.length;++d){const m=d===0?0:i[d-1],y=[0,m,0],b=[1,t[d],o];h[d]=K(nt(e,y,b),n)}return e.dispose(),h}),c=new Ph([],n,e.dtype,t.length);for(let h=0;h<a.length;h++)c.setItem(h,a[h]);return c}const iY=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{const s=R("thenBranch",e,t,n),i=R("elseBranch",e,t,n),o=R("cond",e,t,n),a=R("args",e,t,n),c=await o.data();return c[0]?n.functionMap[s].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap):n.functionMap[i].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{const s=R("body",e,t,n),i=R("cond",e,t,n),o=R("args",e,t,n),a=await n.functionMap[i].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap),c=o.map(m=>m.id);let h=await a[0].data();a.forEach(m=>{!m.kept&&c.indexOf(m.id)===-1&&m.dispose()});let d=o;for(;h[0];){const m=d;d=await n.functionMap[s].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);const y=d.map(w=>w.id);m.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()});const b=await n.functionMap[i].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);h=await b[0].data(),b.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()})}return d}case"LoopCond":{const s=R("pred",e,t,n);return[ir(s)]}case"Switch":{const s=R("pred",e,t,n);let i=R("data",e,t,n);return i.kept||(i=ir(i)),(await s.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const s=e.inputNames.find(i=>Xn(i,t,n)!==void 0);if(s){const i=Xn(s,t,n);return[ir(i)]}return}case"Enter":{const s=R("frameName",e,t,n),i=R("tensor",e,t,n);return n.enterFrame(s),[ir(i)]}case"Exit":{const s=R("tensor",e,t,n);return n.exitFrame(),[ir(s)]}case"NextIteration":{const s=R("tensor",e,t,n);return n.nextIteration(),[ir(s)]}case"TensorArrayV3":{const 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 QV(d,i,s,o,h,a,c);return n.addTensorArray(m),[m.idTensor,Ne(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 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s=R("elementShape",e,t,n),i=R("elementDType",e,t,n),o=R("numElements",e,t,n),a=tY(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=eY(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 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s=R("axis",e,t,n);return[Fr(R("x",e,t,n),s)]}case"Reshape":return[K(R("x",e,t,n),R("shape",e,t,n))];case"PadV2":case"Pad":return[Ci(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[th(R("x",e,t,n),s,i)]}case"BatchToSpaceND":{const s=R("blockShape",e,t,n),i=R("crops",e,t,n);return[ql(R("x",e,t,n),s,i)]}case"DepthToSpace":{const s=R("blockSize",e,t,n),i=R("dataFormat",e,t,n).toUpperCase();return[Qy(R("x",e,t,n),s,i)]}case"BroadcastTo":return[jl(R("x",e,t,n),R("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},ree="transformation";function vN(e,t,n){const s=((i,o,a)=>{switch(i.category){case"arithmetic":return ee(()=>XV(i,o,a));case"basic_math":return ee(()=>JV(i,o,a));case"control":return iY(i,o,a);case"convolution":return ee(()=>rY(i,o,a));case"creation":return ee(()=>oY(i,o,a));case"dynamic":return aY(i,o,a);case"evaluation":return ee(()=>cY(i,o,a));case"image":return ee(()=>hY(i,o,a));case"graph":return ee(()=>lY(i,o,a));case"logical":return ee(()=>uY(i,o,a));case"matrices":return ee(()=>dY(i,o,a));case"normalization":return ee(()=>pY(i,o,a));case"reduction":return ee(()=>mY(i,o,a));case"slice_join":return ee(()=>fY(i,o,a));case"spectral":return ee(()=>gY(i,o,a));case"transformation":return ee(()=>yY(i,o,a));case"custom":const c=LN(i.op);if(c&&c.customExecutor)return c.customExecutor(new KV(i,o,a));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.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 s instanceof Promise?s.then(i=>[].concat(i)):[].concat(s)}class NN{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 CN(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const h=new Set,d=Object.keys(e).map(b=>os(b)[0]);let m=[];s!=null&&(m=s.map(b=>os(b.name)[0]));const y=[...t];for(;y.length>0;){const b=y.pop();if((RN(b)||SY(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),y.push(w)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:o,dynamicNode:a,syncInputs:c}}function bY(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>os(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(y=>{!h.has(y.name)&&s.has(y.name)&&y.inputs.every(b=>h.has(b.name))&&o.push(y)})}return d}const wY=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],LY=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function RN(e){return wY.indexOf(e.op)>=0}function SY(e){return LY.indexOf(e.op)>=0}class _L{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 _L(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}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=CN(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 bY(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[os(m)[0]]),i=t.map(m=>os(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 ee(()=>{const m=new NN(this.weightMap,h,d,this.functionExecutorMap),y=Object.assign({},this.weightMap);Object.keys(e).forEach(L=>{const[T,A]=os(L),N=[];N[A]=e[L],y[T]=N});const b=this.getFrozenTensorIds(y),w={};for(let L=0;L<c.length;L++){const T=c[L];if(!y[T.name]){const A=vN(T,y,m);if(A instanceof Promise)throw new Error(`The execution of the op '${T.op}' returned a promise. Please use model.executeAsync() instead.`);y[T.name]=A,this.checkTensorForDisposal(T.name,T,y,m,b,i,w)}}return this.parent==null&&m.dispose(b),t.map(L=>Xn(L,y,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=fV(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 NN(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(y=>Xn(y,a,o)),h=c.map(y=>y.id),d=Object.keys(e).map(y=>e[y].id),m=new Set([...h,...d,...this.weightIds]);return Object.keys(a).forEach(y=>{const b=a[y];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(E=>this.graph.nodes[os(E)[0]]),a=n.map(E=>os(E)[0]),c=a.map(E=>this.graph.nodes[E]),{usedNodes:h,missingInputs:d,dynamicNode:m,syncInputs:y}=CN(e,c,this.weightMap),b=[...o,...this.graph.weights].map(E=>({node:E,contexts:t.currentContext})),w=Object.assign({},this.weightMap);Object.keys(e).forEach(E=>{const[D,F]=os(E),_=[];_[F]=e[E],w[D]=_});const L={},T=this.getFrozenTensorIds(w),A={};for(;b.length>0;){const E=this.processStack(o,b,t,w,A,T,a,L,h);await Promise.all(E)}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(E=>!RN(E)&&!Xn(E.name,w,t)).map(E=>E.name);if(N.length>0){let E="";throw m!=null&&(E=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${y}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${i}]. Consider providing the following inputs: [${d}]. ${E}`)}return w}processStack(e,t,n,s,i,o,a,c,h){const d=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let y="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([y]=sr(m.node.name,n)),e.indexOf(m.node)===-1){const b=vN(m.node,s,n);y||([y]=sr(m.node.name,n));const w=n.currentContext;b instanceof Promise?d.push(b.then(L=>(s[y]=L,n.currentContext=w,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h),L))):(s[y]=b,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h))}else this.processChildNodes(m.node,t,n,s,i,h)}return d}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=sr(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(h=>!!Xn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(h=>!!Xn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=os(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,h)=>o[h]===-1||o[h]===c);k(a,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${o}], but was [${n.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&k(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 ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=os(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=os(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}const IY="?tfjs-format=file",xY="model.json";class ON{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=fd(e,this.loadOptions);else{const t=Ly(e,this.loadOptions);if(t.length===0)t.push(fd(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;const s=ud(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new _L(SN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),e.modelInitializer!=null){const i=SN.Instance.transformGraph(e.modelInitializer);this.initializer=new _L(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=wy(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Q)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function TY(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${xY}${IY}`));const n=new ON(e,t);return await n.load(),n}const EN="2.6.0";function AY(e,t){return Yp(e,t)}function Yp(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(Ja(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],h=Yp(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 vY(e,t=kN){return DN(e,t)}function DN(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(Ja(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(d=>d[a]),h=DN(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 kN(e){return e===null?null:Ja(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function FN(e,t){const n=new Map;Yp(e,t,n);for(const i of Array.from(n.keys())){const o=n.get(i);if(o instanceof Promise){const a=await o;n.set(i,a)}}const s=Yp(e,t,n);return s}function Ja(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Q))}function NY(e){return e==null||CY(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Q||wn(e)}function CY(e){return e===null||typeof e!="object"&&typeof e!="function"}function RY(e){return AY(e,OY)}function OY(e){return e instanceof Q?{value:e.clone(),recurse:!1}:Ja(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class _N{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 WL extends _N{constructor(){super(WL.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}}WL.INITIAL_CAPACITY=32;function WN(e){return new DY(e)}function oee(e){let t=e;return zh(()=>({value:t++,done:!1}))}function zh(e){return new kY(e)}function $N(e,t){return new BN(e,t)}function aee(e,t,n){return $N(zh(e).take(t),n)}function EY(e,t=Yr.FAIL){return new zY(e,t)}class fn{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 MY(this,e)}filter(e){return new UY(this,e)}map(e){return new BY(this,e)}mapAsync(e){return new UN(this,e)}serialMapAsync(e){return new UN(this,e).serial()}flatmap(e){return new PY(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 $Y(this,e,t)}columnMajorBatch(e,t=!0,n=kN){const s=this.rowMajorBatch(e,t);return s.map(i=>vY(i,n))}concatenate(e,t){return new BN(WN([this,e]),t)}take(e){return e<0||e==null?this:new WY(this,e)}skip(e){return e<0||e==null?this:new _Y(this,e)}prefetch(e){return new MN(this,e)}shuffle(e,t){return new GY(this,e,t)}serial(){return new FY(this)}}class DY extends fn{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:RY(e),done:!1}}}class kY extends fn{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: ${e.message}`,e}}}class FY extends fn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class _Y extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;He(e.value)}return this.upstream.next()}}class WY extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class $Y extends fn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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 UY extends fn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;He(e.value)}}}class BY extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${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)ld(i,s)||i.dispose();return{value:n,done:!1}}}class MY extends fn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class UN extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${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)ld(i,s)||i.dispose();return{value:n,done:!1}}}class $L extends fn{constructor(){super();this.outputQueue=new WL,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 PY extends $L{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${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)ld(i,s)||i.dispose();return!0}}class BN extends fn{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`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Yr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Yr||(Yr={}));class zY extends fn{constructor(e,t=Yr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,n=0;function s(o){if(o instanceof fn){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 FN(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Yr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Yr.SHORTEST:return{value:null,done:!0};case Yr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class MN extends fn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new _N(t)}summary(){return`${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 GY extends MN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=_a(n||Vn().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 Za{constructor(){this.size=null}batch(e,t=!0){const n=this;k(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),as(async()=>(await n.iterator()).columnMajorBatch(e,t,HY),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,as(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,as(async()=>(await t.iterator()).filter(s=>ee(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return as(async()=>(await t.iterator()).map(n=>ee(()=>e(n))),this.size)}mapAsync(e){const t=this;return as(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 as(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,as(async()=>{const s=zh(async()=>({value:await t.iterator(),done:!1}));return $N(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,as(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=_a(t||Vn().toString());return as(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,as(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()}}Za.MAX_BUFFER_SIZE=1e4;function as(e,t=null){return new class extends Za{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function VY(e){return as(async()=>WN(e),e.length)}function YY(e){if(!Ja(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 as(async()=>{const n=await FN(e,s=>{if(s instanceof Za)return{value:s.iterator(),recurse:!1};if(Ja(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return EY(n,Yr.SHORTEST)},t)}function HY(e){if(e===null)return null;const t=e[0];if(NY(t)){const n=qY(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function qY(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Q?is(e):en(e)}class PN extends Za{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 Hp='"',Gh=Symbol("out"),zN=Symbol("field"),qp=Symbol("quote"),UL=Symbol("quoteafterquote"),GN=Symbol("quoteinquote");class VN extends Za{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 PN(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k(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&&k(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(k(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=Gh;for(let a=0;a<i;a++)switch(o){case Gh:switch(e.charAt(a)){case Hp:s=a+1,o=qp;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=Gh;break;default:o=zN,s=a;break}break;case zN:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=Gh,s=a+1;break;default:}break;case qp:switch(e.charAt(a)){case Hp:o=UL;break;default:}break;case UL:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=Gh,s=a+1;break;case Hp:o=qp;break;default:o=GN;break}break;case GN:switch(e.charAt(a)){case Hp:o=qp;break;default:}break;default:}if(o===UL?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 YN extends fn{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(C().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new YN(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio: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(we(t));return n.set(e,n.length-e.length),en(n,t)}}class HN extends fn{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=ns([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=_r([o,i,c,a],[1,4])}else this.cropBox=_r([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(C().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 HN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k(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=uT(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 ee(()=>{const t=e.toFloat().expandDims(0);let n;n=Wr.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 qN{}class jN extends fn{split(e){return new jY(this,e)}}class jY extends jN{constructor(e,t){super();this.upstream=e,this.impl=new KY(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class KY extends $L{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${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 XY extends fn{decodeUTF8(){return new JY(this)}}class JY extends jN{constructor(e){super();this.upstream=e,this.impl=new ZY(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class ZY extends $L{constructor(e){super();if(this.upstream=e,C().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=require("string_decoder");this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> 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qN{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return XN(this.url)?new JN(this.url,this.fileOptions).iterator():QY(this.url,this.fileOptions)}}function tH(e,t={}){return new VN(new ZN(e),t)}function nH(e){const t=zh(e);return as(async()=>t)}function sH(e){return as(async()=>{const t=await e();return zh(()=>t.next())})}async function iH(e,t){return HN.create(e,t)}async function rH(e){return YN.create(e)}const QN="2.6.0";var oH=Object.freeze({__proto__:null,array:VY,Dataset:Za,zip:YY,CSVDataset:VN,TextLineDataset:PN,csv:tH,func:nH,generator:sH,microphone:rH,webcam:iH,FileDataSource:JN,URLDataSource:ZN,version_data:QN});function xe(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}const aH=tp,cH=Mb,lH=Pb,hH=zb,uH=jd;function BL(e,t,n,s){if(n==="linear")return e.linear(t);if(n==="relu")return e.relu(t);if(n==="elu")return So(t);if(n==="relu6")return 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============================
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){const 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 Zi(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=>Dl(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ze(e.shape,e.dtype,n)}makeOutput(e,t,n){const s=this.write(e,t,n);return Fs().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=Vn();e();const n=Vn()-t;return{kernelMs:n}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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ue=0;ue<y;ue+=h){const he=(q+ue)/o;if(he<0||he>=s.outDepth||Math.floor(he)!==he)continue;for(let de=0;de<b;de+=d){const le=(J+de)/a;if(le<0||le>=s.outHeight||Math.floor(le)!==le)continue;for(let ye=0;ye<w;ye+=m){const pe=(re+ye)/c;if(pe<0||pe>=s.outWidth||Math.floor(pe)!==pe)continue;const Ie=y*b*w-1-E.get(F,he,le,pe,_),Se=ue*b*w+de*w+ye,Ee=Ie===Se?1:0;if(Ee===0)continue;const We=D.get(F,he,le,pe,_);ce+=We*Ee}}}N.set(ce,F,B,$,H,_)}return N.toTensor()}resizeBilinear(e,t,n,s){xe(e,"resizeBilinear");const[i,o,a,c]=e.shape,h=this.readSync(e.dataId),d=new Float32Array(we([i,t,n,c])),m=[s&&t>1?o-1:o,s&&n>1?a-1:a],y=[s&&t>1?t-1:t,s&&n>1?n-1:n];let b=0;const w=m[0]/y[0],L=m[1]/y[1];for(let T=0;T<i;T++)for(let A=0;A<t;A++){const N=w*A,E=Math.floor(N),D=N-E,F=Math.min(o-1,Math.ceil(N)),_=T*e.strides[0]+E*e.strides[1],B=T*e.strides[0]+F*e.strides[1];for(let $=0;$<n;$++){const 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Ye=L[T++];d[le+$e]+=Ye*Se,d[ye+$e]+=Ye*Ee,d[pe+$e]+=Ye*We,d[Ie+$e]+=Ye*Oe}}}}return Ua(d,[s,o,i,a],t.dtype)}resizeNearestNeighbor(e,t,n,s){xe(e,"resizeNearestNeighbor");const[i,o,a,c]=e.shape,h=this.readSync(e.dataId),d=new Float32Array(i*t*n*c),m=[s&&t>1?o-1:o,s&&n>1?a-1:a],y=[s&&t>1?t-1:t,s&&n>1?n-1:n],b=m[0]/y[0],w=m[1]/y[1];let L=0;for(let T=0;T<i;T++){const A=T*e.strides[0];for(let N=0;N<t;N++){const E=b*N,D=Math.min(o-1,s?Math.round(E):Math.floor(E)),F=A+D*e.strides[1];for(let _=0;_<n;_++){const B=w*_,$=Math.min(a-1,s?Math.round(B):Math.floor(B)),H=F+$*e.strides[2];for(let q=0;q<c;q++){const J=h[H+q];d[L++]=J}}}}return en(d,[i,t,n,c],e.dtype)}resizeNearestNeighborBackprop(e,t,n){xe([e,t],"resizeNearestNeighborBackprop");const[s,i,o,a]=t.shape,[,c,h]=e.shape,d=new Float32Array(s*i*o*a),m=this.readSync(e.dataId),y=[n&&c>1?i-1:i,n&&h>1?o-1:o],b=[n&&c>1?c-1:c,n&&h>1?h-1:h],w=y[0]/b[0],L=y[1]/b[1],T=1/w,A=1/L,N=Math.ceil(T)*2+2,E=Math.ceil(A)*2+2;for(let D=0;D<s;D++){const F=D*t.strides[0];for(let _=0;_<i;_++){const B=F+_*t.strides[1],$=Math.floor(_*T),H=Math.floor($-N/2);for(let q=0;q<o;q++){const J=B+q*t.strides[2],re=Math.floor(q*A),ce=Math.floor(re-E/2);for(let ue=0;ue<a;ue++){let he=0;for(let de=0;de<N;de++){const le=de+H;if(le<0||le>=c)continue;const ye=F+le*e.strides[1],pe=le*w,Ie=Math.min(i-1,n?Math.round(pe):Math.floor(pe));if(_!==Ie)continue;for(let Se=0;Se<E;Se++){const Ee=Se+ce;if(Ee<0||Ee>=h)continue;const We=ye+Ee*e.strides[2],Oe=Ee*L,$e=Math.min(o-1,n?Math.round(Oe):Math.floor(Oe));q===$e&&(he+=m[We+ue])}}d[J+ue]=he}}}}return Ua(d,t.shape,t.dtype)}localResponseNormalization4D(e,t,n,s,i){xe(e,"localResponseNormalization4D");const o=e.shape[3],a=o-1,c=this.readSync(e.dataId),h=e.size,d=new Float32Array(h);function m(y){const b=y%o;let w=y-b+Math.max(0,b-t);const L=y-b+Math.min(b+t,a);let T=0;for(;w<=L;w++){const A=c[w];T+=A*A}return T}for(let y=0;y<h;y++){const b=m(y),w=c[y]*Math.pow(n+s*b,-i);d[y]=w}return Ua(d,e.shape)}LRNGrad(e,t,n,s,i,o,a){xe(e,"LRNGrad");const c=e.shape[3],h=this.readSync(e.dataId),d=this.readSync(t.dataId),m=this.readSync(n.dataId),y=new Float32Array(e.size),b=e.size;for(let w=0;w<b;w++){const L=w%c,T=w-L+Math.max(0,L-s),A=w-L+Math.min(c,L+s+1);let N=0;for(let E=T;E<A;E++)N+=Math.pow(d[E],2);N=o*N+i;for(let E=T;E<A;E++){let D=-2*o*a*d[E]*m[w]/N;w===E&&(D+=Math.pow(N,-a)),D*=h[w],y[E]+=D}}return Ua(y,e.shape)}multinomial(e,t,n,s){xe(e,"multinomial");const i=t?e:No(e),o=i.shape[0],a=i.shape[1],c=ct([o,n],"int32"),h=this.readSync(c.dataId),d=this.readSync(i.dataId);for(let m=0;m<o;++m){const y=m*a,b=new Float32Array(a-1);b[0]=d[y];for(let T=1;T<b.length;++T)b[T]=b[T-1]+d[y+T];const w=_a(s.toString()),L=m*n;for(let T=0;T<n;++T){const A=w();h[L+T]=b.length;for(let N=0;N<b.length;N++)if(A<b[N]){h[L+T]=N;break}}}return c}oneHot(e,t,n,s){xe(e,"oneHot");const i=new Float32Array(e.size*t);i.fill(s);const o=this.readSync(e.dataId);for(let a=0;a<e.size;++a)o[a]>=0&&o[a]<t&&(i[a*t+o[a]]=n);return _r(i,[e.size,t],"int32")}nonMaxSuppression(e,t,n,s,i){xe(e,"nonMaxSuppression");const o=this.readSync(e.dataId),a=this.readSync(t.dataId);return aH(o,a,n,s,i)}depthToSpace(e,t,n){k(n==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${n}`),k(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`);const s=e.shape[0],i=e.shape[1],o=e.shape[2],a=e.shape[3],c=i*t,h=o*t,d=a/(t*t),m=this.readSync(e.dataId),y=new Float32Array(s*c*h*d);let b=0;for(let w=0;w<s;++w)for(let L=0;L<c;++L){const T=Math.floor(L/t),A=L%t;for(let N=0;N<h;++N){const E=Math.floor(N/t),D=N%t,F=(A*t+D)*d;for(let _=0;_<d;++_){const B=_+F,$=B+a*(E+o*(T+i*w));y[b++]=m[$]}}}return Ua(y,[s,c,h,d])}broadcastedBinaryOp(e,t,n,s){const i=tt(e.shape,t.shape),o=Ze(i,n),a=this.readSync(e.dataId),c=this.readSync(t.dataId),h=Lo(e.shape,i),d=Lo(t.shape,i),m=o.values;if(h.length+d.length===0)for(let y=0;y<m.length;++y)m[y]=s(a[y%a.length],c[y%c.length]);else{const y=this.bufferSync(e),b=this.bufferSync(t);for(let w=0;w<m.length;++w){const L=o.indexToLoc(w),T=L.slice(-e.rank);h.forEach(D=>T[D]=0);const A=y.locToIndex(T),N=L.slice(-t.rank);d.forEach(D=>N[D]=0);const E=b.locToIndex(N);m[w]=s(a[A],c[E])}}return o.toTensor()}split(e,t,n){return cH(e,t,n)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}cropAndResize(e,t,n,s,i,o){const[a,c,h,d]=e.shape,m=t.shape[0],[y,b]=s,w=Ze([m,y,b,d],"float32"),L=this.readSync(t.dataId),T=this.readSync(n.dataId),A=this.readSync(e.dataId),N=e.strides,E=w.strides;for(let D=0;D<m;D++){const F=D*4,_=L[F],B=L[F+1],$=L[F+2],H=L[F+3],q=T[D];if(q>=a)continue;const J=y>1?($-_)*(c-1)/(y-1):0,re=b>1?(H-B)*(h-1)/(b-1):0;for(let ce=0;ce<y;ce++){const ue=y>1?_*(c-1)+ce*J:.5*(_+$)*(c-1);if(ue<0||ue>c-1){for(let he=0;he<b;he++)for(let de=0;de<d;de++){const le=de+he*E[2]+ce*E[1]+D*E[0];w.values[le]=o}continue}if(i==="bilinear"){const he=Math.floor(ue),de=Math.ceil(ue),le=ue-he;for(let ye=0;ye<b;ye++){const pe=b>1?B*(h-1)+ye*re:.5*(B+H)*(h-1);if(pe<0||pe>h-1){for(let We=0;We<d;We++){const Oe=We+ye*E[2]+ce*E[1]+D*E[0];w.values[Oe]=o}continue}const Ie=Math.floor(pe),Se=Math.ceil(pe),Ee=pe-Ie;for(let We=0;We<d;We++){let Oe=We+Ie*N[2]+he*N[1]+q*N[0];const $e=A[Oe];Oe=We+Se*N[2]+he*N[1]+q*N[0];const Ye=A[Oe];Oe=We+Ie*N[2]+de*N[1]+q*N[0];const et=A[Oe];Oe=We+Se*N[2]+de*N[1]+q*N[0];const bt=A[Oe],Jt=$e+(Ye-$e)*Ee,un=et+(bt-et)*Ee;Oe=We+ye*E[2]+ce*E[1]+D*E[0],w.values[Oe]=Jt+(un-Jt)*le}}}else for(let he=0;he<b;++he){const de=b>1?B*(h-1)+he*re:.5*(B+H)*(h-1);if(de<0||de>h-1){for(let pe=0;pe<d;pe++){const Ie=pe+he*E[2]+ce*E[1]+D*E[0];w.values[Ie]=o}continue}const le=Math.round(de),ye=Math.round(ue);for(let pe=0;pe<d;pe++){const Ie=pe+le*N[2]+ye*N[1]+q*N[0],Se=pe+he*E[2]+ce*E[1]+D*E[0];w.values[Se]=A[Ie]}}}}return w.toTensor()}sparseToDense(e,t,n,s){const{sliceRank:i,numUpdates:o,sliceSize:a,strides:c,outputSize:h}=va(t,e,n),d=!1;return this.scatter(e,t,n,h,a,o,i,c,s,d)}gatherND(e,t){const n=t.shape,s=n[n.length-1],[i,o,a,c]=gd(e,t);if(o===0)return en([],i,e.dtype);const h=new Ar([o,a],e.dtype),d=this.readSync(t.dataId),m=this.readSync(e.dataId);for(let y=0;y<o;y++){const b=[];let w=0;for(let L=0;L<s;L++){const T=d[y*s+L];w+=T*c[L],b.push(T)}if(w<0||w>=e.size/a)throw new Error(`Invalid indices: ${b} does not index into ${e.shape}`);for(let L=0;L<a;L++)h.values[y*a+L]=m[w*a+L]}return h.toTensor().reshape(i)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=va(t,e,n),h=Ne(0),d=!0;return this.scatter(e,t,n,c,o,i,s,a,h,d)}fill(e,t,n){n=n||ba(t);const s=lo(n,we(e));return s.fill(t),Fs().makeTensor(s,e,n,this)}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported for string tensors");return this.fill(e.shape,1,e.dtype)}zerosLike(e){const t=lo(e.dtype,we(e.shape));return this.makeOutput(t,e.shape,e.dtype)}linspace(e,t,n){return Ub(e,t,n)}scatter(e,t,n,s,i,o,a,c,h,d){const m=[s/i,i],y=this.readSync(e.dataId),b=this.readSync(t.dataId);if(s===0)return en([],n,t.dtype);const w=new Ar(m,t.dtype);w.values.fill(this.readSync(h.dataId)[0]);for(let L=0;L<o;L++){const T=[];let A=0;for(let N=0;N<a;N++){const E=y[L*a+N];T.push(E),A+=E*c[N]}if(A<0||A>=s/i)throw new Error(`Invalid indices: ${T} does not index into ${n}`);for(let N=0;N<i;N++)d?w.values[A*i+N]+=b[L*i+N]:w.values[A*i+N]=t.rank===0?b[0]:b[L*i+N]}return w.toTensor().reshape(n)}}function e0(e){const t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}const pH=e=>{const{x:t}=e.inputs,n=e.backend;let s=new Float32Array(we(t.shape));if(t.dtype!=="complex64"){const i=n.data.get(t.dataId).values;s=e0(i)}else{const i=n.data.get(t.dataId),o=i.complexTensorInfos.real,a=i.complexTensorInfos.imag,c=n.data.get(o.dataId).values,h=n.data.get(a.dataId).values;for(let d=0;d<c.length;d++){const m=c[d],y=h[d];s[d]=Math.hypot(m,y)}}return n.makeOutput(s,t.shape,"float32")},mH={kernelName:ge,backendName:"cpu",kernelFunc:pH};function $o(e){return(t,n,s,i,o)=>{const a=tt(t,n),c=a.length,h=Ot(a),d=we(a),m=bn(o,d),y=t.length,b=n.length,w=Ot(t),L=Ot(n),T=Lo(t,a),A=Lo(n,a);if(T.length+A.length===0)for(let 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yH={kernelName:_g,backendName:"cpu",kernelFunc:Vh};function Yh(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{dtype:o}=s;if(o==="complex64"){if(i.dtype==="complex64")return Qa({inputs:{x:i},backend:n});const a=ct(i.shape),c=Yh({inputs:{x:i},backend:n,attrs:{dtype:"float32"}}),h=li({inputs:{real:c,imag:a},backend:n});return a.dispose(),n.disposeIntermediateTensorInfo(c),h}if(i.dtype==="complex64"){const a=Vh({inputs:{input:i},backend:n}),c=Yh({inputs:{x:a},backend:n,attrs:{dtype:o}});return n.disposeIntermediateTensorInfo(a),c}if(!iy(i.dtype,o)){const a=Qa({inputs:{x:i},backend:n});return{dataId:a.dataId,shape:a.shape,dtype:o}}if(o==="int32"){const a=n.data.get(i.dataId).values,c=Int32Array.from(a);return n.makeTensorInfo(i.shape,"int32",c)}if(o==="bool"){const a=n.data.get(i.dataId).values,c=Tr([0],i.dtype),[h,d]=$o((m,y)=>m!==y?1:0)(i.shape,[],a,c,"bool");return n.makeTensorInfo(d,"bool",h)}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${o}`)}const 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pq={kernelName:Uu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i}=e,{strides:o,pad:a,dilations:c}=n,h=t,d=h.data.get(s.dataId).values,m=s.shape.length,y=h.data.get(i.dataId).values,b=i.shape.length,{batchSize:w,inHeight:L,inWidth:T,inChannels:A,outHeight:N,outWidth:E,padInfo:D,strideHeight:F,strideWidth:_,filterHeight:B,filterWidth:$,dilationHeight:H,dilationWidth:q,outShape:J}=Td(s.shape,i.shape,o,a,"NHWC",c),re=we(J),ce=J.length,ue=lo(s.dtype,re);for(let de=0;de<w;++de)for(let le=0;le<N;++le){const ye=le*F-D.top;for(let pe=0;pe<E;++pe){const Ie=pe*_-D.left;for(let Se=0;Se<A;++Se){let Ee=Number.MIN_SAFE_INTEGER;for(let Oe=0;Oe<B;++Oe){const $e=ye+Oe*H;if($e>=0&&$e<L)for(let Ye=0;Ye<$;++Ye){const et=Ie+Ye*q;if(et>=0&&et<T){const bt=Js([de,$e,et,Se],m,Ot(s.shape)),Jt=Js([Oe,Ye,Se],b,Ot(i.shape)),un=d[bt]+y[Jt];un>Ee&&(Ee=un)}}}const We=Js([de,le,pe,Se],ce,Ot(J));ue[We]=Ee}}}const he=h.write(Tr(ue,s.dtype),J,s.dtype);return{dataId:he,shape:J,dtype:s.dtype}}};const mq={kernelName:Mu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i,dy:o}=e,{strides:a,pad:c,dilations:h}=n,d=t,m=ys(s.shape,d.data.get(s.dataId).values),y=ys(i.shape,d.data.get(i.dataId).values),{batchSize:b,inHeight:w,inWidth:L,inChannels:T,outHeight:A,outWidth:N,padInfo:E,strideHeight:D,strideWidth:F,filterHeight:_,filterWidth:B,dilationHeight:$,dilationWidth:H,outShape:q}=Td(s.shape,i.shape,a,c,"NHWC",h);k(o.rank===q.length,()=>`Error in ${Mu}, dy must have the same rank as output ${q.length}, but got ${o.rank}`);const J=ys(q,d.data.get(o.dataId).values),re=ay(i.shape,i.dtype);for(let ue=0;ue<b;++ue)for(let he=0;he<A;++he){const de=he*D-E.top;for(let le=0;le<N;++le){const ye=le*F-E.left;for(let pe=0;pe<T;++pe){let Ie=Number.MIN_SAFE_INTEGER,Se=0,Ee=0;for(let We=0;We<_;++We){const Oe=de+We*$;if(Oe>=0&&Oe<w)for(let $e=0;$e<B;++$e){const Ye=ye+$e*H;if(Ye>=0&&Ye<L){const 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Ie=Number.MIN_SAFE_INTEGER,Se=de<0?0:de,Ee=ye<0?0:ye;for(let We=0;We<_;++We){const Oe=de+We*$;if(Oe>=0&&Oe<w)for(let $e=0;$e<B;++$e){const Ye=ye+$e*H;if(Ye>=0&&Ye<L){const et=m[ue][Oe][Ye][pe]+y[We][$e][pe];et>Ie&&(Ie=et,Se=Oe,Ee=Ye)}}}re[ue][Se][Ee][pe]+=J[ue][he][le][pe]}}}const ce=d.write(Tr(re,s.dtype),s.shape,s.dtype);return{dataId:ce,shape:s.shape,dtype:s.dtype}}};const gq=$o((e,t)=>e/t),yq=ec(pa,gq),VL={kernelName:pa,backendName:"cpu",kernelFunc:yq};const bq=xt(tl,e=>e>=0?e:Math.exp(e)-1),wq={kernelName:tl,backendName:"cpu",kernelFunc:bq};const Lq=Eb,Sq=Db,Iq=kb,xq=Fb,Tq=_b,Aq=Wb,vq=xt(nl,e=>{const t=Math.sign(e),n=Math.abs(e),s=1/(1+Lq*n);return t*(1-((((Aq*s+Tq)*s+xq)*s+Iq)*s+Sq)*s*Math.exp(-n*n))}),Nq={kernelName:nl,backendName:"cpu",kernelFunc:vq};function y0(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=we(d),y=bn("float32",m),b=bn("float32",m);for(let A=0;A<i;A++){const N=PL({inputs:{x:c},backend:n,attrs:{begin:[A,0],size:[1,o]}}),E=PL({inputs:{x:h},backend:n,attrs:{begin:[A,0],size:[1,o]}}),D=li({inputs:{real:N,imag:E},backend:n}),{real:F,imag:_}=Cq(D,t,n),B=Zi(F,_);for(let $=0;$<o;$++){const H=$b(B,$);y[A*o+$]=H.real,b[A*o+$]=H.imag}n.disposeIntermediateTensorInfo(N),n.disposeIntermediateTensorInfo(E),n.disposeIntermediateTensorInfo(D)}const w=n.makeTensorInfo(d,"float32",y),L=n.makeTensorInfo(d,"float32",b),T=li({inputs:{real:w,imag:L},backend:n});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(L),T}function Cq(e,t,n){const s=we(e.shape),i=n.data.get(e.dataId),o=n.data.get(i.complexTensorInfos.real.dataId).values,a=n.data.get(i.complexTensorInfos.imag.dataId).values;if(Rq(s)){const c=YL(o,a,s,t,n),h=[e.shape[0],e.shape[1]];if(t){const d=n.makeTensorInfo(h,"float32",c.real),m=n.makeTensorInfo(h,"float32",c.imag),y=n.makeTensorInfo([],"float32",_x(s,"float32")),b=Qa({inputs:{x:y},backend:n}),w=VL.kernelFunc({inputs:{a:d,b:y},backend:n}),L=VL.kernelFunc({inputs:{a:m,b},backend:n}),T=n.data.get(w.dataId).values,A=n.data.get(L.dataId).values;return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(L),{real:T,imag:A}}return c}else{const c=Zi(o,a),h=Oq(c,s,t);return vA(h)}}function Rq(e){return(e&e-1)===0}function YL(e,t,n,s,i){if(n===1)return{real:e,imag:t};const o=Zi(e,t),a=n/2,c=NA(o),h=c.real,d=c.imag,m=[h.length],y=i.makeTensorInfo(m,"float32",h),b=i.makeTensorInfo(m,"float32",d),w=li({inputs:{real:y,imag:b},backend:i}),L=CA(o),T=L.real,A=L.imag,N=[T.length],E=i.makeTensorInfo(N,"float32",T),D=i.makeTensorInfo(N,"float32",A),F=li({inputs:{real:E,imag:D},backend:i}),_=YL(h,d,a,s,i),B=_.real,$=_.imag,H=[B.length],q=i.makeTensorInfo(H,"float32",B),J=i.makeTensorInfo(H,"float32",$),re=li({inputs:{real:q,imag:J},backend:i}),ce=YL(T,A,a,s,i),ue=ce.real,he=ce.imag,de=[ue.length],le=i.makeTensorInfo(de,"float32",ue),ye=i.makeTensorInfo(de,"float32",he),pe=li({inputs:{real:le,imag:ye},backend:i}),Ie=OA(n,s),Se=[Ie.real.length],Ee=i.makeTensorInfo(Se,"float32",Ie.real),We=i.makeTensorInfo(Se,"float32",Ie.imag),Oe=li({inputs:{real:Ee,imag:We},backend:i}),$e=h0({inputs:{a:Oe,b:pe},backend:i}),Ye=n0({inputs:{a:re,b:$e},backend:i}),et=m0({inputs:{a:re,b:$e},backend:i}),bt=Vh({inputs:{input:Ye},backend:i}),Jt=Vh({inputs:{input:et},backend:i}),un=jp({inputs:{input:Ye},backend:i}),ls=jp({inputs:{input:et},backend:i}),Yt=Hh({inputs:[bt,Jt],backend:i,attrs:{axis:0}}),$n=Hh({inputs:[un,ls],backend:i,attrs:{axis:0}}),hi=i.data.get(Yt.dataId).values,vs=i.data.get($n.dataId).values;return i.disposeIntermediateTensorInfo(y),i.disposeIntermediateTensorInfo(b),i.disposeIntermediateTensorInfo(w),i.disposeIntermediateTensorInfo(E),i.disposeIntermediateTensorInfo(D),i.disposeIntermediateTensorInfo(F),i.disposeIntermediateTensorInfo(q),i.disposeIntermediateTensorInfo(J),i.disposeIntermediateTensorInfo(re),i.disposeIntermediateTensorInfo(le),i.disposeIntermediateTensorInfo(ye),i.disposeIntermediateTensorInfo(pe),i.disposeIntermediateTensorInfo(Ee),i.disposeIntermediateTensorInfo(We),i.disposeIntermediateTensorInfo(Oe),i.disposeIntermediateTensorInfo($e),i.disposeIntermediateTensorInfo(Ye),i.disposeIntermediateTensorInfo(et),i.disposeIntermediateTensorInfo(bt),i.disposeIntermediateTensorInfo(un),i.disposeIntermediateTensorInfo(Jt),i.disposeIntermediateTensorInfo(ls),i.disposeIntermediateTensorInfo(Yt),i.disposeIntermediateTensorInfo($n),{real:hi,imag:vs}}function Oq(e,t,n){const s=new Float32Array(t*2);for(let i=0;i<t;i++){let o=0,a=0;for(let c=0;c<t;c++){const h=EA(i*c,t,n),d=$b(e,c);o+=d.real*h.real-d.imag*h.imag,a+=d.real*h.imag+d.imag*h.real}n&&(o/=t,a/=t),RA(s,o,a,i)}return s}function Eq(e){const{inputs:t,backend:n}=e,{input:s}=t,i=we(s.shape),o=s.shape[s.shape.length-1],a=i/o,c=Hr({inputs:{x:s},backend:n,attrs:{shape:[a,o]}}),h=y0(c,!1,n),d=Hr({inputs:{x:h},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}const Dq={kernelName:gg,backendName:"cpu",kernelFunc:Eq};const kq={kernelName:Pu,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:s}=e,i=n,o=bn(s.dtype,we(s.shape)),[a,c,h,d]=s.shape,m=i.data.get(s.dataId).values;for(let b=0;b<a;b++){const w=b*h*c*d;for(let L=0;L<c;L++){const T=L*(h*d);for(let A=0;A<h;A++){const N=A*d;for(let E=0;E<d;E++){const D=[a,L,A,E],F=D[2],_=Math.round(h-F),B=w+T+N+E;let $=m[B];if(_>=0&&_<h){const H=_*d,q=w+T+H+E;$=m[q]}o[B]=$}}}}const y=i.write(o,s.shape,s.dtype);return{dataId:y,shape:s.shape,dtype:s.dtype}}};function Fq(e){const{inputs:t,backend:n}=e,{input:s}=t,i=we(s.shape),o=s.shape[s.shape.length-1],a=i/o,c=Hr({inputs:{x:s},backend:n,attrs:{shape:[a,o]}}),h=y0(c,!0,n),d=Hr({inputs:{x:h},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}const _q={kernelName:Lg,backendName:"cpu",kernelFunc:Fq};const Wq=xt(cl,e=>Number.isFinite(e)?1:0,"bool"),$q={kernelName:cl,backendName:"cpu",kernelFunc:Wq};const Uq=xt(ll,e=>Math.abs(e)===Infinity?1:0,"bool"),Bq={kernelName:ll,backendName:"cpu",kernelFunc:Uq};const Mq=xt(hl,e=>Number.isNaN(e)?1:0,"bool"),Pq={kernelName:hl,backendName:"cpu",kernelFunc:Mq};const zq=xt(dl,e=>Math.log1p(e)),Gq={kernelName:dl,backendName:"cpu",kernelFunc:zq};const Vq=xt(zu,e=>e?0:1,"bool"),Yq={kernelName:zu,backendName:"cpu",kernelFunc:Vq};const Hq={kernelName:pl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n;let c=s.shape;const h=c.length,d=ft(i,c);let m=d;const y=kn(m,h);let b=a.data.get(s.dataId).values;if(y!=null){const D=new Array(h);for(let F=0;F<D.length;F++)D[F]=c[y[F]];b=zL(b,c,s.dtype,y,D),m=ws(m.length,h),c=D}xe(s,"max"),es("max",m,h);const[w,L]=Cn(c,m),T=we(L),A=c0(b,T,w,s.dtype),N=a.write(A,w,s.dtype);let E=w;if(o){const D=Rn(w,d);E=D}return{dataId:N,shape:E,dtype:s.dtype}}};function qq(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;xe(i,"maxPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;k(rn(a,d),()=>`Error in maxPool: Either strides or dilations must be 1. 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ue=0;ue<b.inWidth;++ue){const he=ce-B,de=ue-_;let le=0;for(let ye=0;ye<D;ye+=N){const pe=(he+ye)/T;if(pe<0||pe>=b.outHeight||Math.floor(pe)!==pe)continue;for(let Ie=0;Ie<F;Ie+=E){const Se=(de+Ie)/A;if(Se<0||Se>=b.outWidth||Math.floor(Se)!==Se)continue;const Ee=D*F-1-L.get(J,pe,Se,re),We=ye*F+Ie,Oe=Ee===We?1:0;if(Oe===0)continue;const $e=q.get(J,pe,Se,re);le+=$e*Oe}}$.set(le,J,ce,ue,re)}return n.makeTensorInfo($.shape,$.dtype,$.values)}const Xq={kernelName:Gu,backendName:"cpu",kernelFunc:Kq};function Jq(e,t,n,s,i){const o=Ot(t),a=GL(e,t,n,o,i,"max"),c=g0(e,t,n,i,!0,s);return[a.values,c.values]}const Zq={kernelName:Vu,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,h=n;xe(s,"MaxPoolWithArgmax");const d=h.data.get(s.dataId).values,m=Fn(s.shape,i,o,[1,1],a),[y,b]=Jq(d,s.shape,s.dtype,c,m),w=h.write(y,m.outShape,s.dtype),L=h.write(b,m.outShape,s.dtype);return[{dataId:w,shape:m.outShape,dtype:s.dtype},{dataId:L,shape:m.outShape,dtype:"int32"}]}};const Qq=np,e4={kernelName:Hu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,padToMaxOutputSize:h}=n,d=t;xe(s,"NonMaxSuppressionPadded");const m=d.data.get(s.dataId).values,y=d.data.get(i.dataId).values,{selectedIndices:b,validOutputs:w}=Qq(m,y,o,a,c,h);return[b,w]}};const t4=sp,n4={kernelName:qu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}=n,d=t;xe(s,"NonMaxSuppressionWithScore");const m=d.data.get(s.dataId).values,y=d.data.get(i.dataId).values,b=o,w=a,L=c,T=h,{selectedIndices:A,selectedScores:N}=t4(m,y,b,w,L,T);return[A,N]}};const s4=$o((e,t)=>e!==t?1:0),i4=ec(Yu,s4,null,"bool"),r4={kernelName:Yu,backendName:"cpu",kernelFunc:i4};function o4(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{paddings:o,constantValue:a}=s;xe(i,"pad");const c=o.map((E,D)=>E[0]+i.shape[D]+E[1]),h=o.map(E=>E[0]),d=n.data.get(i.dataId).values,m=we(i.shape),y=i.shape.length,b=Ot(i.shape),w=we(c),L=c.length,T=Ot(c),A=bn(i.dtype,w);a!==0&&A.fill(a);for(let E=0;E<m;E++){const D=La(E,y,b),F=D.map((B,$)=>B+h[$]),_=Js(F,L,T);A[_]=d[E]}const N=n.write(A,c,i.dtype);return{dataId:N,shape:c,dtype:i.dtype}}const b0={kernelName:ju,backendName:"cpu",kernelFunc:o4};const a4=xt(gl,e=>1/e),c4={kernelName:gl,backendName:"cpu",kernelFunc:a4};const l4={kernelName:ed,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:s}=e,{radians:i,fillValue:o,center:a}=t,c=n,h=bn(s.dtype,we(s.shape)),[d,m,y,b]=s.shape,[w,L]=Cb(a,m,y),T=255,A=Math.sin(i),N=Math.cos(i),E=c.data.get(s.dataId).values;for(let F=0;F<d;F++){const _=F*y*m*b;for(let B=0;B<m;B++){const $=B*(y*b);for(let H=0;H<y;H++){const q=H*b;for(let J=0;J<b;J++){const re=[d,B,H,J],ce=re[2],ue=re[1];let he=(ce-w)*N-(ue-L)*A,de=(ce-w)*A+(ue-L)*N;he=Math.round(he+w),de=Math.round(de+L);let le=o;if(typeof o!="number"&&(J===3?le=T:le=o[J]),he>=0&&he<y&&de>=0&&de<m){const pe=de*(y*b),Ie=he*b,Se=_+pe+Ie+J;le=E[Se]}const ye=_+$+q+J;h[ye]=le}}}}const D=c.write(h,s.shape,s.dtype);return{dataId:D,shape:s.shape,dtype:s.dtype}}};const h4=xt(bl,e=>{const t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2===0?t:t+1}),u4={kernelName:bl,backendName:"cpu",kernelFunc:h4};const d4=rp,p4=op,m4=xt(Ll,e=>e>=0?p4*e:d4*(Math.exp(e)-1)),f4={kernelName:Ll,backendName:"cpu",kernelFunc:m4};const g4=xt(xl,e=>1/(1+Math.exp(-e))),y4={kernelName:xl,backendName:"cpu",kernelFunc:g4};const b4=xt(Il,e=>e<0?-1:e>0?1:0),w4={kernelName:Il,backendName:"cpu",kernelFunc:b4};const L4=xt(ma,e=>Math.sin(e)),S4={kernelName:ma,backendName:"cpu",kernelFunc:L4};const 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float result = ${s};
setOutput(result);
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setOutput(result);
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int outIdx = coords[1];
int inOffset = outIdx * ${s};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${s}; i++) {
int inIdx = ${c};
float candidate = getA(batch, inIdx);
if (candidate ${a} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}}function v0(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function cs(e,t){return t===1?[e]:v0(e,t)}function Pj(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}function Wn(){let e,t,n,s,i,o,a,c,h,d;return C().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",i="texture",o="outputColor",a="out vec4 outputColor;",c=`
bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,h="",d=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",s="varying",i="texture2D",o="gl_FragColor",a="",c=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,h=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,d=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
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int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}const N0=`
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:C0}=Bb;function zj(e,t,n,s){const i=[];e.forEach(L=>{const T=we(L.shapeInfo.logicalShape);L.shapeInfo.isUniform?i.push(`uniform float ${L.name}${T>1?`[${T}]`:""};`):(i.push(`uniform sampler2D ${L.name};`),i.push(`uniform int offset${L.name};`))});const o=i.join(`
`),a=e.map(L=>Gj(L,t,s)).join(`
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float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function qj(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function jj(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function Kj(e){const t=`${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);
}
${Xj}
${Jj}
${Zj}
`;return t}const Xj=`
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);
}
`,Jj=`
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);
}
`,Zj=`
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);
}
`,Qj=`
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 O0(){return`
int getOutputCoords() {
return 0;
}
`}function eK(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 tK(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 * ${t[1]} + resTexRC.y;
}
`}function nK(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),i=s*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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, ${s}) * 2;
return ivec3(b, r, c);
}
`}function sK(e,t){const n=Bo(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function iK(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[e.length-1]/2),i=s*Math.ceil(e[e.length-2]/2);let o=i,a="",c="b, r, c";for(let h=2;h<e.length-1;h++)o*=e[e.length-h-1],a=`
int b${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}(${c});
}
`}function rK(e,t){const n=Bo(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function oK(e,t){const n=Bo(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function aK(e,t){const n=Bo(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function cK(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(ot(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${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, ${s}) * 2;
return ivec2(r, c);
}
`}function lK(e,t){return ot(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
return ivec2(r, c);
}
`}function Mo(e){return`offset${e}`}function hK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Wn();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function uK(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=Mo(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${o}, ${a}, ${c});
return sampleTexture(${t}, uv);
}
`}function dK(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=Wn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${o.texture2D}(${t}, uv);
}
`}function pK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${ac(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=Mo(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 mK(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=Wn();if(i!=null&&ot(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 fK(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&&ot(t,i)){const y=i[0],b=i[1];return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${y}.0);
return sampleTexture(${n}, uv);
}
`}const{newShape:o,keptDims:a}=Sr(t),c=o;if(c.length<t.length){const y=cc(e,c),b=["row","col"];return`
${oc(y)}
float ${s}(int row, int col) {
return ${s}(${lc(b,a)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${ac(e)}
}
`;const h=i[0],d=i[1],m=Mo(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 gK(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 y=t.slice(1),b=[1,2],w=cc(e,y),L=["b","row","col"];return`
${R0(w)}
vec4 ${s}(int b, int row, int col) {
return ${s}(${lc(L,b)});
}
`}const a=o[0],c=o[1],h=Math.ceil(t[2]/2),d=h*Math.ceil(t[1]/2),m=Wn();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 yK(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}=Sr(t),h=a;if(h.length<t.length){const L=cc(e,h),T=["row","col","depth"];return`
${oc(L)}
float ${s}(int row, int col, int depth) {
return ${s}(${lc(T,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)));
${ac(e)}
}
`;const d=e.shapeInfo.texShape,m=d[0],y=d[1],b=e.shapeInfo.flatOffset;if(y===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(${y}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;if(y===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(${y}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;const w=Mo(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}, ${y}, index);
return sampleTexture(${n}, uv);
}
`}function bK(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),y="int b, int row, int col",b=`b * ${m} + (row / 2) * ${d} + (col / 2)`;for(let L=2;L<n-1;L++)y=`int b${L}, `+y,m*=t[n-L-1],b=`b${L} * ${m} + `+b;const w=Wn();return`
vec4 ${i}(${y}) {
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 wK(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}=Sr(t);if(c.length<t.length){const L=cc(e,c),T=["row","col","depth","depth2"];return`
${oc(L)}
float ${s}(int row, int col, int depth, int depth2) {
return ${s}(${lc(T,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)));
${ac(e)}
}
`;const d=e.shapeInfo.flatOffset,m=e.shapeInfo.texShape,y=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, ${y}.0);
return sampleTexture(${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, ${y}.0);
return sampleTexture(${n}, uv);
}
`;const w=Mo(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 * ${i} + depth2;
vec2 uv = uvFromFlat(${y}, ${b}, index + ${w});
return sampleTexture(${n}, uv);
}
`}function LK(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}=Sr(t);if(h.length<t.length){const T=cc(e,h),A=["row","col","depth","depth2","depth3"];return`
${oc(T)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${lc(A,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;
${ac(e)}
}
`;const m=e.shapeInfo.flatOffset,y=e.shapeInfo.texShape,b=y[0],w=y[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=Mo(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 SK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=Sr(t);if(i.length<t.length){const A=cc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
${oc(A)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${lc(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)));
${ac(e)}
}
`;const y=e.shapeInfo.flatOffset,b=e.shapeInfo.texShape,w=b[0],L=b[1];if(L===m&&y==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&&y==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 T=Mo(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 + ${T};
vec2 uv = uvFromFlat(${w}, ${L}, index);
return sampleTexture(${n}, uv);
}
`}function ac(e){const t=e.name,n=we(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function IK(e,t){const n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),i="get"+s+"AtOutCoords",o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,c=C0(e.shapeInfo.logicalShape,t.logicalShape),h=Et(a),d=a-o;let m;const y=["x","y","z","w","u","v"];o===0?m="":a<2&&c.length>=1?m="coords = 0;":m=c.map(E=>`coords.${y[E+d]} = 0;`).join(`
`);let b="";a<2&&o>0?b="coords":b=e.shapeInfo.logicalShape.map((E,D)=>`coords.${y[D+d]}`).join(", ");let w="return outputValue;";const L=we(e.shapeInfo.logicalShape),T=L===1,A=we(t.logicalShape),N=A===1;if(o===1&&!T&&!N)w=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(T&&!N)a===1?w=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:w=`
return vec4(outputValue.x);
`;else if(c.length){const E=o-2,D=o-1;c.indexOf(E)>-1&&c.indexOf(D)>-1?w="return vec4(outputValue.x);":c.indexOf(E)>-1?w="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":c.indexOf(D)>-1&&(w="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${i}() {
${h} coords = getOutputCoords();
${m}
vec4 outputValue = get${s}(${b});
${w}
}
`}function xK(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&&ot(a,o))return`
float ${i}() {
return sampleTexture(${n}, resultUV);
}
`;const d=Et(h),m=C0(e.shapeInfo.logicalShape,t.logicalShape),y=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(T=>`coords.${w[T+y]} = 0;`).join(`
`);let L="";return h<2&&c>0?L="coords":L=e.shapeInfo.logicalShape.map((T,A)=>`coords.${w[A+y]}`).join(", "),`
float ${i}() {
${d} coords = getOutputCoords();
${b}
return get${s}(${L});
}
`}function Et(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 ${e} is not yet supported`)}function cc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function lc(e,t){return t.map(n=>e[n]).join(", ")}class TK{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k(e.length>2,()=>`Packed arg${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=Et(c),d=cs("coords",c);let m,y;if(o===1){y=c+1;const $=Et(y);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 y=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,y),w="."+b[y-1],L=b.map($=>"int "+$),T=cs("sourceLocR",y-1).concat("inIdx.r"),A=cs("sourceLocG",y-1).concat("inIdx.g"),N=cs("sourceLocB",y-1).concat("inIdx.b"),E=cs("sourceLocA",y-1).concat("inIdx.a"),D=n==="max"?"greaterThan":"lessThan",F=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${T.join()}),
getBestIndicesAChannel(${A.join()}),
getBestIndicesAChannel(${N.join()}),
getBestIndicesAChannel(${E.join()})));`,_=`vec4(
getAChannel(${T.join()}),
hasNextCol ? getAChannel(${A.join()}) : 0.,
hasNextRow ? getAChannel(${N.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${E.join()}) : 0.)`,B=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()}));
}
${B}
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 = ${_};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${F}
vec4 candidate = ${_};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${D}(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 AK{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,y=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${d}, ${m});
const float avgMultiplier = float(${y});
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 >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}}class vK{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=e.dilationDepth,h=e.dilationHeight,d=e.dilationWidth,m=e.effectiveFilterDepth,y=e.effectiveFilterHeight,b=e.effectiveFilterWidth,w=m-1-e.padInfo.front,L=y-1-e.padInfo.top,T=b-1-e.padInfo.left,A=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${w}, ${L}, ${T});
const float avgMultiplier = float(${A});
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 < ${y};
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 E0={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class D0{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=tt(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}}const k0=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,QL="return a + b;",eS="return a - b;",F0="return a * b;",NK=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,CK=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,yee="return (a - b) * (a - b);",RK="return float(a == b);",OK="return float(a != b);",EK="return float(a < b);",DK="return float(a <= b);",kK="return float(a > b);",FK="return float(a >= b);",_K="return float(a >= 1.0 && b >= 1.0);",WK="return float(a >= 1.0 || b >= 1.0);",$K=k0+`
return max(a, b);
`,UK=k0+`
return min(a, b);
`,BK=`if (b == 0.0) return NAN;
return mod(a, b);`,MK="return (b >= 1.0) ? a : a * (b + 1.0);",_0="return (a < 0.) ? b * a : a;";class hn{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=tt(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}}const nm=`
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;
`,PK=`
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);
`,zK=`
// 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));
`+nm+`
return result;
`,W0=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`,GK=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,VK=`
return vec4(equal(a, b));
`,YK=`
return vec4(notEqual(a, b));
`,HK=`
return vec4(lessThan(a, b));
`,qK=`
return vec4(lessThanEqual(a, b));
`,jK=`
return vec4(greaterThan(a, b));
`,KK=`
return vec4(greaterThanEqual(a, b));
`,XK=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,JK=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,ZK=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+nm+`
return result;
`,QK=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+nm+`
return result;
`,e5=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+nm+`
return result;
`;class qr{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=tt(t,n);const i=this.outputShape.length;let o="";if(s)if(i===0||we(this.outputShape)===1)o=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else{const a=Et(i);if(o=`
${a} coords = getOutputCoords();
`,i===1)o+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{const c=cs("coords",i);o+=`
bool nextRowOutOfBounds =
(${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);
${o}
setOutput(result);
}
`}}class t5{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(n,s)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(s,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(s,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}}class n5{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(n,s)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(s,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(s,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}}class s5{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}}class i5{constructor(e){this.outputShape=[],this.outputShape=Or(e,1),this.variableNames=e.map((o,a)=>`T${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;
${n.join(`
`)}
}
`}}class r5{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=Or(e,t);const n=this.outputShape,s=n.length,i=Et(s),o=cs("coords",s),a=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((L,T)=>`T${T}`);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 y=`if (${h} < ${c[0]}) {
return getChannel(
getT0(${m}), vec2(${d.join()}));
}`;for(let L=1;L<c.length;L++){const T=c[L-1];y+=`
if (${h} < ${c[L]} && ${h} >= ${c[L-1]}) {
return getChannel(
getT${L}(${sm(a,h,T)}),
vec2(${sm(d,h,T)}));
}`}const b=c.length,w=c[c.length-1];y+=`
return getChannel(
getT${b}(${sm(a,h,w)}),
vec2(${sm(d,h,w)}));`,this.userCode=`
float getValue(${a.map(L=>"int "+L)}) {
${y}
}
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 sm(e,t,n){const s=e.indexOf(t),i=e.map((o,a)=>a===s?`${o} - ${n}`:o);return i.join()}class o5{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 a5{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 c5{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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}}class l5{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=t-1-e.padInfo.front,h=n-1-e.padInfo.top,d=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${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 h5{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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}}class u5{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.top,a=n-1-e.padInfo.left,c=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${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 $0{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,y=e.filterWidth,b=Math.floor(e.inChannels/4)*4,w=e.inChannels%4,L=e.dataFormat==="channelsLast",T=L?1:2,A=L?2:3,N=L?3:1;let E="",D="";n&&(s?E=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:E=`
float activation(float x) {
${n}
}
`,D="result = activation(result);");const F=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${E}
const ivec2 strides = ivec2(${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[${T}], coords[${A}]) * 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 < ${y}; 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;
${F}
${D}
setOutput(result);
}
`}}class d5{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=e.dilationDepth,h=e.dilationHeight,d=e.dilationWidth,m=e.filterDepth,y=e.filterHeight,b=e.filterWidth,w=Math.floor(e.inChannels/4)*4,L=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${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 < ${y}; 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 U0{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,y=e.dilationWidth,b=e.filterHeight,w=e.filterWidth,L=e.outChannels/e.inChannels;let T="",A="";n&&(s?T=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:T=`
float activation(float x) {
${n}
}
`,A="result = activation(result);");const N=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${T}
const ivec2 strides = ivec2(${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 * ${y};
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}
${A}
setOutput(result);
}
`}}class B0{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;const i=e.inHeight,o=e.inWidth,a=e.padInfo.top,c=e.padInfo.left,h=e.strideHeight,d=e.strideWidth,m=e.dilationHeight,y=e.dilationWidth,b=e.filterHeight,w=e.filterWidth,L=w;let T="int xR; int xC; int xCOffset;";for(let D=0;D<b;D++)for(let F=0;F<w;F++)T+=`
vec4 xTexelR${D}C${F*2} = vec4(0.);
vec4 wR${D}C${F} = vec4(0.);
vec4 xR${D}C${F} = vec4(0.);`;for(let D=0;D<b;D++)for(let F=0;F<L;F++){const _=F*2;if(T+=`
xR = xRCorner + ${D*m};
xC = xCCorner + ${_*y};
`,d===1){if(_<w&&(c%2===1?T+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${i} && xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${o}) {
xTexelR${D}C${_}.zw = vec2(0.);
}
} else {
xTexelR${D}C${_} = 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 >= ${o}) {
previous.zw = vec2(0.);
}
xR${D}C${_} = vec4(previous.zw, xTexelR${D}C${_}.xy);
} else {
xR${D}C${_} = vec4(0, 0, xTexelR${D}C${_}.xy);
}
`:T+=`
if(xR >= 0 && xR < ${i} && xC >= 0 && xC < ${o}) {
xTexelR${D}C${_} = getX(batch, xR, xC, d1);
} else {
xTexelR${D}C${_} = vec4(0.);
}
xR${D}C${_} = xTexelR${D}C${_};
`,_+1<w)){const B=c%2===0?ny(y):y;y%2===0&&c%2===1||y%2!==0&&c%2!==1?(T+=`
xCOffset = xC + ${c%2} + ${B};
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_+2} = getX(batch, xR, xCOffset, d1);
}
`,y>1&&(T+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${D}C${_} = vec4(0.);
}
`),T+=`
xR${D}C${_+1} = vec4(
xTexelR${D}C${_}.zw, xTexelR${D}C${_+2}.xy);
`):T+=`
xCOffset = xC + ${B};
if(xR >= 0 && xR < ${i} &&
xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_+2} = getX(batch, xR, xCOffset, d1);
}
xR${D}C${_+1} = xTexelR${D}C${_+2};
`}}else _<w&&(T+=`
if(xR >= 0 && xR < ${i}) {
`,c%2===1?(T+=`
xCOffset = xC + 1 - ${d};
if(xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${D}C${_} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${o}) {
xTexelR${D}C${_+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${D}C${_+2} = vec4(0.);
}
xR${D}C${_} = vec4(
xTexelR${D}C${_}.zw, xTexelR${D}C${_+2}.zw);
`,_+1<w&&(T+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${d};
if(xCOffset >= 0 && xCOffset < ${o}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${D}C${_+1} = vec4(xTexelR${D}C${_+2}.xy, final.xy);
`)):(T+=`
if(xC >= 0 && xC < ${o}) {
xTexelR${D}C${_} = getX(batch, xR, xC, d1);
} else {
xTexelR${D}C${_} = vec4(0.);
}
xCOffset = xC + ${d};
if(xCOffset >= 0 && xCOffset < ${o}) {
xTexelR${D}C${_+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${D}C${_+2} = vec4(0.);
}
xR${D}C${_} = vec4(
xTexelR${D}C${_}.xy, xTexelR${D}C${_+2}.xy);
`,_+1<w&&(T+=`
xR${D}C${_+1} = vec4(
xTexelR${D}C${_}.zw, xTexelR${D}C${_+2}.zw);
`)),T+="}");_<w&&(T+=`
vec4 wTexelR${D}C${_} = getW(${D}, ${_}, d1, q);
wR${D}C${_} = vec4(wTexelR${D}C${_}.xz, wTexelR${D}C${_}.xz);
`,_+1<w&&(T+=`
vec4 wTexelR${D}C${_+1} = getW(${D}, ${_+1}, d1, q);
wR${D}C${_+1} =
vec4(wTexelR${D}C${_+1}.xz, wTexelR${D}C${_+1}.xz);`))}for(let D=0;D<b;D++)for(let F=0;F<w;F++)T+=`dotProd += xR${D}C${F} * wR${D}C${F};`;let A="",N="";n&&(s?A=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:A=`vec4 activation(vec4 x) {
${n}
}`,N="result = activation(result);");const E=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${A}
const ivec2 strides = ivec2(${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.);
${T}
vec4 result = dotProd;
${E}
${N}
setOutput(result);
}
`}}class p5{constructor(e,t,n,s,i){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[o,a,c,h]=e,[d]=t,[m,y]=n;this.outputShape=[d,m,y,h];const b=s==="bilinear"?1:0,[w,L]=[`${a-1}.0`,`${c-1}.0`],[T,A,N]=m>1?[`${(a-1)/(m-1)}`,"(y2-y1) * height_ratio",`y1*${w} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${w}`],[E,D,F]=y>1?[`${(c-1)/(y-1)}`,"(x2-x1) * width_ratio",`x1*${L} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${L}`];this.userCode=`
const float height_ratio = float(${T});
const float width_ratio = float(${E});
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 = ${A};
float width_scale = ${D};
float in_y = ${N};
if( in_y < 0.0 || in_y > ${w} ) {
setOutput(float(${i}));
return;
}
float in_x = ${F};
if( in_x < 0.0 || in_x > ${L} ) {
setOutput(float(${i}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${b} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}}class M0{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;const s=e.length,i=t?"0.0":`getX(${P0(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() {
${Et(s)} coords = getOutputCoords();
int end = ${z0(s,"coords")};
float val = ${i};
int pow2 = int(pow(2.0, index));
if (${a}) {
int idx = ${c};
${z0(s,"coords")} = idx;
val += getX(${P0(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 P0(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 ${e} is not yet supported`)}function z0(e,t){if(e===1)return`${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 m5{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=qh.DENSE;const t=Kh(e),n=Wn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Bo(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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 f5{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=qh.DENSE;const t=Kh(e),n=Wn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Bo(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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 g5{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 y5{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 b5{constructor(e){this.variableNames=["A"],this.outTexUsage=As.DOWNLOAD;const t=Wn();this.outputShape=e,this.userCode=`
${N0}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}}class w5{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=As.DOWNLOAD;const t=Wn();this.outputShape=e,this.userCode=`
${N0}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}}class L5{constructor(e,t,n=!1){this.variableNames=["A"];const s=Wn(),[i,o]=t;this.outputShape=e;let a="result";n&&(a="floor(result * 255. + 0.5)"),this.userCode=`
${ZL(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 S5{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const s=Wn(),[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=`
${ZL(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};
}
`}}const G0={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"};class V0{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";this.userCode=`
const float exponentMultiplier = ${i};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${e}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${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]));
}
`}}class I5{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 x5{constructor(e,t,n){this.variableNames=["A","indices"];const s=e.slice();s[n]=t,this.outputShape=s,this.rank=s.length;const i=Et(this.rank),o=T5(e,n);this.userCode=`
void main() {
${i} resRC = getOutputCoords();
setOutput(getA(${o}));
}
`}}function T5(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 A5{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;const s=Et(t.length),i=Et(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 * ${o};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}}function v5(e){const t=Wn(),n=`${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 tj(e,n)}function N5(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 aj(e,t)}function C5(e){const t=new Uint16Array([0,1,2,2,1,3]);return cj(e,t)}function Jh(e,t,n,s,i,o){hj(t,n);const a=lj(e),c=e.TEXTURE_2D;return Re(e,()=>e.bindTexture(c,a)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MIN_FILTER,e.NEAREST)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MAG_FILTER,e.NEAREST)),Re(e,()=>e.texImage2D(c,0,s,t,n,0,i,o,null)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null)),a}function Y0(e){return e.internalFormatFloat}function R5(e,t,n,s){const[i,o]=jh(t,n);return Jh(e,i,o,Y0(s),s.textureFormatFloat,e.FLOAT)}function H0(e){return e.internalFormatHalfFloat}function O5(e,t,n,s){const[i,o]=jh(t,n);return Jh(e,i,o,H0(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function q0(e){return e.downloadTextureFormat}function E5(e,t,n,s){const[i,o]=jh(t,n);return Jh(e,i,o,q0(s),e.RGBA,e.UNSIGNED_BYTE)}function j0(e){return e.internalFormatPackedFloat}function D5(e,t,n,s){const[i,o]=sc(t,n);return Jh(e,i,o,j0(s),e.RGBA,e.FLOAT)}function K0(e){return e.internalFormatPackedHalfFloat}function k5(e,t,n,s){const[i,o]=sc(t,n);return Jh(e,i,o,K0(s),e.RGBA,s.textureTypeHalfFloat)}function F5(e,t,n){const s=0,i=3*4,o=3*4+2*4;Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=S0(e,t,"clipSpacePos",n,3,o,s);return a&&S0(e,t,"uv",n,2,o,i)}function _5(e,t,n,s,i,o){Re(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),Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,h,n,s,0,e.RGBA,c,a)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function W5(e,t,n){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function $5(e,t,n,s){const i=e.createBuffer();Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const o=4,a=4,c=o*a*t*n;return Re(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,c,e.STREAM_READ)),Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function U5(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 B5(e,t,n,s){const[i,o]=jh(t,n),a=4,c=new Uint8Array(j4(t*n,a));return Re(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function M5(e,t,n,s,i,o,a,c){const h=e,d=new Float32Array(K4(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 P5(e,t,n){const s=new Float32Array(t*n*4);return Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}class z5{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=C().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Y4(t,e)):this.gl=_i(t);let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(C().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",o="OES_texture_half_float";if(this.textureFloatExtension=Kp(this.gl,i),Ps(this.gl,o))this.textureHalfFloatExtension=Kp(this.gl,o);else if(C().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),Ps(this.gl,s))this.colorBufferHalfFloatExtension=Kp(this.gl,s);else if(C().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",Ps(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ps(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=N5(this.gl),this.indexBuffer=C5(this.gl),this.framebuffer=uj(this.gl),this.textureConfig=qL(this.gl,this.textureHalfFloatExtension)}get debug(){return C().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;Re(e,()=>e.finish()),Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Re(e,()=>e.deleteFramebuffer(this.framebuffer)),Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Re(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Re(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),R5(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),O5(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),E5(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),W5(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),_5(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),k5(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),D5(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(I0(this.gl,this.framebuffer),this.outputTexture=null),Re(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>B5(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return M5(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return U5(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=$5(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(C().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 C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>P5(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=nj(t,e),s=v5(t),i=rj(t);return Re(t,()=>t.attachShader(i,s)),Re(t,()=>t.attachShader(i,n)),oj(t,i),this.debug&&jL(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=F5(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Re(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&jL(this.gl,this.program),Re(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?pj(this.gl,e,t):mj(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Re(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(),fj(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=sc(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&&jL(this.gl,this.program),Xp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Re(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Re(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Kp(this.gl,C().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(C().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(C().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 sy(()=>this.disposed||this.isQueryAvailable(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,C().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=G5(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;sy(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),KL(this.gl,e,this.framebuffer),this.debug&&Xp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(KL(this.gl,this.outputTexture,this.framebuffer),this.debug&&Xp(this.gl)):I0(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;KL(s,e,this.framebuffer),this.debug&&Xp(s),this.outputTexture=e,Re(s,()=>s.viewport(0,0,t,n)),Re(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Re(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 G5(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function V5(e,t,n,s){const i=t.userCode,o=n.map((w,L)=>{const T={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&&(T.flatOffset=w.texData.slice.flatOffset),{name:t.variableNames[L],shapeInfo:T}}),a=o.map(w=>w.shapeInfo),c={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},h=zj(o,c,i,t.packedInputs),d=e.createProgram(h);let m=null;const y=e.getUniformLocation(d,"NAN",!1);C().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],T=!1;b[L]=e.getUniformLocation(d,L,T),b[`offset${L}`]=e.getUniformLocation(d,`offset${L}`,T)}return{program:t,source:h,webGLProgram:d,uniformLocations:b,inShapeInfos:a,outShapeInfo:c,infLoc:m,nanLoc:y}}function X0(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(!ot(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(!ot(c,h))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${c} and ${h} must match`)})}function Y5(e,t,n,s,i){X0(t.inShapeInfos,n),X0([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),C().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,h)=>{const d=t.program.variableNames[h],m=t.uniformLocations[d],y=t.uniformLocations[`offset${d}`];if(m==null)return;if(c.isUniform){if(we(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&&y!=null&&e.gl.uniform1i(y,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,m,h)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function H5(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 q5{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:y}=n,{left:b,top:w}=c,L=i*s,T=Wn(),A=y==="channelsLast",N=A?0:1,E=A?1:2;let D="";for(let F=0;F<=1;F++)for(let _=0;_<=1;_++)D+=`
blockIndex = rc.y + ${_};
pos = rc.x + ${F};
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[E]} && d1 >= 0) {
ch = int(mod(float(pos), ${i}.));
if (${A}) {
innerDims = vec2(d1, ch);
result[${F*2+_}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${F*2+_}] = 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;
${D}
${T.output} = result;
}
`}}class j5{constructor(e,t,n,s,i){this.variableNames=["x"],this.outputShape=[];const o=t,a=e[3]-1;this.outputShape=e;let c;const h=`float(${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 K5{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 * ${i});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}}class X5{constructor(e,t,n,s,i){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const o=t,a=e[3]-1;this.outputShape=e;let c;const h=`float(${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 * ${c};
setOutput(result);
}
`}}class J5{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,i=e.effectiveFilterHeight,o=e.effectiveFilterWidth,a=i-1-e.padInfo.top,c=o-1-e.padInfo.left,h=i*o-1;this.userCode=`
const ivec2 pads = ivec2(${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 * ${o} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}}class Z5{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,i=e.dilationDepth,o=e.dilationHeight,a=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,m=c-1-e.padInfo.front,y=h-1-e.padInfo.top,b=d-1-e.padInfo.left,w=c*h*d-1;this.userCode=`
const ivec3 pads = ivec3(${m}, ${y}, ${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 tS{constructor(e,t,n=!1,s=!1,i=!1,o=null,a=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;const c=n?e[1]:e[2],h=Math.ceil(c/2),d=n?"i * 2, rc.y":"rc.y, i * 2",m=s?"rc.z, i * 2":"i * 2, rc.z",y=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],b=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let w="",L="";o&&(a?w=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:w=`vec4 activation(vec4 x) {
${o}
}`,L="result = activation(result);");const T=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.userCode=`
${w}
const float sharedDimension = ${h}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${h}; i++) {
vec4 a = getMatrixA(rc.x, ${d});
vec4 b = getMatrixB(rc.x, ${m});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${y[0]} * ${b[0]});
result += (${y[1]} * ${b[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${T}
${L}
setOutput(result);
}
`}}class Q5{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 e8{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 t8{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=cs("rc",t),s=Et(t),i=s8(t,e,n),o=i8(t,e[e.length-1],e[e.length-2],n),a=r8(e,n);this.userCode=`
void main() {
${s} rc = getOutputCoords();
if(${i}) {
setOutput(vec4(0));
} else {
${o}
setOutput(vec4(${a}));
}
}
`}}}function n8(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 s8(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 i8(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 r8(e,t){const n=e.length,s=n8(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 o8{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((h,d)=>h[0]+e[d]+h[1]);const s=e.length,i=Et(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 a8{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((L,T)=>L[0]+e[T]+L[1]);const s=e.length,i=Et(s),o=t.map(L=>L[0]).join(","),a=t.map((L,T)=>L[0]+e[T]).join(","),c=cs("rc",s),h=cs("source",s),d=`${c[s-1]} < ${this.outputShape[s-1]}`,m=s===1?"source":`vec2(${h.slice(-2).join()})`,y=[`${i} rc = outputLoc;`,`${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,T=s===1?2:4;L<T;L++)w+=`
${y[L]}
if (${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 Zh{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,y=e.effectiveFilterWidth,b=e.padInfo.top,w=e.padInfo.left;this.outputShape=e.outShape;const L=t==="avg",T=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(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 < ${y};
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?T:A:`wR * ${y} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}const E="max";let D=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(D="avgValue / count");const F=Math.floor(o/4)*4,_=o%4,B=`
if (${L}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${E}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${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 < ${F}; 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)
);
${B}
}
int xC = xCCorner + ${F};
if (${_===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${B}
} else if (${_===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${d}, d),
initializationValue,
initializationValue
);
${B}
} else if (${_===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${d}, d),
getValue(batch, xR, xC + 2 * ${d}, d),
initializationValue
);
${B}
}
}
setOutput(${D});
}
`}}class nS{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,y=e.dilationWidth,b=e.effectiveFilterDepth,w=e.effectiveFilterHeight,L=e.effectiveFilterWidth,T=e.padInfo.front,A=e.padInfo.top,N=e.padInfo.left;this.outputShape=e.outShape;const E=t==="avg";let D="0.0";if(E||(D="-1.0 / 1e-20"),n){const q=">=";this.userCode=`
const ivec3 strides =
ivec3(${a}, ${c}, ${h});
const ivec3 pads = ivec3(${T}, ${A}, ${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 += ${y}) {
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 ${q} 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 * ${L} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}const F="max";let _=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");const B=Math.floor(o/4)*4,$=o%4,H=`
if (${E}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${F}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${a}, ${c}, ${h});
const ivec3 pads = ivec3(${T}, ${A}, ${N});
const float initializationValue = ${D};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${D});
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 < ${B}; wC += 4) {
int xC = xCCorner + wC * ${y};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${y}, ch),
getValue(batch, xD, xR, xC + 2 * ${y}, ch),
getValue(batch, xD, xR, xC + 3 * ${y}, ch)
);
${H}
}
int xC = xCCorner + ${B};
if (${$===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${H}
} else if (${$===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${y}, ch),
initializationValue,
initializationValue
);
${H}
} else if (${$===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${y}, ch),
getValue(batch, xD, xR, xC + 2 * ${y}, ch),
initializationValue
);
${H}
}
}
setOutput(${_});
}
}
`}}class J0{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}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?h="sumValue":t==="prod"?h="prodValue":t==="all"?h="allValue":t==="any"&&(h="anyValue");const d=Math.floor(n/4)*4,m=n%4;let y=`
if (${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",y=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,b="bvec4"):t==="any"&&(a="0.0",y=`
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)
);
${y}
}
int inIdx = inOffset + ${d};
if (${m===1}) {
${b} values = ${b}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${y}
} else if (${m===2}) {
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${y}
} else if (${m===3}) {
${b} values = ${b}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${y}
}
setOutput(${h});
}
`}}class Z0{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=`
${c8(t)}
${ZL(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${n}
setOutput(result);
}
`}}function c8(e){const t=Bo(["r","c","d"],e);return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t}
return ivec3(r, c, d);
}
`}class l8{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;const[,s,i]=t.shape,[,o,a]=e.shape,c=[n&&o>1?s-1:s,n&&a>1?i-1:i],h=[n&&o>1?o-1:o,n&&a>1?a-1:a],d=c[0]/h[0],m=c[1]/h[1],y=1/d,b=1/m,w=Math.ceil(y)*2+2,L=Math.ceil(b)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${d});
const float widthScale = float(${m});
const float invHeightScale = float(${y});
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 h8{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 u8{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 d8{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],y=1/d,b=1/m,w=Math.ceil(y)*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(${m});
const float invHeightScale = float(${y});
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 p8{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 + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}}class m8{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=Et(n);this.userCode=`
void main() {
${o} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}}class f8{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-${n} tensor is not yet supported`);this.outputShape=e;const s=cs("rc",n),i=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,o=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,a=Et(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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 = ${m(s.slice())};
}
}
setOutput(result);
}
`;function c(w){return y(w)}function h(w){return w[n-1]="("+w[n-1]+" + 1)",y(w)}function d(w){return w[n-2]="("+w[n-2]+" + 1)",y(w)}function m(w){return w[n-1]="("+w[n-1]+" + 1)",w[n-2]="("+w[n-2]+" + 1)",y(w)}function y(w){const L=e.map((N,E)=>b(E,w)),T=L.join(","),A=L.slice(-2).join(",");return`getChannel(getX(${T}), vec2(${A}))`}function b(w,L){return t.indexOf(w)!==-1&&e[w]!==1?`${e[w]} - ${L[w]} - 1`:`${L[w]}`}}}class Q0{constructor(e,t,n,s,i,o,a=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=o;const c=Et(i.length),h=Et(o.length);let d="";n===1?d="i":n===2&&(d="i, j");const m=`getIndices(${d})`;let y="";s===1?y="i":s===2&&(y="i, coords[1]");const b=`getUpdates(${y})`,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 g8{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,y=`
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
);
${y}
}
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
);
${y}
} else if (${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
);
${y}
} else if (${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
);
${y}
}
setOutput(${h});
}
`}}class y8{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=Et(n);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${i}));
} else {
setOutput(getB(${i}));
}
}
`}}class b8{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=Et(this.rank),n=`uniform int start[${this.rank}];`,s=w8(this.rank);let i;const o=e.map((a,c)=>`sourceLoc.${sS[c]} = start[${c}] + coords.${sS[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 sS=["x","y","z","w","u","v"];function w8(e){if(e===1)return"sourceLoc";if(e<=6)return sS.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}class L8{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;const t=Et(this.rank),n=cs("coords",this.rank),s=cs("sourceLoc",this.rank),i=this.rank===1?"sourceLoc":`vec2(${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 (${e.length})`);return(t,n)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}class S8{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const s=n.length,i=Et(n.length),o=Et(n.length);let a="";if(s===1)a="coords * strides + begin";else{let c=0;a=n.map((h,d)=>(c++,n.length===1?`coords * strides[${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 I8{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=tC(t,n),i=nC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=eC(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===Sn.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Sn.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Sn.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=tC(n,s),o=nC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=eC(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,s),c=C().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 x8(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 eC(e,t,n,s,i){const o=T8(t,s);let a;if(i){const[h,d]=sc(e[0],e[1]);a=h*d}else{const[h,d]=jh(e[0],e[1]);a=h*d}const c=x8(n,o);return a*c}function T8(e,t){switch(e){case Sn.PACKED_2X2_FLOAT32:return j0(t);case Sn.PACKED_2X2_FLOAT16:return K0(t);case Sn.UNPACKED_FLOAT32:return Y0(t);case Sn.UNPACKED_FLOAT16:return H0(t);case Sn.PACKED_4X1_UNSIGNED_BYTE:return q0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function A8(e){return C().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Sn.PACKED_2X2_FLOAT32:Sn.UNPACKED_FLOAT32:e?Sn.PACKED_2X2_FLOAT16:Sn.UNPACKED_FLOAT16}function tC(e,t){if(e===As.UPLOAD)return Sn.PACKED_2X2_FLOAT32;if(e===As.RENDER||e==null)return A8(t);if(e===As.DOWNLOAD||e===As.PIXELS)return Sn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function nC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class v8{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=Et(this.rank),i=N8(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${i}));
}
`}}function N8(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 or="if (isnan(x)) return x;",C8="return x;",sC="return abs(x);",iC=or+`
return (x < 0.0) ? 0.0 : x;
`,rC=or+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,oC="return (x >= 0.0) ? x : (exp(x) - 1.0);",R8=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${rp};
float scale = ${op};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;function O8(e=0){return or+`
return x > 0.0 ? 1.0 : float(${e});
`}const aC="return -x;",cC="return ceil(x);",lC="return floor(x);",E8=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,D8="return float(isnan(x));",k8="return float(isinf(x));",F8="return float(!isnan(x) && !isinf(x));",_8=`
// 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;
}
}
`,hC="return exp(x);",uC="return exp(x) - 1.0;",W8=`if (x < 0.0) return NAN;
return log(x);`,$8="return log(1.0 + x);",U8="return sqrt(x);",B8="return inversesqrt(x);",M8="return 1.0 / (1.0 + exp(-1.0 * x));",P8=`
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;
`,z8=or+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,G8=or+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,V8=or+`
return atan(x);
`,Y8=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,H8=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,q8=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,j8=or+"return log(x + sqrt(x * x + 1.0));",K8=or+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,X8=or+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,J8=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${Eb};
float a1 = ${Db};
float a2 = ${kb};
float a3 = ${Fb};
float a4 = ${_b};
float a5 = ${Wb};
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));
`,Z8="return 1.0 / x;",Q8="return float(!(x >= 1.0));",e6="return float(int(x));",im="return x;";const t6="return x;",n6=`
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;
`,pC=`
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;
`,mC=`
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 Qh{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}}class s6{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;const t=e.length,n=cs("rc",t),s=Et(t),i=Pj(t,n),o=n.slice(-2),a=t<=1?"rc":`vec2(${o.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${i});
setOutput(getChannel(packedInput, ${a}));
}
`}}const{segment_util:fC}=Bb,i6=Mb,r6=Pb,o6=zb,a6=jd,c6=1e-7,l6=1e-4,rm={};function h6(e){return e in rm||(rm[e]={}),rm[e]}function om(e,t=!1){if(e==="linear")return t?t6:C8;if(e==="relu")return t?dC:iC;if(e==="elu")return t?mC:oC;if(e==="relu6")return t?pC:rC;if(e==="prelu")return t?W0:_0;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const u6=128,d6=600;function p6(){return C().global.screen==null?1024:C().global.screen.height*C().global.screen.width*window.devicePixelRatio*d6/1024/1024}const gC=1e3;class m6 extends g{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,!C().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=_i(C().getNumber("WEBGL_VERSION"));this.binaryCache=h6(C().getNumber("WEBGL_VERSION")),this.gpgpu=new z5(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 I8(this.gpgpu),this.numMBBeforeWarning=p6(),this.texData=new p(this,Fs())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((C().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||C().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:As.UPLOAD,refCount:1}),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(C().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:As.UPLOAD,refCount:1})}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,complexTensors:i,slice:o,shape:a,isPacked:c}=t;if(o!=null){let y;c?y=new Qh(a,im):y=new st(a,im);const b=this.runWebGLProgram(y,[{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=Vn());let m;if(s==="complex64"){const y=i.real.dataSync(),b=i.imag.dataSync();m=Zi(y,b)}else m=this.getValuesFromTexture(e);return h&&(this.downloadWaitMs+=Vn()-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,complexTensors:a,isPacked:c}=t;if(i!=null){let w;c?w=new Qh(s,im):w=new st(s,im);const L=this.runWebGLProgram(w,[{dataId:e,shape:s,dtype:o}],o),T=this.read(L.dataId);return this.disposeIntermediateTensorInfo(L),T}if(n!=null)return this.convertAndCacheOnCPU(e);if(!C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&C().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"&&C().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);const w=this.texData.get(d.dataId);h=this.gpgpu.createBufferFromTexture(w.texture,...Kh(s))}this.pendingRead.set(e,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let m;if(o==="complex64"){const w=await Promise.all([a.real.data(),a.imag.data()]),L=w[0],T=w[1];m=Zi(L,T)}else if(h==null)m=this.getValuesFromTexture(e);else{const w=we(s);m=this.gpgpu.downloadFloat32MatrixFromBuffer(h,w)}d!=null&&this.disposeIntermediateTensorInfo(d);const y=this.convertAndCacheOnCPU(e,m),b=this.pendingRead.get(e);return this.pendingRead.delete(e),b.forEach(w=>w(y)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),y}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const n=e[t];if(!Q4(n))throw C().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=we(t);if(C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const y=this.decode(e),b=this.texData.get(y.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...Kh(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(y),w}const o=C().getBool("WEBGL_PACK")&&s===!0,a=o?XL(t):t,c=o?new w5(a):new b5(a),h=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),d=this.texData.get(h.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(h),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=Yi(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=Yi(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=Ox(c),a.getExtraProfileInfo=()=>c.map((h,d)=>({name:o[d],ms:h})).map(h=>`${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 C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:Vn(),endMs:null}}endTimer(e){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=Vn(),e)}async getQueryTime(e){if(C().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;this.releaseGPUData(e);const{complexTensors:t}=this.texData.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),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 C().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Fs().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=u6){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&&we(s.shape)<t)}getGPGPUContext(){return this.gpgpu}complex(e,t){const n=this.makeOutput(e.shape,"complex64"),s=this.texData.get(n.dataId);return s.complexTensors={real:Fs().keep(e.clone()),imag:Fs().keep(t.clone())},n}real(e){const t=this.texData.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.texData.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,n){if(this.shouldExecuteOnCPU([e])){const o=_j(this.texData.get(e.dataId).values,t,n,e.shape,e.dtype);return this.makeOutput(n,e.dtype,o)}if(we(n)===0)return en([],n,e.dtype);const{isPacked:s}=this.texData.get(e.dataId),i=Ey(e.shape,t,n);if(s||!i){const o=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new L8(n):new b8(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=Dy(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=bd(t,n,s);if(o.some(c=>c===0))return en([],o);const a=new S8(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new f8(e.shape,t):new m8(e.shape,t);return this.compileAndRun(n,[e])}concat(e,t){if(e[0].dtype==="complex64"){const a=e.map(h=>xo(h)),c=e.map(h=>Ea(h));return xi(this.concat(a,t),this.concat(c,t))}if(e.length===1)return e[0];if(e.length>C().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const a=Math.floor(e.length/2),c=this.concat(e.slice(0,a),t),h=this.concat(e.slice(a),t);return this.concat([c,h],t)}if(C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const a=new r5(e.map(c=>c.shape),t);return this.compileAndRun(a,e)}const n=Or(e.map(a=>a.shape),t),s=e.map(a=>a.as2D(-1,we(a.shape.slice(t)))),i=new i5(s.map(a=>a.shape)),o=this.compileAndRun(i,s);return o.reshape(n)}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,aC,e.dtype);const n=new st(e.shape,aC);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,,]=e.shape;if((i===1||o===1)&&a>gC){n&&(e=Me(e,[0,2,1])),s&&(t=Me(t,[0,2,1]));const m=o===1?e:e.as3D(c,a,1),y=o===1?2:1,b=o===1?t.as3D(c,1,a):t;return this.multiply(m,b).sum(y,!0)}const h=vn(e.dtype,t.dtype),d=new tS(e.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,,]=e.shape,m=vn(e.dtype,t.dtype),y=i!=null,b=a!=null,w=o?om(o,!0):null,L=new tS(e.shape,[d,c,h],n,s,y,w,b),T=[e,t];return i&&T.push(i),a&&T.push(a),this.compileAndRun(L,T,m)}multiply(e,t){if(e.dtype==="complex64"){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),a=new D0(E0.REAL,e.shape,t.shape),c=new D0(E0.IMAG,e.shape,t.shape),h=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag),this.makeComplexComponentTensorInfo(t,o.complexTensors.real),this.makeComplexComponentTensorInfo(t,o.complexTensors.imag)],d=this.compileAndRun(a,h),m=this.compileAndRun(c,h),y=this.complex(d,m);return d.dispose(),m.dispose(),y}const n=vn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=kj(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,F0,e.dtype);const s=new hn(F0,e.shape,t.shape);return this.compileAndRun(s,[e,t],e.dtype)}localResponseNormalization4D(e,t,n,s,i){const o=C().getBool("WEBGL_PACK_NORMALIZATION")?new X5(e.shape,t,n,s,i):new j5(e.shape,t,n,s,i);return this.compileAndRun(o,[e])}LRNGrad(e,t,n,s,i,o,a){const c=new K5(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=>Dl(a)),o=Ze(e.shape,e.dtype,i);return r6(o,t)}const n=new v8(e.shape,t);return this.compileAndRun(n,[e])}pad(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new a8(e.shape,t,n):new o8(e.shape,t,n);return this.compileAndRun(s,[e])}gather(e,t,n){const s=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.gather(e,t,n));if(s)return s;const i=new x5(e.shape,t.size,n);return this.compileAndRun(i,[e,t])}batchToSpaceND(e,t,n){k(e.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((d,m)=>d*m),i=fh(e.shape,t,s),o=gh(i.length,t.length),a=yh(e.shape,t,s),c=Rb(n,t.length),h=Ob(a,n,t.length);return Me(e.reshape(i),o).reshape(a).slice(c,h)}spaceToBatchND(e,t,n){k(e.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((m,y)=>m*y),i=[[0,0]];i.push(...n);for(let m=1+t.length;m<e.shape.length;++m)i.push([0,0]);const o=e.pad(i),a=fh(o.shape,t,s,!1),c=gh(a.length,t.length,!1),h=yh(o.shape,t,s,!1),d=Me(o.reshape(a),c);return K(d,h)}reduce(e,t,n){const s=e.shape[0],i=e.shape[1],o=Jl(i),a=Math.ceil(i/o),c={windowSize:o,inSize:i,batchSize:s,outSize:a},h=new J0(c,t),d=this.compileAndRun(h,[e],n);return d.shape[1]===1?d:this.reduce(d,t,n)}argReduce(e,t,n=null){let s=e.shape[0],i=e.shape[1];n!=null&&(s=n.shape[0],i=n.shape[1]);const o=Jl(i),a={windowSize:o,inSize:i,batchSize:s,outSize:Math.ceil(i/o)},c=new Mj(a,t,n==null),h=[e];n!=null&&h.push(n);const d=this.compileAndRun(c,h,"int32");return d.shape[1]===1?d:this.argReduce(e,t,d)}argReducePacked(e,t,n=null){const s=n!=null?n.shape:e.shape,i=s[s.length-1],o=Jl(i),a=new TK(s,o,t,n==null),c=n==null?[e]:[e,n],h=this.compileAndRun(a,c,"int32");return h.rank===e.rank?this.argReducePacked(e,t,h):h}sum(e,t){es("sum",t,e.rank);const[n,s]=Cn(e.shape,t),i=we(s),o=e.as2D(-1,i),a=cd(e.dtype);return this.reduce(o,"sum",a).reshape(n)}prod(e,t){const n=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.prod(e,t));if(n)return n;const[s,i]=Cn(e.shape,t),o=we(i),a=e.as2D(-1,o),c=cd(e.dtype);return this.reduce(a,"prod",c).reshape(s)}unsortedSegmentSum(e,t,n){let s=0;const i=kn([s],e.rank);let o=e;i!=null&&(o=Me(e,i),s=ws(1,e.rank)[0]);const a=fC.computeOutShape(o.shape,s,n),c=we([o.shape[s]]),h=o.as2D(-1,c),d=cd(e.dtype);let m=this.segOpCompute(h,"unsortedSegmentSum",t,d,n).reshape(a);return i!=null&&(m=Me(m,Ml(i))),m}segOpCompute(e,t,n,s,i){const o=e.shape[0],a=e.shape[1],c=fC.segOpComputeOptimalWindowSize(a,i),h={windowSize:c,inSize:a,batchSize:o,numSegments:i},d=new g8(h,t),m=this.compileAndRun(d,[e,n],s);return m.shape[1]===i?m:(n=sh(0,i).tile([a/c]),this.segOpCompute(m,t,n,s,i))}argMinMaxReduce(e,t,n){const s=[t];if(es("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.rank),!C().getBool("WEBGL_PACK_REDUCE")||e.rank<=2){const[i,o]=Cn(e.shape,s),a=we(o),c=e.as2D(-1,a);return this.argReduce(c,n).reshape(i)}return this.argReducePacked(e,n)}argMin(e,t){return this.argMinMaxReduce(e,t,"min")}argMax(e,t){return this.argMinMaxReduce(e,t,"max")}cumsum(e,t,n,s){if(t!==e.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${e.rank-1} but got axis=${t}`);const i=e.shape[t];let o=e;for(let a=0;a<=Math.ceil(Math.log2(i))-1;a++){const c=new M0(e.shape,!1,s),h=c.getCustomSetupFunc(a),d=o;o=this.compileAndRun(c,[o],o.dtype,h),d.dispose()}if(n){const a=new M0(e.shape,n,s),c=o;o=this.compileAndRun(a,[o]),c.dispose()}return o}equal(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,VK,"bool");const n=new hn(RK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}notEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,YK,"bool");const n=new hn(OK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}less(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.less(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,HK,"bool");const s=new hn(EK,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}lessEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,qK,"bool");const n=new hn(DK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}greater(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.greater(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,jK,"bool");const s=new hn(kK,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greaterEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,KK,"bool");const n=new hn(FK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalNot(e){const t=new st(e.shape,Q8);return this.compileAndRun(t,[e])}logicalAnd(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,XK,"bool");const n=new hn(_K,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalOr(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,JK,"bool");const n=new hn(WK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}select(e,t,n){const s=new y8(e.rank,t.shape,t.rank);return this.compileAndRun(s,[e,t,n],vn(t.dtype,n.dtype))}where(e){Pa("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return a6(e.shape,t)}topk(e,t,n){const s=e.dataSync();return o6(s,e.shape,e.dtype,t,n)}min(e,t){es("min",t,e.rank);const[n,s]=Cn(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"min",o.dtype).reshape(n)}minimum(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.minimum(e,t));if(n)return n;const s=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(QK,e.shape,t.shape):new hn(UK,e.shape,t.shape);return this.compileAndRun(s,[e,t])}mod(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(e5,e.shape,t.shape):new hn(BK,e.shape,t.shape);return this.compileAndRun(n,[e,t])}maximum(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.maximum(e,t));if(n)return n;const s=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(ZK,e.shape,t.shape):new hn($K,e.shape,t.shape);return this.compileAndRun(s,[e,t])}all(e,t){es("all",t,e.rank);const[n,s]=Cn(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"all",o.dtype).reshape(n)}any(e,t){es("any",t,e.rank);const[n,s]=Cn(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"any",o.dtype).reshape(n)}floorDiv(e,t){const n=NK,s="int32";if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,PK,s);const i=new hn(n,e.shape,t.shape);return this.compileAndRun(i,[e,t],s)}add(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,QL);const n=vn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=vj(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,QL,n);const s=new hn(QL,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}packedUnaryOp(e,t,n){const s=new Qh(e.shape,t);return this.compileAndRun(s,[e],n)}packedBinaryOp(e,t,n,s,i=!1){const o=new qr(n,e.shape,t.shape,i);return this.compileAndRun(o,[e,t],s)}complexSeparableBinaryOp(e,t,n){const s=this.texData.get(e.dataId),i=this.texData.get(t.dataId),[o,a]=[[s.complexTensors.real,i.complexTensors.real],[s.complexTensors.imag,i.complexTensors.imag]].map(h=>{const[d,m]=h,y=this.makeComplexComponentTensorInfo(e,d),b=this.makeComplexComponentTensorInfo(t,m),w=new hn(n,e.shape,t.shape);return this.compileAndRun(w,[y,b],vn(d.dtype,m.dtype))}),c=this.complex(o,a);return o.dispose(),a.dispose(),c}makeComplexComponentTensorInfo(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}addN(e){if(e.length===1)return e[0];if(e.length>C().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const o=Math.floor(e.length/2),a=this.addN(e.slice(0,o)),c=this.addN(e.slice(o));return this.addN([a,c])}const t=e.map(o=>o.dtype).reduce((o,a)=>vn(o,a)),n=e.map(o=>o.shape),s=C().getBool("WEBGL_PACK"),i=s?new Bj(e[0].shape,n):new Uj(e[0].shape,n);return this.compileAndRun(i,e,t)}subtract(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,eS);const n=vn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=Wj(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,eS,e.dtype);const s=new hn(eS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}pow(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS"),s=n?new qr(zK,e.shape,t.shape):new hn(CK,e.shape,t.shape),i=vn(e.dtype,t.dtype);return this.compileAndRun(s,[e,t],i)}ceil(e){if(this.shouldExecuteOnCPU([e])){const n=Nj(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,cC,e.dtype);const t=new st(e.shape,cC);return this.compileAndRun(t,[e])}floor(e){if(this.shouldExecuteOnCPU([e])){const n=Oj(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,lC,e.dtype);const t=new st(e.shape,lC);return this.compileAndRun(t,[e])}sign(e){const t=new st(e.shape,E8);return this.compileAndRun(t,[e])}isNaN(e){const t=new st(e.shape,D8);return this.compileAndRun(t,[e],"bool")}isInf(e){const t=new st(e.shape,k8);return this.compileAndRun(t,[e],"bool")}isFinite(e){const t=new st(e.shape,F8);return this.compileAndRun(t,[e],"bool")}round(e){const t=new st(e.shape,_8);return this.compileAndRun(t,[e])}exp(e){if(this.shouldExecuteOnCPU([e])){const n=Cj(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,hC,e.dtype);const t=new st(e.shape,hC);return this.compileAndRun(t,[e])}expm1(e){if(this.shouldExecuteOnCPU([e])){const n=Rj(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,uC,e.dtype);const t=new st(e.shape,uC);return this.compileAndRun(t,[e])}softmax(e,t){const n=ft([t],e.shape),s=qn(e,n),i=Rn(s.shape,n),o=this.subtract(e,s.reshape(i)),a=this.exp(o),c=this.sum(a,n).reshape(i);return _e(a,c)}log(e){if(this.shouldExecuteOnCPU([e])){const n=Ej(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,n6,e.dtype);const t=new st(e.shape,W8);return this.compileAndRun(t,[e])}log1p(e){const t=new st(e.shape,$8);return this.compileAndRun(t,[e])}sqrt(e){const t=new st(e.shape,U8);return this.compileAndRun(t,[e])}rsqrt(e){if(this.shouldExecuteOnCPU([e])){const n=Fj(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}const t=new st(e.shape,B8);return this.compileAndRun(t,[e])}reciprocal(e){const t=new st(e.shape,Z8);return this.compileAndRun(t,[e])}relu(e){let t;return C().getBool("WEBGL_PACK")?t=new Qh(e.shape,dC):t=new st(e.shape,iC),this.compileAndRun(t,[e])}relu6(e){let t;return C().getBool("WEBGL_PACK")?t=new Qh(e.shape,pC):t=new st(e.shape,rC),this.compileAndRun(t,[e])}prelu(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(W0,e.shape,t.shape):new hn(_0,e.shape,t.shape);return this.compileAndRun(n,[e,t])}elu(e){if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,mC,e.dtype);const t=new st(e.shape,oC);return this.compileAndRun(t,[e])}eluDer(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(GK,e.shape,t.shape):new hn(MK,e.shape,t.shape);return this.compileAndRun(n,[e,t])}selu(e){const t=new st(e.shape,R8);return this.compileAndRun(t,[e])}int(e){const t=new st(e.shape,e6);return this.compileAndRun(t,[e],"int32")}clip(e,t,n){let s;C().getBool("WEBGL_PACK_CLIP")?s=new n5(e.shape):s=new t5(e.shape);const i=s.getCustomSetupFunc(t,n);return this.compileAndRun(s,[e],null,i)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){const n=Aj(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,sC,e.dtype);const t=new st(e.shape,sC);return this.compileAndRun(t,[e])}complexAbs(e){const t=this.texData.get(e.dataId),n=new s5(e.shape),s=[this.makeComplexComponentTensorInfo(e,t.complexTensors.real),this.makeComplexComponentTensorInfo(e,t.complexTensors.imag)];return this.compileAndRun(n,s)}sigmoid(e){const t=new st(e.shape,M8);return this.compileAndRun(t,[e])}softplus(e){const t=new st(e.shape,P8);return this.compileAndRun(t,[e])}asin(e){const t=new st(e.shape,z8);return this.compileAndRun(t,[e])}acos(e){const t=new st(e.shape,G8);return this.compileAndRun(t,[e])}atan(e){const t=new st(e.shape,V8);return this.compileAndRun(t,[e])}sinh(e){const t=new st(e.shape,Y8);return this.compileAndRun(t,[e])}cosh(e){const t=new st(e.shape,H8);return this.compileAndRun(t,[e])}tanh(e){const t=new st(e.shape,q8);return this.compileAndRun(t,[e])}asinh(e){const t=new st(e.shape,j8);return this.compileAndRun(t,[e])}acosh(e){const t=new st(e.shape,K8);return this.compileAndRun(t,[e])}atanh(e){const t=new st(e.shape,X8);return this.compileAndRun(t,[e])}erf(e){const t=new st(e.shape,J8);return this.compileAndRun(t,[e])}step(e,t){const n=new st(e.shape,O8(t));return this.compileAndRun(n,[e])}conv2dByMatMul(e,t,n,s,i,o){const a=e.shape,c=this.texData.get(e.dataId),h=n.inChannels,d=a[0]*a[1]*a[2],m=n.outChannels,y=n.dataFormat==="channelsLast",b=!1,w=!1,L=(d===1||m===1)&&h>gC,T=a[2]%2!==0&&!!c.isPacked;if(L||!C().getBool("WEBGL_LAZILY_UNPACK")||!C().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!T){const B=y?a[0]*a[1]*a[2]:a[0]*a[2]*a[3],$=K(e,[1,B,n.inChannels]),H=K(t,[1,n.inChannels,n.outChannels]),q=this.fusedBatchMatMul({a:$,b:H,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o});return K(q,n.outShape)}const A=y?a[0]*a[1]*(a[2]+1):a[0]*a[2]*(a[3]+1),N={dataId:e.dataId,shape:[1,A,n.inChannels],dtype:e.dtype},E=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k(Zp(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);const D=K(t,[1,n.inChannels,n.outChannels]),F=this.fusedBatchMatMul({a:N,b:D,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o}),_=this.texData.get(F.dataId);return k(_.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=E,_.shape=n.outShape,Fs().makeTensorFromDataId(F.dataId,n.outShape,F.dtype)}conv2dWithIm2Row(e,t,n,s,i,o){const{filterWidth:a,filterHeight:c,inChannels:h,outWidth:d,outHeight:m,dataFormat:y}=n,b=y==="channelsLast",w=a*c*h,L=m*d,T=[w,L],A=!0,N=!1,E=e.squeeze([0]),D=t.reshape([1,w,-1]),F=new q5(T,E.shape,n),_=this.compileAndRun(F,[E]).reshape([1,T[0],T[1]]),B=s!=null,$=o!=null,H=i?om(i,!0):null,q=new tS(_.shape,[1,L,n.outChannels],A,N,B,H,$),J=[_,D];s&&J.push(s),$&&J.push(o);const re=this.compileAndRun(q,J);return b?re.reshape([1,m,d,n.outChannels]):re.reshape([1,n.outChannels,m,d])}fusedConv2d({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n,s,i,o);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n,s,i,o);const a=s!=null,c=o!=null,h=i?om(i,!1):null,d=new $0(n,a,h,c),m=[e,t];return s&&m.push(s),o&&m.push(o),this.compileAndRun(d,m)}conv2d(e,t,n){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n);const s=new $0(n);return this.compileAndRun(s,[e,t])}conv2dDerInput(e,t,n){const s=new a5(n);return this.compileAndRun(s,[e,t])}conv2dDerFilter(e,t,n){const s=new o5(n);return this.compileAndRun(s,[e,t])}fusedDepthwiseConv2D({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){const a=C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1,c=i?om(i,a):null,h=[e,t],d=s!=null,m=o!=null;d&&h.push(s),m&&h.push(o);let y;return a?(y=new B0(n,d,c,m),this.compileAndRun(y,h)):(y=new U0(n,d,c,m),this.compileAndRun(y,h))}depthwiseConv2D(e,t,n){let s;return C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1?(s=new B0(n),this.compileAndRun(s,[e,t])):(s=new U0(n),this.compileAndRun(s,[e,t]))}depthwiseConv2DDerInput(e,t,n){const s=new u5(n);return this.compileAndRun(s,[e,t])}depthwiseConv2DDerFilter(e,t,n){const s=new h5(n);return this.compileAndRun(s,[e,t])}conv3d(e,t,n){const s=new d5(n);return this.compileAndRun(s,[e,t])}conv3dDerInput(e,t,n){const s=new l5(n);return this.compileAndRun(s,[e,t])}conv3dDerFilter(e,t,n){const s=new c5(n);return this.compileAndRun(s,[e,t])}cast(e,t){return DA(e,t,this)}unstack(e,t){const n=e.shape[t],s=new Array(e.rank-1);let i=0;for(let h=0;h<e.rank;h++)h!==t&&(s[i++]=e.shape[h]);const o=new Array(e.rank).fill(0),a=e.shape.slice();a[t]=1;const c=new Array(n);for(let h=0;h<c.length;h++)o[t]=h,c[h]=this.slice(e,o,a).reshape(s);return c}avgPool3d(e,t){const n=new nS(t,"avg",!1);return this.compileAndRun(n,[e],"float32")}avgPool3dBackprop(e,t,n){const s=new vK(n);return this.compileAndRun(s,[e],t.dtype)}maxPool3d(e,t){const n=new nS(t,"max",!1);return this.compileAndRun(n,[e],"float32")}maxPool3dBackprop(e,t,n,s){const i=!0,o=new nS(s,"max",i),a=this.compileAndRun(o,[t]),c=new Z5(s),h=this.compileAndRun(c,[e,a],t.dtype);return a.dispose(),h}resizeBilinear(e,t,n,s){const i=C().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new u8(e.shape,t,n,s):new h8(e.shape,t,n,s);return this.compileAndRun(i,[e],"float32")}resizeBilinearBackprop(e,t,n){const s=new l8(e,t,n);return this.compileAndRun(s,[e])}resizeNearestNeighbor(e,t,n,s){const i=new p8(e.shape,t,n,s);return this.compileAndRun(i,[e])}resizeNearestNeighborBackprop(e,t,n){const s=new d8(e,t,n);return this.compileAndRun(s,[e])}multinomial(e,t,n,s){const i=t?e:No(e),o=i.shape[0],a=i.shape[1],c=new Q5(o,a,n),h=c.getCustomSetupFunc(s);return this.compileAndRun(c,[i],"int32",h)}oneHot(e,t,n,s){const i=new e8(e.size,t,n,s);return this.compileAndRun(i,[e])}diag(e){const t=new y5(e.size);return this.compileAndRun(t,[e])}cropAndResize(e,t,n,s,i,o){const a=new p5(e.shape,t.shape,s,i,o);return this.compileAndRun(a,[e,t,n],"float32")}depthToSpace(e,t,n){k(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],y=new g5(m,t,n);return this.compileAndRun(y,[e])}split(e,t,n){return i6(e,t,n)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=va(t,e,n),h=[c/o,o],d=e.reshape([i,s]),m=t.reshape([i,o]);if(c===0)return kA(en([]),n);const y=Ne(0),b=new Q0(i,s,d.rank,m.rank,a,h),w=this.compileAndRun(b,[m,d,y]);return w.reshape(n)}sparseToDense(e,t,n,s){const{sliceRank:i,numUpdates:o,strides:a,outputSize:c}=va(t,e,n),h=!1,d=new Q0(o,i,e.rank,t.rank,a,[c,1],h),m=this.compileAndRun(d,[t,e,s]);return m.reshape(n)}fft(e){const t=!1;return this.fftImpl(e,t)}ifft(e){const t=!0;return this.fftImpl(e,t)}fftImpl(e,t){const n=this.texData.get(e.dataId),s=new V0(G0.REAL,e.shape,t),i=new V0(G0.IMAG,e.shape,t),o=[this.makeComplexComponentTensorInfo(e,n.complexTensors.real),this.makeComplexComponentTensorInfo(e,n.complexTensors.imag)],a=this.compileAndRun(s,o),c=this.compileAndRun(i,o),h=this.complex(a,c).as2D(e.shape[0],e.shape[1]);return a.dispose(),c.dispose(),h}gatherND(e,t){const n=t.shape,s=n[n.length-1],[i,o,a,c]=gd(e,t),h=t.reshape([o,s]),d=e.reshape([e.size/a,a]),m=new A5(s,c,[o,a]),y=this.compileAndRun(m,[d,h]);return y.reshape(i)}fill(e,t,n){if(n=n||ba(t),n==="string"){const s=lo(n,we(e));return s.fill(t),Fs().makeTensor(s,e,n,this)}else{const s=new I5(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 Ub(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 Fs().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new s6(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new t8(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){const n=[ic(e.shape),...rc(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},i=[ic(t),...rc(t)],o=new Z0(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=XL(s);let a;n?a=new f5(o):a=new m5(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===qh.DENSE){const L=Kh(e.outputShape);a.texShape=L.map(T=>T*2)}if(e.outTexUsage!=null&&(a.usage=e.outTexUsage),we(o.shape)===0)return a.values=bn(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 T=this.texData.get(L.dataId);if(T.texture==null){if(!e.packedInputs&&we(L.shape)<=C().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:L.shape,texData:null,isUniform:!0,uniformValues:T.values};e.packedInputs&&(T.isPacked=!0,T.shape=L.shape)}else if(!!T.isPacked!==!!e.packedInputs)L=T.isPacked?this.unpackTensor(L):this.packTensor(L),c.push(L),T=this.texData.get(L.dataId);else if(T.isPacked&&!Zp(T.shape,L.shape)){const A=L,N=L.shape;L.shape=T.shape,L=this.packedReshape(L,N),c.push(L),T=this.texData.get(L.dataId),A.shape=N}return this.uploadToGPU(L.dataId),{shape:L.shape,texData:T,isUniform:!1}});this.uploadToGPU(o.dataId);const d={shape:o.shape,texData:a,isUniform:!1},m=H5(e,h,d),y=this.getAndSaveBinary(m,()=>V5(this.gpgpu,e,h,d)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),Y5(this.gpgpu,y,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)})),!C().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 Fs().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(!C().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=ee(()=>{if(!C().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=C().getBool("DEBUG");C().set("DEBUG",!1);const t=this.abs(Ne(1e-8)).dataSync()[0];if(C().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?c6:l6}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=Vn());let m=t.texShape;if(m==null&&(m=yj(n,c),t.texShape=m),i!=null){const y=XL(n);let b,w=m[1],L=m[0];const T=i instanceof Uint8Array;c?([w,L]=sc(m[0],m[1]),b=new S5(y,[L,w],T)):b=new L5(y,[L,w],T);const A=this.makeTensorInfo([L,w],s);T?this.texData.get(A.dataId).usage=As.PIXELS:this.texData.get(A.dataId).usage=As.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),w,L,i);const N=!0,E=this.runWebGLProgram(b,[A],s,null,N),D=this.texData.get(E.dataId);t.texture=D.texture,t.texShape=D.texShape,t.isPacked=D.isPacked,t.usage=D.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(E.dataId),t.values=null,h&&(this.uploadWaitMs+=Vn()-d)}else{const y=this.acquireTexture(m,a,s,c);t.texture=y}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=f6(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]*ry(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(n){if(C().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function f6(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 g6="2.6.0";function y6(){C().set("WEBGL_FORCE_F16_TEXTURES",!0)}fy()&&Wy("webgl",()=>new m6,2);const bee={forceHalfFloat:y6},yC="if (isnan(x)) return x;",b6=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,w6=`
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 am(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 iS(e,t,n,s){return({inputs:i,backend:o})=>{const{a,b:c}=i,h=o,d=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(t,a.shape,c.shape,!!n):new hn(e,a.shape,c.shape),m=s||a.dtype,y=h.runWebGLProgram(d,[a,c],m);return y}}const L6=b6+`
return atan(a, b);
`,S6=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+w6+`
return result;
`,I6=iS(L6,S6),x6={kernelName:Li,backendName:"webgl",kernelFunc:I6};function rS(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 T6={kernelName:al,backendName:"webgl",kernelFunc:rS};function A6(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;Xh(i,"avgPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;k(rn(a,d),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Fn(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return rS({inputs:{x:i},backend:n});const y=new Zh(m,"avg",!1);return n.runWebGLProgram(y,[i],"float32")}const v6={kernelName:Xs,backendName:"webgl",kernelFunc:A6};function N6(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;Xh([i,o],"avgPoolBackprop");const{filterSize:c,strides:h,pad:d}=s,m=Fn(a.shape,c,h,1,d),y=new AK(m);return n.runWebGLProgram(y,[i],a.dtype)}const C6={kernelName:ua,backendName:"webgl",kernelFunc:N6};class R6{constructor(e,t,n,s,i,o){this.outputShape=[],this.variableNames=["x","mean","variance"],tt(e,t),tt(e,n);let a="0.0";s!=null&&(tt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="1.0";i!=null&&(tt(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 O6{constructor(e,t,n,s,i,o){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],tt(e,t),tt(e,n);let a="vec4(0.0)";s!=null&&(tt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="vec4(1.0)";i!=null&&(tt(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 E6=({inputs:e,backend:t,attrs:n})=>{const{x:s,mean:i,variance:o,offset:a,scale:c}=e;k(i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k(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 y=null;c!=null&&(y=c.shape,d.push(c));const b=C().getBool("WEBGL_PACK_NORMALIZATION")?new O6(s.shape,i.shape,o.shape,m,y,h):new R6(s.shape,i.shape,o.shape,m,y,h),w=t.runWebGLProgram(b,d,d[0].dtype);return w},D6={kernelName:ol,backendName:"webgl",kernelFunc:E6};const k6=yC+`
return cos(x);
`,F6=am(k6),_6={kernelName:da,backendName:"webgl",kernelFunc:F6};const W6=`
if (a == b) {
return 1.0;
};
return a / b;`,$6=`
// 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;
`,U6=iS(W6,$6,!0),B6={kernelName:pa,backendName:"webgl",kernelFunc:U6};class M6{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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}}const P6={kernelName:Pu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,s=t,i=new M6(n.shape),o=s.runWebGLProgram(i,[n],n.dtype);return o}};class z6{constructor(e){this.variableNames=["A"];const t=Wn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}}class G6{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=Wn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${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);
}
}
${t.output} = result;
}
`}}const V6={kernelName:Qu,backendName:"webgl",kernelFunc:Y6};let hc;function Y6(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:i}=t;const{numChannels:o}=s,a=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,c=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[h,d]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[d,h],y=[d,h,o];(c||a)&&(hc==null&&(hc=document.createElement("canvas").getContext("2d")),hc.canvas.width=h,hc.canvas.height=d,hc.drawImage(i,0,0,h,d),i=hc.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=As.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=C().getBool("WEBGL_PACK")?new G6(y):new z6(y),L=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),L}function H6(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const n=t.length?t[t.length-1].outSize:e[1],s=Jl(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function q6(e,t,n,s){const i=H6(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:h,outSize:d}=i[a],m=new J0({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d},n),y=o;o=s.runWebGLProgram(m,[o],t),y.dataId!==e.dataId&&s.disposeData(y.dataId)}return o}function j6(e,t,n){const s=[ic(e.shape),...rc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[ic(t),...rc(t)],a=new Z0(o,s),c=!0,h=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:h.dataId,shape:t,dtype:h.dtype}}function oS(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=we(i.shape),h=id(o,c),d=we(h);k(c===d,()=>`The new shape (${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&&!Zp(i.shape,h)&&!(m.texture!==null&&Zp(m.shape,h))?j6(i,h,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:h,dtype:i.dtype})}const K6={kernelName:yl,backendName:"webgl",kernelFunc:oS};function X6(e,t,n,s){const i=we(t),o=we(e.shape),a=o/i,c=oS({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),h=q6(c,e.dtype,"max",s),d=oS({inputs:{x:h},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(h),d}class J6{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=Et(this.rank),i=Z6(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${i}));
}
`}}function Z6(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 Q6{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 ${this.rank} is not yet supported.`);const s=Et(this.rank),i=v0("rc",this.rank),o=new Array(this.rank);for(let d=0;d<t.length;d++)o[t[d]]=i[d];const a=`vec2(${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);
}
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float coordYFloat = (float(x) - ${m}) * ${a} + (float(y) - ${y}) * ${c};
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fc(r){return ui(r)&&0<=r&&r<=1}class Je{constructor(r,l){this._x=r,this._y=l}get x(){return this._x}get y(){return this._y}add(r){return new Je(this.x+r.x,this.y+r.y)}sub(r){return new Je(this.x-r.x,this.y-r.y)}mul(r){return new Je(this.x*r.x,this.y*r.y)}div(r){return new Je(this.x/r.x,this.y/r.y)}abs(){return new Je(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(Math.pow(this.x,2)+Math.pow(this.y,2))}floor(){return new Je(Math.floor(this.x),Math.floor(this.y))}}class Ct{static isRect(r){return!!r&&[r.x,r.y,r.width,r.height].every(ui)}static assertIsValidBox(r,l,u=!1){if(!Ct.isRect(r))throw new Error(`${l} - invalid box: ${JSON.stringify(r)}, expected object with properties x, y, width, height`);if(!u&&(r.width<0||r.height<0))throw new Error(`${l} - width (${r.width}) and height (${r.height}) must be positive numbers`)}constructor(r,l=!0){const u=r||{},p=[u.left,u.top,u.right,u.bottom].every(ui),g=[u.x,u.y,u.width,u.height].every(ui);if(!g&&!p)throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(u)}`);const[f,I,S,x]=g?[u.x,u.y,u.width,u.height]:[u.left,u.top,u.right-u.left,u.bottom-u.top];Ct.assertIsValidBox({x:f,y:I,width:S,height:x},"Box.constructor",l),this._x=f,this._y=I,this._width=S,this._height=x}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 Je(this.left,this.top)}get topRight(){return new Je(this.right,this.top)}get bottomLeft(){return new Je(this.left,this.bottom)}get bottomRight(){return new Je(this.right,this.bottom)}round(){const[r,l,u,p]=[this.x,this.y,this.width,this.height].map(g=>Math.round(g));return new Ct({x:r,y:l,width:u,height:p})}floor(){const[r,l,u,p]=[this.x,this.y,this.width,this.height].map(g=>Math.floor(g));return new Ct({x:r,y:l,width:u,height:p})}toSquare(){let{x:r,y:l,width:u,height:p}=this;const g=Math.abs(u-p);return u<p&&(r-=g/2,u+=g),p<u&&(l-=g/2,p+=g),new Ct({x:r,y:l,width:u,height:p})}rescale(r){const l=pm(r)?r.width:r,u=pm(r)?r.height:r;return new Ct({x:this.x*l,y:this.y*u,width:this.width*l,height:this.height*u})}pad(r,l){let[u,p,g,f]=[this.x-r/2,this.y-l/2,this.width+r,this.height+l];return new Ct({x:u,y:p,width:g,height:f})}clipAtImageBorders(r,l){const{x:u,y:p,right:g,bottom:f}=this,I=Math.max(u,0),S=Math.max(p,0),x=g-I,v=f-S,O=Math.min(x,r-I),C=Math.min(v,l-S);return new Ct({x:I,y:S,width:O,height:C}).floor()}shift(r,l){const{width:u,height:p}=this,g=this.x+r,f=this.y+l;return new Ct({x:g,y:f,width:u,height:p})}padAtBorders(r,l){const u=this.width+1,p=this.height+1;let g=1,f=1,I=u,S=p,x=this.left,v=this.top,O=this.right,C=this.bottom;return O>l&&(I=-O+l+u,O=l),C>r&&(S=-C+r+p,C=r),x<1&&(S=2-x,x=1),v<1&&(S=2-v,v=1),{dy:f,edy:S,dx:g,edx:I,y:v,ey:C,x,ex:O,w:u,h:p}}calibrate(r){return new Ct({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 tu extends Ct{constructor(r,l,u,p,g=!1){super({left:r,top:l,right:u,bottom:p},g)}}class gc{constructor(r,l,u,p,g){this._imageDims=new us(g.width,g.height),this._score=r,this._classScore=l,this._className=u,this._box=new Ct(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 Ct(this._box).rescale(this.imageDims.reverse())}forSize(r,l){return new gc(this.score,this.classScore,this.className,this.relativeBox,{width:r,height:l})}}class Ht extends gc{constructor(r,l,u){super(r,r,"",l,u)}forSize(r,l){const{score:u,relativeBox:p,imageDims:g}=super.forSize(r,l);return new Ht(u,p,g)}}function mS(r,l,u=!0){const p=Math.max(0,Math.min(r.right,l.right)-Math.max(r.left,l.left)),g=Math.max(0,Math.min(r.bottom,l.bottom)-Math.max(r.top,l.top)),f=p*g;return u?f/(r.area+l.area-f):f/Math.min(r.area,l.area)}function fS(r){const l=r.map(S=>S.x),u=r.map(S=>S.y),p=l.reduce((S,x)=>x<S?x:S,Infinity),g=u.reduce((S,x)=>x<S?x:S,Infinity),f=l.reduce((S,x)=>S<x?x:S,0),I=u.reduce((S,x)=>S<x?x:S,0);return new tu(p,g,f,I)}function gS(r,l,u,p=!0){let g=l.map((I,S)=>({score:I,boxIndex:S})).sort((I,S)=>I.score-S.score).map(I=>I.boxIndex);const f=[];for(;g.length>0;){const I=g.pop();f.push(I);const S=g,x=[];for(let v=0;v<S.length;v++){const O=S[v],C=r[I],U=r[O];x.push(mS(C,U,p))}g=g.filter((v,O)=>x[O]<=u)}return f}const $i=Ke(Xe());function di(r,l){return $i.tidy(()=>{const[u,p,g]=l,f=$i.fill([...r.shape.slice(0,3),1],u,"float32"),I=$i.fill([...r.shape.slice(0,3),1],p,"float32"),S=$i.fill([...r.shape.slice(0,3),1],g,"float32"),x=$i.concat([f,I,S],3);return $i.sub(r,x)})}const jr=Ke(Xe());function yS(r,l=!1){return jr.tidy(()=>{const[u,p]=r.shape.slice(1);if(u===p)return r;const g=Math.abs(u-p),f=Math.round(g*(l?.5:1)),I=u>p?2:1,S=U=>{const G=r.shape.slice();return G[I]=U,jr.fill(G,0,"float32")},x=S(f),v=g-x.shape[I],O=l&&v?S(v):null,C=[O,r,x].filter(U=>!!U).map(U=>jr.cast(U,"float32"));return jr.concat(C,I)})}function MX(r){const l=r.slice();for(let u=l.length-1;u>0;u--){const p=Math.floor(Math.random()*(u+1)),g=l[u];l[u]=l[p],l[p]=g}return l}function nu(r){return 1/(1+Math.exp(-r))}function PX(r){return Math.log(r/(1-r))}class su extends Ct{constructor(r,l,u,p,g=!1){super({x:r,y:l,width:u,height:p},g)}}const zX=.5,GX=.43,VX=.45;class Gs{constructor(r,l,u=new Je(0,0)){const{width:p,height:g}=l;this._imgDims=new us(p,g),this._shift=u,this._positions=r.map(f=>f.mul(new Je(p,g)).add(u))}get shift(){return new Je(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 Je(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 Je(r,l))}shiftByPoint(r){return this.shiftBy(r.x,r.y)}align(r,l={}){if(r){const g=r instanceof Ht?r.box.floor():new Ct(r);return this.shiftBy(g.x,g.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,g=O=>p.sub(O).magnitude(),f=(g(l)+g(u))/2,I=Math.floor(f/VX),S=Go(r),x=Math.floor(Math.max(0,S.x-zX*I)),v=Math.floor(Math.max(0,S.y-GX*I));return new su(x,v,Math.min(I,this.imageWidth+x),Math.min(I,this.imageHeight+v))}alignMinBbox(r){const l=fS(this.positions);return l.pad(l.width*r,l.height*r)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class YX extends Gs{getRefPointsForAlignment(){const r=this.positions;return[r[0],r[1],Go([r[3],r[4]])]}}class iu 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(Go)}}class mm{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}${r?` (${zo(this.distance)})`:""}`}}class fm extends Ct{static assertIsValidLabeledBox(r,l){if(Ct.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 Vo{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 Vo(r.label,l)}}class HX extends fm{static assertIsValidPredictedBox(r,l){if(fm.assertIsValidLabeledBox(r,l),!fc(r.score)||!fc(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 Ui(r){return r.detection instanceof Ht}function Yo(r,l){const u={detection:l};return Object.assign({},r,u)}function bS(){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 gm(r){let l="";if(!r)try{r=require("fs")}catch(p){l=p.toString()}const u=r?function(p){return new Promise((g,f)=>{r.readFile(p,function(I,S){return I?f(I):g(S)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${l}`)};return{readFile:u}}function wS(){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")},g=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},f=gm();return{Canvas:r||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:l||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:u,createImageElement:p,fetch:g,...f}}function LS(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}const SS=Ke(AC());let gn;function qX(){if(!gn)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return gn}function IS(r){gn=r}function xS(){if(LS())return IS(bS());if(SS.isNodejs())return IS(wS())}function jX(r){if(gn||xS(),!gn)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");const{Canvas:l=gn.Canvas,Image:u=gn.Image}=r;gn.Canvas=l,gn.Image=u,gn.createCanvasElement=r.createCanvasElement||(()=>new l),gn.createImageElement=r.createImageElement||(()=>new u),gn.ImageData=r.ImageData||gn.ImageData,gn.Video=r.Video||gn.Video,gn.fetch=r.fetch||gn.fetch,gn.readFile=r.readFile||gn.readFile}const gt={getEnv:qX,setEnv:IS,initialize:xS,createBrowserEnv:bS,createFileSystem:gm,createNodejsEnv:wS,monkeyPatch:jX,isBrowser:LS,isNodejs:SS.isNodejs};xS();function Ho(r){return!gt.isNodejs()&&typeof r=="string"?document.getElementById(r):r}function Jn(r){const{Canvas:l,CanvasRenderingContext2D:u}=gt.getEnv();if(r instanceof u)return r;const p=Ho(r);if(!(p instanceof l))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const g=p.getContext("2d");if(!g)throw new Error("resolveContext2d - canvas 2d context is null");return g}var Bi;(function(r){r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Bi||(Bi={}));class ym{constructor(r={}){const{anchorPosition:l,backgroundColor:u,fontColor:p,fontSize:g,fontStyle:f,padding:I}=r;this.anchorPosition=l||Bi.TOP_LEFT,this.backgroundColor=u||"rgba(0, 0, 0, 0.5)",this.fontColor=p||"rgba(255, 255, 255, 1)",this.fontSize=g||14,this.fontStyle=f||"Georgia",this.padding=I||4}}class yc{constructor(r,l,u={}){this.text=typeof r=="string"?[r]:r instanceof yc?r.text:r,this.anchor=l,this.options=new ym(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===Bi.BOTTOM_RIGHT||u===Bi.TOP_RIGHT,g=u===Bi.BOTTOM_LEFT||u===Bi.BOTTOM_RIGHT,f=this.measureWidth(r),I=this.measureHeight(),S=p?this.anchor.x-f:this.anchor.x,x=g?this.anchor.y-I:this.anchor.y;if(l){const{width:v,height:O}=l,C=Math.max(Math.min(S,v-f),0),U=Math.max(Math.min(x,O-I),0);return{x:C,y:U}}return{x:S,y:x}}draw(r){const l=Ho(r),u=Jn(l),{backgroundColor:p,fontColor:g,fontSize:f,fontStyle:I,padding:S}=this.options;u.font=`${f}px ${I}`;const x=this.measureWidth(u),v=this.measureHeight();u.fillStyle=p;const O=this.getUpperLeft(u,l);u.fillRect(O.x,O.y,x,v),u.fillStyle=g,this.text.forEach((C,U)=>{const G=S+O.x,ne=S+O.y+(U+1)*f;u.fillText(C,G,ne)})}}class NC{constructor(r={}){const{boxColor:l,lineWidth:u,label:p,drawLabelOptions:g}=r;this.boxColor=l||"rgba(0, 0, 255, 1)",this.lineWidth=u||2,this.label=p;const f={anchorPosition:Bi.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new ym(Object.assign({},f,g))}}class TS{constructor(r,l={}){this.box=new Ct(r),this.options=new NC(l)}draw(r){const l=Jn(r),{boxColor:u,lineWidth:p}=this.options,{x:g,y:f,width:I,height:S}=this.box;l.strokeStyle=u,l.lineWidth=p,l.strokeRect(g,f,I,S);const{label:x}=this.options;x&&new yc([x],{x:g-p/2,y:f},this.options.drawLabelOptions).draw(r)}}function KX(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const g=p instanceof Ht?p.score:Ui(p)?p.detection.score:void 0,f=p instanceof Ht?p.box:Ui(p)?p.detection.box:new Ct(p),I=g?`${zo(g)}`:void 0;new TS(f,{label:I}).draw(r)})}function ru(r){const{Image:l,Video:u}=gt.getEnv();return r instanceof l&&r.complete||r instanceof u&&r.readyState>=3}function AS(r){return new Promise((l,u)=>{if(r instanceof gt.getEnv().Canvas||ru(r))return l(null);function p(f){if(!f.currentTarget)return;f.currentTarget.removeEventListener("load",p),f.currentTarget.removeEventListener("error",g),l(f)}function g(f){if(!f.currentTarget)return;f.currentTarget.removeEventListener("load",p),f.currentTarget.removeEventListener("error",g),u(f)}r.addEventListener("load",p),r.addEventListener("error",g)})}function vS(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 g=gt.getEnv().createImageElement();g.onload=()=>l(g),g.onerror=u,g.src=p.result},p.onerror=u,p.readAsDataURL(r)})}function qo(r){const{Image:l,Video:u}=gt.getEnv();return r instanceof l?new us(r.naturalWidth,r.naturalHeight):r instanceof u?new us(r.videoWidth,r.videoHeight):new us(r.width,r.height)}function bc({width:r,height:l}){const{createCanvasElement:u}=gt.getEnv(),p=u();return p.width=r,p.height=l,p}function ou(r,l){const{ImageData:u}=gt.getEnv();if(!(r instanceof u)&&!ru(r))throw new Error("createCanvasFromMedia - media has not finished loading yet");const{width:p,height:g}=l||qo(r),f=bc({width:p,height:g});return r instanceof u?Jn(f).putImageData(r,0,0):Jn(f).drawImage(r,0,0,p,g),f}const bm=Ke(Xe());async function NS(r,l){const u=l||gt.getEnv().createCanvasElement(),[p,g,f]=r.shape.slice(Ns(r)?1:0),I=bm.tidy(()=>r.as3D(p,g,f).toInt());return await bm.browser.toPixels(I,u),I.dispose(),u}function wm(r){const{Image:l,Canvas:u,Video:p}=gt.getEnv();return r instanceof l||r instanceof u||r instanceof p}const XX=1e-7,JX=1e-4;class CC{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?XX:JX}batchMatMul(r,l,u,p){return se("batchMatMul")}fusedBatchMatMul({a:r,b:l,transposeA:u,transposeB:p,bias:g,activation:f,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 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`;for(let te=2;te<x;te++)ne+=`
`;return U[U.length-1]=" "+U[U.length-1]+"]"+(f?"":ne),U}function du(r){const l=[];for(let u=0;u<r.length;u+=2)l.push([r[u],r[u+1]]);return l}class bR{constructor(r,l,u){if(this.dtype=l,this.shape=r.slice(),this.size=qt(r),u!=null){const p=u.length;Z(p===this.size,()=>`Length of values '${p}' does not match the size inferred by the shape '${this.size}'.`)}if(l==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=u||oR(l,this.size),this.strides=lu(r)}set(r,...l){l.length===0&&(l=[0]),Z(l.length===this.rank,()=>`The number of provided coordinates (${l.length}) must match the rank (${this.rank})`);const u=this.locToIndex(l);this.values[u]=r}get(...r){r.length===0&&(r=[0]);let l=0;for(const p of r){if(p<0||p>=this.shape[l]){const g=`Requested out of range element at ${r}. Buffer shape=${this.shape}`;throw new Error(g)}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 Mi().makeTensor(this.values,this.shape,this.dtype)}}let Mi=null,Tc=null,h7=null;function wR(r){Mi=r}function LR(r){Tc=r}function SR(r){h7=r}class In{constructor(r,l,u,p){this.kept=!1,this.isDisposedInternal=!1,this.shape=r.slice(),this.dtype=l||"float32",this.size=qt(r),this.strides=lu(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 Tc.buffer(this.shape,this.dtype,r)}bufferSync(){return Tc.buffer(this.shape,this.dtype,this.dataSync())}async array(){const r=await this.data();return WS(this.shape,r)}arraySync(){return WS(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const r=Mi().read(this.dataId);if(this.dtype==="string"){const l=await r;try{return l.map(u=>US(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=Mi().readSync(this.dataId);if(this.dtype==="string")try{return r.map(l=>US(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 Mi().read(this.dataId);return this.dtype==="string"?r:new Uint8Array(r.buffer)}dispose(){if(this.isDisposed)return;Mi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(r=!1){return Tc.print(this,r)}clone(){return this.throwIfDisposed(),Tc.clone(this)}toString(r=!1){const l=this.dataSync();return gR(l,this.shape,this.dtype,r)}cast(r){return this.throwIfDisposed(),Tc.cast(this,r)}variable(r=!0,l,u){return this.throwIfDisposed(),Mi().makeVariable(this,r,l,u)}}Object.defineProperty(In,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});class If extends In{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(!jo(r.shape,this.shape))throw new Error(`shape of the new value (${r.shape}) and previous value (${this.shape}) must match`);Mi().disposeTensor(this),this.dataId=r.dataId,Mi().incRef(this,null)}dispose(){Mi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(If,Symbol.hasInstance,{value:r=>r instanceof In&&r.assign!=null&&r.assign instanceof Function});var IR;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(IR||(IR={}));var MS;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(MS||(MS={}));var PS;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(PS||(PS={}));var zS;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(zS||(zS={}));var GS;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(GS||(GS={}));const u7={float32:zS,int32:MS,bool:PS,complex64:GS};function xR(r,l){if(r==="string"||l==="string"){if(r==="string"&&l==="string")return"string";throw new Error(`Can not upcast ${r} with ${l}`)}return u7[r][l]}function mt(r,l){if(r.dtype===l.dtype)return[r,l];const u=xR(r.dtype,l.dtype);return[r.cast(u),l.cast(u)]}function xf(r){const l=[],u=new Set;return TR(r,l,u),l}function TR(r,l,u){if(r==null)return;if(r instanceof In){l.push(r);return}if(!d7(r))return;const p=r;for(const g in p){const f=p[g];u.has(f)||(u.add(f),TR(f,l,u))}}function d7(r){return Array.isArray(r)||typeof r=="object"}class AR{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const r in this.registeredVariables)this.registeredVariables[r].dispose()}}class pu{constructor(r){this.ENV=r,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new AR}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const u=r[l],p=await this.initializeBackend(u).success;if(p){await this.setBackend(u);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 dR(this.backendInstance),!0}setupRegisteredKernels(){const r=FS(this.backendName);r.forEach(l=>{l.setupFunc!=null&&l.setupFunc(this.backendInstance)})}disposeRegisteredKernels(r){const l=FS(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 CC)&&typeof u.then=="function"){const p=++this.pendingBackendInitId,g=u.then(f=>p<this.pendingBackendInitId?!1:(this.registry[r]=f,this.pendingBackendInit=null,!0)).catch(f=>(p<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${r} failed`),console.warn(f.stack||f.message)),!1));return this.pendingBackendInit=g,{success:g,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:g}=this.initializeBackend(u);if(g||p)return{name:u,asyncInit:g}}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,g=this.readSync(l);p.disposeData(l),u.backend=r,r.move(l,g,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 pu.nextTensorId++}nextVariableId(){return pu.nextVariableId++}clone(r){const l=this.makeTensorFromDataId(r.dataId,r.shape,r.dtype),u={x:r},p=f=>({x:()=>{const I="float32",S={x:f},x={dtype:I};return Y.runKernelFunc(v=>v.cast(f,I),S,null,Lc,x)}}),g=[];return this.addTapeNode(this.state.activeScope.name,u,[l],p,g,{}),l}runKernel(r,l,u,p,g){const f=null,I=null;return this.runKernelFunc(f,l,I,r,u,p,g)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(r,l,u){const p=this.backend.numDataIds();let g=0;u.forEach(S=>{g+=S.dtype==="complex64"?3:1});const f=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],I=p-l-g-f;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,g,f,I){let S,x=[];const v=this.isTapeOn();p==null&&(p=this.state.activeScope!=null?this.state.activeScope.name:"");const O=this.state.numBytes,C=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let U;const G=yf(p,this.backendName);let ne;if(G!=null)U=()=>{const oe=this.backend.numDataIds();ne=G.kernelFunc({inputs:l,attrs:g,backend:this.backend});const ge=Array.isArray(ne)?ne:[ne];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,oe,ge);const fe=ge.map(({dataId:Ae,shape:Te,dtype:Ve})=>this.makeTensorFromDataId(Ae,Te,Ve));if(v){let Ae=this.getTensorsForGradient(p,l,fe);if(Ae==null){I==null&&(I=[]);const Te=fe.filter((Ve,rt)=>I[rt]);Ae=(f||[]).slice().concat(Te)}x=this.saveTensorsForBackwardMode(Ae)}return fe};else{const oe=ge=>{if(!v)return;x=ge.map(fe=>this.keep(this.clone(fe)))};U=()=>{const ge=this.backend.numDataIds();ne=this.tidy(()=>r(this.backend,oe));const fe=Array.isArray(ne)?ne:[ne];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,ge,fe),fe}}let te;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?S=U():(te=this.profiler.profileKernel(p,l,()=>U()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(te),S=te.outputs)}),v&&this.addTapeNode(p,l,S,u,x,g),this.state.profiling&&this.state.activeProfile.kernels.push({name:p,bytesAdded:this.state.numBytes-O,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-C,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(oe=>l[oe]!=null?l[oe].shape:null),outputShapes:S.map(oe=>oe.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=kS(r);if(p!=null){const g=p.inputsToSave||[],f=p.outputsToSave||[];let I;p.saveAllInputs?(Z(Array.isArray(l),()=>"saveAllInputs is true, expected inputs to be an array."),I=Object.keys(l).map(x=>l[x])):I=g.map(x=>l[x]);const S=u.filter((x,v)=>f[v]);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 g=r;u==="string"&&au(r[0])&&(g=r.map(S=>uR(S)));const f=p.write(g,l,u),I=new In(l,u,f,this.nextTensorId());if(this.incRef(I,p),u==="string"){const S=this.state.tensorInfo.get(f),x=lR(g);this.state.numBytes+=x-S.bytes,S.bytes=x}return I}makeTensorFromDataId(r,l,u,p){u=u||"float32";const g=new In(l,u,r,this.nextTensorId());return this.incRef(g,p),g}makeVariable(r,l=!0,u,p){u=u||this.nextVariableId().toString(),p!=null&&p!==r.dtype&&(r=r.cast(p));const g=new If(r,l,u,this.nextTensorId());if(this.state.registeredVariables[g.name]!=null)throw new Error(`Variable with name ${g.name} was already registered`);return this.state.registeredVariables[g.name]=g,this.incRef(g,this.backend),g}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*cR(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 If||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 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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 EI(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}=gt.getEnv(),{manifestUri:u,modelBaseUri:p}=Mf(r,this.getDefaultModelName()),g=x=>Promise.all(x.map(v=>l(v).then(O=>O.buffer))),f=fr.io.weightsLoaderFactory(g),I=JSON.parse((await l(u)).toString()),S=await f(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((g,f)=>{if(!g.nextObj.hasOwnProperty(f))throw new Error(`traversePropertyPath - object does not have property ${f}, for path ${r}`);return{obj:g.nextObj,objProp:f,nextObj:g.nextObj[f]}},{nextObj:this.params}),{obj:u,objProp:p}=l;if(!u||!p||!(u[p]instanceof fr.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${r}`);return{obj:u,objProp:p}}}const Fc=Ke(Xe());function Qn(r,l,u){return Fc.tidy(()=>{let p=Fc.separableConv2d(r,l.depthwise_filter,l.pointwise_filter,u,"same");return p=Fc.add(p,l.bias),p})}const Dt=Ke(Xe());function Pf(r,l,u=!1){return Dt.tidy(()=>{const p=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,[2,2],"same"),l.conv0.bias):Qn(r,l.conv0,[2,2])),g=Qn(p,l.conv1,[1,1]),f=Dt.relu(Dt.add(p,g)),I=Qn(f,l.conv2,[1,1]);return Dt.relu(Dt.add(p,Dt.add(g,I)))})}function Iu(r,l,u=!1,p=!0){return Dt.tidy(()=>{const g=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,p?[2,2]:[1,1],"same"),l.conv0.bias):Qn(r,l.conv0,p?[2,2]:[1,1])),f=Qn(g,l.conv1,[1,1]),I=Dt.relu(Dt.add(g,f)),S=Qn(I,l.conv2,[1,1]),x=Dt.relu(Dt.add(g,Dt.add(f,S))),v=Qn(x,l.conv3,[1,1]);return Dt.relu(Dt.add(g,Dt.add(f,Dt.add(S,v))))})}const no=Ke(Xe());function ia(r,l,u="same",p=!1){return no.tidy(()=>{const g=no.add(no.conv2d(r,l.filters,[1,1],u),l.bias);return p?no.relu(g):g})}function Pn(r,l){Object.keys(r).forEach(u=>{l.some(p=>p.originalPath===u)||r[u].dispose()})}const zf=Ke(Xe());function _c(r,l){return function(u,p,g,f){const I=zf.tensor4d(r(u*p*g*g),[g,g,u,p]),S=zf.tensor1d(r(p));return l.push({paramPath:`${f}/filters`},{paramPath:`${f}/bias`}),{filters:I,bias:S}}}const Gf=Ke(Xe());function Vf(r,l){return function(u,p,g){const f=Gf.tensor2d(r(u*p),[u,p]),I=Gf.tensor1d(r(p));return l.push({paramPath:`${g}/weights`},{paramPath:`${g}/bias`}),{weights:f,bias:I}}}class DI{constructor(r,l,u){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=u}}const xu=Ke(Xe());function Wc(r,l){return function(u,p,g){const f=xu.tensor4d(r(3*3*u),[3,3,u,1]),I=xu.tensor4d(r(u*p),[1,1,u,p]),S=xu.tensor1d(r(p));return l.push({paramPath:`${g}/depthwise_filter`},{paramPath:`${g}/pointwise_filter`},{paramPath:`${g}/bias`}),new DI(f,I,S)}}function $c(r){return function(l){const u=r(`${l}/depthwise_filter`,4),p=r(`${l}/pointwise_filter`,4),g=r(`${l}/bias`,1);return new DI(u,p,g)}}function ms(r,l){return function(u,p,g){const f=r[u];if(!Po(f,p))throw new Error(`expected weightMap[${u}] to be a Tensor${p}D, instead have ${f}`);return l.push({originalPath:u,paramPath:g||u}),f}}function zn(r){let l=r;function u(g){const f=l.slice(0,g);return l=l.slice(g),f}function p(){return l}return{extractWeights:u,getRemainingWeights:p}}function Yf(r,l){const u=_c(r,l),p=Wc(r,l);function g(I,S,x,v=!1){const O=v?u(I,S,3,`${x}/conv0`):p(I,S,`${x}/conv0`),C=p(S,S,`${x}/conv1`),U=p(S,S,`${x}/conv2`);return{conv0:O,conv1:C,conv2:U}}function f(I,S,x,v=!1){const{conv0:O,conv1:C,conv2:U}=g(I,S,x,v),G=p(S,S,`${x}/conv3`);return{conv0:O,conv1:C,conv2:U,conv3:G}}return{extractDenseBlock3Params:g,extractDenseBlock4Params:f}}function fE(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),{extractDenseBlock4Params:g}=Yf(u,l),f=g(3,32,"dense0",!0),I=g(32,64,"dense1"),S=g(64,128,"dense2"),x=g(128,256,"dense3");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S,dense3:x}}}function Hf(r){return function(l){const u=r(`${l}/filters`,4),p=r(`${l}/bias`,1);return{filters:u,bias:p}}}function qf(r,l){const u=ms(r,l),p=Hf(u),g=$c(u);function f(S,x=!1){const v=x?p(`${S}/conv0`):g(`${S}/conv0`),O=g(`${S}/conv1`),C=g(`${S}/conv2`);return{conv0:v,conv1:O,conv2:C}}function I(S,x=!1){const v=x?p(`${S}/conv0`):g(`${S}/conv0`),O=g(`${S}/conv1`),C=g(`${S}/conv2`),U=g(`${S}/conv3`);return{conv0:v,conv1:O,conv2:C,conv3:U}}return{extractDenseBlock3Params:f,extractDenseBlock4Params:I}}function gE(r){const l=[],{extractDenseBlock4Params:u}=qf(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2"),dense3:u("dense3")};return Pn(r,l),{params:p,paramMappings:l}}const so=Ke(Xe());class jf extends En{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return so.tidy(()=>{const u=so.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=di(u,p).div(so.scalar(255));let f=Iu(g,l.dense0,!0);return f=Iu(f,l.dense1),f=Iu(f,l.dense2),f=Iu(f,l.dense3),f=so.avgPool(f,[7,7],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return gE(r)}extractParams(r){return fE(r)}}const Uc=Ke(Xe());function Tu(r,l){return Uc.tidy(()=>Uc.add(Uc.matMul(r,l.weights),l.bias))}function yE(r,l,u){const p=[],{extractWeights:g,getRemainingWeights:f}=zn(r),I=Vf(g,p),S=I(l,u,"fc");if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{paramMappings:p,params:{fc:S}}}function bE(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:p("fc")};return Pn(r,l),{params:g,paramMappings:l}}function Kf(r){const l={},u={};return Object.keys(r).forEach(p=>{const g=p.startsWith("fc")?u:l;g[p]=r[p]}),{featureExtractorMap:l,classifierMap:u}}const wE=Ke(Xe());class Xf extends En{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return wE.tidy(()=>{const u=r instanceof to?this.faceFeatureExtractor.forwardInput(r):r;return Tu(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 yE(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Kf(r);return this.faceFeatureExtractor.loadFromWeightMap(l),bE(u)}extractParams(r){const l=this.getClassifierChannelsIn(),u=this.getClassifierChannelsOut(),p=u*l+u,g=r.slice(0,r.length-p),f=r.slice(r.length-p);return this.faceFeatureExtractor.extractWeights(g),this.extractClassifierParams(f)}}const kI=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class ra{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);kI.forEach((l,u)=>{this[l]=r[u]})}asSortedArray(){return kI.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const Bc=Ke(Xe());class FI extends Xf{constructor(r=new jf){super("FaceExpressionNet",r)}forwardInput(r){return Bc.tidy(()=>Bc.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Rt(r))}async predictExpressions(r){const l=await Rt(r),u=await this.forwardInput(l),p=await Promise.all(Bc.unstack(u).map(async f=>{const I=await f.data();return f.dispose(),I}));u.dispose();const g=p.map(f=>new ra(f));return l.isBatchInput?g:g[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function _I(r){return r.expressions instanceof ra}function Jf(r,l){const u={expressions:l};return Object.assign({},r,u)}function B9(r,l,u=.1,p){const g=Array.isArray(l)?l:[l];g.forEach(f=>{const I=f instanceof ra?f:_I(f)?f.expressions:void 0;if(!I)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const S=I.asSortedArray(),x=S.filter(C=>C.probability>u),v=Ui(f)?f.detection.box.bottomLeft:p||new Je(0,0),O=new yc(x.map(C=>`${C.expression} (${zo(C.probability)})`),v);O.draw(r)})}function oa(r){return Ui(r)&&r.landmarks instanceof Gs&&r.unshiftedLandmarks instanceof Gs&&r.alignedRect instanceof Ht}function Mc(r,l){const{box:u}=r.detection,p=l.shiftBy(u.x,u.y),g=p.align(),{imageDims:f}=r.detection,I=new Ht(r.detection.score,g.rescale(f.reverse()),f),S={landmarks:p,unshiftedLandmarks:l,alignedRect:I};return Object.assign({},r,S)}class LE{constructor(r={}){const{drawLines:l=!0,drawPoints:u=!0,lineWidth:p,lineColor:g,pointSize:f,pointColor:I}=r;this.drawLines=l,this.drawPoints=u,this.lineWidth=p||1,this.pointSize=f||2,this.lineColor=g||"rgba(0, 255, 255, 1)",this.pointColor=I||"rgba(255, 0, 255, 1)"}}class SE{constructor(r,l={}){this.faceLandmarks=r,this.options=new LE(l)}draw(r){const l=Jn(r),{drawLines:u,drawPoints:p,lineWidth:g,lineColor:f,pointSize:I,pointColor:S}=this.options;if(u&&this.faceLandmarks instanceof iu&&(l.strokeStyle=f,l.lineWidth=g,cr(l,this.faceLandmarks.getJawOutline()),cr(l,this.faceLandmarks.getLeftEyeBrow()),cr(l,this.faceLandmarks.getRightEyeBrow()),cr(l,this.faceLandmarks.getNose()),cr(l,this.faceLandmarks.getLeftEye(),!0),cr(l,this.faceLandmarks.getRightEye(),!0),cr(l,this.faceLandmarks.getMouth(),!0)),p){l.strokeStyle=S,l.fillStyle=S;const x=v=>{l.beginPath(),l.arc(v.x,v.y,I,0,2*Math.PI),l.fill()};this.faceLandmarks.positions.forEach(x)}}}function M9(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const g=p instanceof Gs?p:oa(p)?p.landmarks:void 0;if(!g)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new SE(g).draw(r)})}const WI={};hm(WI,{AnchorPosition:()=>Bi,DrawBox:()=>TS,DrawBoxOptions:()=>NC,DrawFaceLandmarks:()=>SE,DrawFaceLandmarksOptions:()=>LE,DrawTextField:()=>yc,DrawTextFieldOptions:()=>ym,drawContour:()=>cr,drawDetections:()=>KX,drawFaceExpressions:()=>B9,drawFaceLandmarks:()=>M9});function P9(r,l){const u=_c(r,l),p=Wc(r,l);function g(I,S,x){const v=p(I,S,`${x}/separable_conv0`),O=p(S,S,`${x}/separable_conv1`),C=u(I,S,1,`${x}/expansion_conv`);return{separable_conv0:v,separable_conv1:O,expansion_conv:C}}function f(I,S){const x=p(I,I,`${S}/separable_conv0`),v=p(I,I,`${S}/separable_conv1`),O=p(I,I,`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:u,extractSeparableConvParams:p,extractReductionBlockParams:g,extractMainBlockParams:f}}function IE(r,l){const u=[],{extractWeights:p,getRemainingWeights:g}=zn(r),{extractConvParams:f,extractSeparableConvParams:I,extractReductionBlockParams:S,extractMainBlockParams:x}=P9(p,u),v=f(3,32,3,"entry_flow/conv_in"),O=S(32,64,"entry_flow/reduction_block_0"),C=S(64,128,"entry_flow/reduction_block_1"),U={conv_in:v,reduction_block_0:O,reduction_block_1:C},G={};Wi(l,0,1).forEach(ge=>{G[`main_block_${ge}`]=x(128,`middle_flow/main_block_${ge}`)});const ne=S(128,256,"exit_flow/reduction_block"),te=I(256,512,"exit_flow/separable_conv"),oe={reduction_block:ne,separable_conv:te};if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:u,params:{entry_flow:U,middle_flow:G,exit_flow:oe}}}function z9(r,l){const u=ms(r,l),p=Hf(u),g=$c(u);function f(S){const x=g(`${S}/separable_conv0`),v=g(`${S}/separable_conv1`),O=p(`${S}/expansion_conv`);return{separable_conv0:x,separable_conv1:v,expansion_conv:O}}function I(S){const x=g(`${S}/separable_conv0`),v=g(`${S}/separable_conv1`),O=g(`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}}function xE(r,l){const u=[],{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}=z9(r,u),S=p("entry_flow/conv_in"),x=f("entry_flow/reduction_block_0"),v=f("entry_flow/reduction_block_1"),O={conv_in:S,reduction_block_0:x,reduction_block_1:v},C={};Wi(l,0,1).forEach(te=>{C[`main_block_${te}`]=I(`middle_flow/main_block_${te}`)});const U=f("exit_flow/reduction_block"),G=g("exit_flow/separable_conv"),ne={reduction_block:U,separable_conv:G};return Pn(r,u),{params:{entry_flow:O,middle_flow:C,exit_flow:ne},paramMappings:u}}const tn=Ke(Xe());function TE(r,l,u){return tn.add(tn.conv2d(r,l.filters,u,"same"),l.bias)}function $I(r,l,u=!0){let p=u?tn.relu(r):r;return p=Qn(p,l.separable_conv0,[1,1]),p=Qn(tn.relu(p),l.separable_conv1,[1,1]),p=tn.maxPool(p,[3,3],[2,2],"same"),p=tn.add(p,TE(r,l.expansion_conv,[2,2])),p}function G9(r,l){let u=Qn(tn.relu(r),l.separable_conv0,[1,1]);return u=Qn(tn.relu(u),l.separable_conv1,[1,1]),u=Qn(tn.relu(u),l.separable_conv2,[1,1]),u=tn.add(u,r),u}class AE extends En{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return tn.tidy(()=>{const u=tn.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=di(u,p).div(tn.scalar(256));let f=tn.relu(TE(g,l.entry_flow.conv_in,[2,2]));return f=$I(f,l.entry_flow.reduction_block_0,!1),f=$I(f,l.entry_flow.reduction_block_1),Wi(this._numMainBlocks,0,1).forEach(I=>{f=G9(f,l.middle_flow[`main_block_${I}`])}),f=$I(f,l.exit_flow.reduction_block),f=tn.relu(Qn(f,l.exit_flow.separable_conv,[1,1])),f})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return xE(r,this._numMainBlocks)}extractParams(r){return IE(r,this._numMainBlocks)}}function vE(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),g=Vf(u,l),f=g(512,1,"fc/age"),I=g(512,2,"fc/gender");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{fc:{age:f,gender:I}}}}function NE(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:{age:p("fc/age"),gender:p("fc/gender")}};return Pn(r,l),{params:g,paramMappings:l}}var gr;(function(r){r.FEMALE="female",r.MALE="male"})(gr||(gr={}));const Vi=Ke(Xe());class UI extends En{constructor(r=new AE(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Vi.tidy(()=>{const u=r instanceof to?this.faceFeatureExtractor.forwardInput(r):r,p=Vi.avgPool(u,[7,7],[2,2],"valid").as2D(u.shape[0],-1),g=Tu(p,l.fc.age).as1D(),f=Tu(p,l.fc.gender);return{age:g,gender:f}})}forwardInput(r){return Vi.tidy(()=>{const{age:l,gender:u}=this.runNet(r);return{age:l,gender:Vi.softmax(u)}})}async forward(r){return this.forwardInput(await Rt(r))}async predictAgeAndGender(r){const l=await Rt(r),u=await this.forwardInput(l),p=Vi.unstack(u.age),g=Vi.unstack(u.gender),f=p.map((S,x)=>({ageTensor:S,genderTensor:g[x]})),I=await Promise.all(f.map(async({ageTensor:S,genderTensor:x})=>{const v=(await S.data())[0],O=(await x.data())[0],C=O>.5,U=C?gr.MALE:gr.FEMALE,G=C?O:1-O;return S.dispose(),x.dispose(),{age:v,gender:U,genderProbability:G}}));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 vE(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Kf(r);return this.faceFeatureExtractor.loadFromWeightMap(l),NE(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 fs=Ke(Xe());class Zf extends Xf{postProcess(r,l,u){const p=u.map(({width:f,height:I})=>{const S=l/Math.max(I,f);return{width:f*S,height:I*S}}),g=p.length;return fs.tidy(()=>{const f=(O,C)=>fs.stack([fs.fill([68],O,"float32"),fs.fill([68],C,"float32")],1).as2D(1,136).as1D(),I=(O,C)=>{const{width:U,height:G}=p[O];return C(U,G)?Math.abs(U-G)/2:0},S=O=>I(O,(C,U)=>C<U),x=O=>I(O,(C,U)=>U<C),v=r.mul(fs.fill([g,136],l,"float32")).sub(fs.stack(Array.from(Array(g),(O,C)=>f(S(C),x(C))))).div(fs.stack(Array.from(Array(g),(O,C)=>f(p[C].width,p[C].height))));return v})}forwardInput(r){return fs.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 Rt(r))}async detectLandmarks(r){const l=await Rt(r),u=fs.tidy(()=>fs.unstack(this.forwardInput(l))),p=await Promise.all(u.map(async(g,f)=>{const I=Array.from(await g.data()),S=I.filter((v,O)=>dm(O)),x=I.filter((v,O)=>!dm(O));return new iu(Array(68).fill(0).map((v,O)=>new Je(S[O],x[O])),{height:l.getInputHeight(f),width:l.getInputWidth(f)})}));return u.forEach(g=>g.dispose()),l.isBatchInput?p:p[0]}getClassifierChannelsOut(){return 136}}class Au extends Zf{constructor(r=new jf){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function CE(r){const l=[],{extractDenseBlock3Params:u}=qf(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return Pn(r,l),{params:p,paramMappings:l}}function RE(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),{extractDenseBlock3Params:g}=Yf(u,l),f=g(3,32,"dense0",!0),I=g(32,64,"dense1"),S=g(64,128,"dense2");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S}}}const io=Ke(Xe());class OE extends En{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return io.tidy(()=>{const u=io.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=di(u,p).div(io.scalar(255));let f=Pf(g,l.dense0,!0);return f=Pf(f,l.dense1),f=Pf(f,l.dense2),f=io.avgPool(f,[14,14],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return CE(r)}extractParams(r){return RE(r)}}class BI extends Zf{constructor(r=new OE){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class V9 extends Au{}const Qf=Ke(Xe());function EE(r,l){return Qf.add(Qf.mul(r,l.weights),l.biases)}const Pc=Ke(Xe());function MI(r,l,u,p,g="same"){const{filters:f,bias:I}=l.conv;let S=Pc.conv2d(r,f,u,g);return S=Pc.add(S,I),S=EE(S,l.scale),p?Pc.relu(S):S}function DE(r,l){return MI(r,l,[1,1],!0)}function PI(r,l){return MI(r,l,[1,1],!1)}function eg(r,l){return MI(r,l,[2,2],!0,"valid")}const gs=Ke(Xe());function Y9(r,l){function u(S,x,v){const O=r(S),C=O.length/(x*v*v);if(dS(C))throw new Error(`depth has to be an integer: ${C}, weights.length: ${O.length}, numFilters: ${x}, filterSize: ${v}`);return gs.tidy(()=>gs.transpose(gs.tensor4d(O,[x,C,v,v]),[2,3,1,0]))}function p(S,x,v,O){const C=u(S,x,v),U=gs.tensor1d(r(x));return l.push({paramPath:`${O}/filters`},{paramPath:`${O}/bias`}),{filters:C,bias:U}}function g(S,x){const v=gs.tensor1d(r(S)),O=gs.tensor1d(r(S));return l.push({paramPath:`${x}/weights`},{paramPath:`${x}/biases`}),{weights:v,biases:O}}function f(S,x,v,O){const C=p(S,x,v,`${O}/conv`),U=g(x,`${O}/scale`);return{conv:C,scale:U}}function I(S,x,v,O,C=!1){const U=f((C?.5:1)*S,x,v,`${O}/conv1`),G=f(S,x,v,`${O}/conv2`);return{conv1:U,conv2:G}}return{extractConvLayerParams:f,extractResidualLayerParams:I}}function kE(r){const{extractWeights:l,getRemainingWeights:u}=zn(r),p=[],{extractConvLayerParams:g,extractResidualLayerParams:f}=Y9(l,p),I=g(4704,32,7,"conv32_down"),S=f(9216,32,3,"conv32_1"),x=f(9216,32,3,"conv32_2"),v=f(9216,32,3,"conv32_3"),O=f(36864,64,3,"conv64_down",!0),C=f(36864,64,3,"conv64_1"),U=f(36864,64,3,"conv64_2"),G=f(36864,64,3,"conv64_3"),ne=f(147456,128,3,"conv128_down",!0),te=f(147456,128,3,"conv128_1"),oe=f(147456,128,3,"conv128_2"),ge=f(589824,256,3,"conv256_down",!0),fe=f(589824,256,3,"conv256_1"),Ae=f(589824,256,3,"conv256_2"),Te=f(589824,256,3,"conv256_down_out"),Ve=gs.tidy(()=>gs.transpose(gs.tensor2d(l(256*128),[128,256]),[1,0]));if(p.push({paramPath:"fc"}),u().length!==0)throw new Error(`weights remaing after extract: ${u().length}`);const rt={conv32_down:I,conv32_1:S,conv32_2:x,conv32_3:v,conv64_down:O,conv64_1:C,conv64_2:U,conv64_3:G,conv128_down:ne,conv128_1:te,conv128_2:oe,conv256_down:ge,conv256_1:fe,conv256_2:Ae,conv256_down_out:Te,fc:Ve};return{params:rt,paramMappings:p}}function H9(r,l){const u=ms(r,l);function p(I){const S=u(`${I}/scale/weights`,1),x=u(`${I}/scale/biases`,1);return{weights:S,biases:x}}function g(I){const S=u(`${I}/conv/filters`,4),x=u(`${I}/conv/bias`,1),v=p(I);return{conv:{filters:S,bias:x},scale:v}}function f(I){return{conv1:g(`${I}/conv1`),conv2:g(`${I}/conv2`)}}return{extractConvLayerParams:g,extractResidualLayerParams:f}}function FE(r){const l=[],{extractConvLayerParams:u,extractResidualLayerParams:p}=H9(r,l),g=u("conv32_down"),f=p("conv32_1"),I=p("conv32_2"),S=p("conv32_3"),x=p("conv64_down"),v=p("conv64_1"),O=p("conv64_2"),C=p("conv64_3"),U=p("conv128_down"),G=p("conv128_1"),ne=p("conv128_2"),te=p("conv256_down"),oe=p("conv256_1"),ge=p("conv256_2"),fe=p("conv256_down_out"),Ae=r.fc;if(l.push({originalPath:"fc",paramPath:"fc"}),!uS(Ae))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${Ae}`);const Te={conv32_down:g,conv32_1:f,conv32_2:I,conv32_3:S,conv64_down:x,conv64_1:v,conv64_2:O,conv64_3:C,conv128_down:U,conv128_1:G,conv128_2:ne,conv256_down:te,conv256_1:oe,conv256_2:ge,conv256_down_out:fe,fc:Ae};return Pn(r,l),{params:Te,paramMappings:l}}const Gn=Ke(Xe());function fi(r,l){let u=DE(r,l.conv1);return u=PI(u,l.conv2),u=Gn.add(u,r),u=Gn.relu(u),u}function vu(r,l){let u=eg(r,l.conv1);u=PI(u,l.conv2);let p=Gn.avgPool(r,2,2,"valid");const g=Gn.zeros(p.shape),f=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 x=Gn.zeros(S);u=Gn.concat([u,x],1);const v=[...u.shape];v[2]=1;const O=Gn.zeros(v);u=Gn.concat([u,O],2)}return p=f?Gn.concat([p,g],3):p,u=Gn.add(p,u),u=Gn.relu(u),u}const Ds=Ke(Xe());class Nu extends En{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return Ds.tidy(()=>{const u=Ds.cast(r.toBatchTensor(150,!0),"float32"),p=[122.782,117.001,104.298],g=di(u,p).div(Ds.scalar(256));let f=eg(g,l.conv32_down);f=Ds.maxPool(f,3,2,"valid"),f=fi(f,l.conv32_1),f=fi(f,l.conv32_2),f=fi(f,l.conv32_3),f=vu(f,l.conv64_down),f=fi(f,l.conv64_1),f=fi(f,l.conv64_2),f=fi(f,l.conv64_3),f=vu(f,l.conv128_down),f=fi(f,l.conv128_1),f=fi(f,l.conv128_2),f=vu(f,l.conv256_down),f=fi(f,l.conv256_1),f=fi(f,l.conv256_2),f=vu(f,l.conv256_down_out);const I=f.mean([1,2]),S=Ds.matMul(I,l.fc);return S})}async forward(r){return this.forwardInput(await Rt(r))}async computeFaceDescriptor(r){const l=await Rt(r),u=Ds.tidy(()=>Ds.unstack(this.forwardInput(l))),p=await Promise.all(u.map(g=>g.data()));return u.forEach(g=>g.dispose()),l.isBatchInput?p:p[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(r){return FE(r)}extractParams(r){return kE(r)}}function q9(r){const l=new Nu;return l.extractWeights(r),l}function tg(r,l){const u={descriptor:l};return Object.assign({},r,u)}function j9(r){return typeof r.age=="number"}function ng(r,l){const u={age:l};return Object.assign({},r,u)}function K9(r){return(r.gender===gr.MALE||r.gender===gr.FEMALE)&&fc(r.genderProbability)}function sg(r,l,u){const p={gender:l,genderProbability:u};return Object.assign({},r,p)}const gi=Ke(Xe());function X9(r,l){function u(x,v){const O=gi.tensor4d(r(3*3*x),[3,3,x,1]),C=gi.tensor1d(r(x)),U=gi.tensor1d(r(x)),G=gi.tensor1d(r(x)),ne=gi.tensor1d(r(x));return l.push({paramPath:`${v}/filters`},{paramPath:`${v}/batch_norm_scale`},{paramPath:`${v}/batch_norm_offset`},{paramPath:`${v}/batch_norm_mean`},{paramPath:`${v}/batch_norm_variance`}),{filters:O,batch_norm_scale:C,batch_norm_offset:U,batch_norm_mean:G,batch_norm_variance:ne}}function p(x,v,O,C,U){const G=gi.tensor4d(r(x*v*O*O),[O,O,x,v]),ne=gi.tensor1d(r(v));return l.push({paramPath:`${C}/filters`},{paramPath:`${C}/${U?"batch_norm_offset":"bias"}`}),{filters:G,bias:ne}}function g(x,v,O,C){const{filters:U,bias:G}=p(x,v,O,C,!0);return{filters:U,batch_norm_offset:G}}function f(x,v,O){const C=u(x,`${O}/depthwise_conv`),U=g(x,v,1,`${O}/pointwise_conv`);return{depthwise_conv:C,pointwise_conv:U}}function I(){const x=g(3,32,3,"mobilenetv1/conv_0"),v=f(32,64,"mobilenetv1/conv_1"),O=f(64,128,"mobilenetv1/conv_2"),C=f(128,128,"mobilenetv1/conv_3"),U=f(128,256,"mobilenetv1/conv_4"),G=f(256,256,"mobilenetv1/conv_5"),ne=f(256,512,"mobilenetv1/conv_6"),te=f(512,512,"mobilenetv1/conv_7"),oe=f(512,512,"mobilenetv1/conv_8"),ge=f(512,512,"mobilenetv1/conv_9"),fe=f(512,512,"mobilenetv1/conv_10"),Ae=f(512,512,"mobilenetv1/conv_11"),Te=f(512,1024,"mobilenetv1/conv_12"),Ve=f(1024,1024,"mobilenetv1/conv_13");return{conv_0:x,conv_1:v,conv_2:O,conv_3:C,conv_4:U,conv_5:G,conv_6:ne,conv_7:te,conv_8:oe,conv_9:ge,conv_10:fe,conv_11:Ae,conv_12:Te,conv_13:Ve}}function S(){const x=g(1024,256,1,"prediction_layer/conv_0"),v=g(256,512,3,"prediction_layer/conv_1"),O=g(512,128,1,"prediction_layer/conv_2"),C=g(128,256,3,"prediction_layer/conv_3"),U=g(256,128,1,"prediction_layer/conv_4"),G=g(128,256,3,"prediction_layer/conv_5"),ne=g(256,64,1,"prediction_layer/conv_6"),te=g(64,128,3,"prediction_layer/conv_7"),oe=p(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),ge=p(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),fe=p(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),Ae=p(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),Te=p(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Ve=p(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),rt=p(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),vt=p(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),$t=p(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Kt=p(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Dn=p(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Tn=p(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),An={box_encoding_predictor:oe,class_predictor:ge},Ks={box_encoding_predictor:fe,class_predictor:Ae},Li={box_encoding_predictor:Te,class_predictor:Ve},Xs={box_encoding_predictor:rt,class_predictor:vt},ua={box_encoding_predictor:$t,class_predictor:Kt},Xc={box_encoding_predictor:Dn,class_predictor:Tn};return{conv_0:x,conv_1:v,conv_2:O,conv_3:C,conv_4:U,conv_5:G,conv_6:ne,conv_7:te,box_predictor_0:An,box_predictor_1:Ks,box_predictor_2:Li,box_predictor_3:Xs,box_predictor_4:ua,box_predictor_5:Xc}}return{extractMobilenetV1Params:I,extractPredictionLayerParams:S}}function _E(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),{extractMobilenetV1Params:g,extractPredictionLayerParams:f}=X9(u,l),I=g(),S=f(),x=gi.tensor3d(u(5118*4),[1,5118,4]),v={extra_dim:x};if(l.push({paramPath:"output_layer/extra_dim"}),p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{params:{mobilenetv1:I,prediction_layer:S,output_layer:v},paramMappings:l}}function J9(r,l){const u=ms(r,l);function p(v,O,C){const U=u(`${v}/Conv2d_${O}_pointwise/weights`,4,`${C}/filters`),G=u(`${v}/Conv2d_${O}_pointwise/convolution_bn_offset`,1,`${C}/batch_norm_offset`);return{filters:U,batch_norm_offset:G}}function g(v){const O=`mobilenetv1/conv_${v}`,C=`MobilenetV1/Conv2d_${v}_depthwise`,U=`${O}/depthwise_conv`,G=`${O}/pointwise_conv`,ne=u(`${C}/depthwise_weights`,4,`${U}/filters`),te=u(`${C}/BatchNorm/gamma`,1,`${U}/batch_norm_scale`),oe=u(`${C}/BatchNorm/beta`,1,`${U}/batch_norm_offset`),ge=u(`${C}/BatchNorm/moving_mean`,1,`${U}/batch_norm_mean`),fe=u(`${C}/BatchNorm/moving_variance`,1,`${U}/batch_norm_variance`);return{depthwise_conv:{filters:ne,batch_norm_scale:te,batch_norm_offset:oe,batch_norm_mean:ge,batch_norm_variance:fe},pointwise_conv:p("MobilenetV1",v,G)}}function f(){return{conv_0:p("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:g(1),conv_2:g(2),conv_3:g(3),conv_4:g(4),conv_5:g(5),conv_6:g(6),conv_7:g(7),conv_8:g(8),conv_9:g(9),conv_10:g(10),conv_11:g(11),conv_12:g(12),conv_13:g(13)}}function I(v,O){const C=u(`${v}/weights`,4,`${O}/filters`),U=u(`${v}/biases`,1,`${O}/bias`);return{filters:C,bias:U}}function S(v){const O=I(`Prediction/BoxPredictor_${v}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${v}/box_encoding_predictor`),C=I(`Prediction/BoxPredictor_${v}/ClassPredictor`,`prediction_layer/box_predictor_${v}/class_predictor`);return{box_encoding_predictor:O,class_predictor:C}}function x(){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:f,extractPredictionLayerParams:x}}function WE(r){const l=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:p}=J9(r,l),g=r["Output/extra_dim"];if(l.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!lr(g))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${g}`);const f={mobilenetv1:u(),prediction_layer:p(),output_layer:{extra_dim:g}};return Pn(r,l),{params:f,paramMappings:l}}const ro=Ke(Xe());function Hs(r,l,u){return ro.tidy(()=>{let p=ro.conv2d(r,l.filters,u,"same");return p=ro.add(p,l.batch_norm_offset),ro.clipByValue(p,0,6)})}const yr=Ke(Xe()),Z9=.0010000000474974513;function Q9(r,l,u){return yr.tidy(()=>{let p=yr.depthwiseConv2d(r,l.filters,u,"same");return p=yr.batchNorm(p,l.batch_norm_mean,l.batch_norm_variance,l.batch_norm_offset,l.batch_norm_scale,Z9),yr.clipByValue(p,0,6)})}function eZ(r){return[2,4,6,12].some(l=>l===r)?[2,2]:[1,1]}function $E(r,l){return yr.tidy(()=>{let u,p=Hs(r,l.conv_0,[2,2]);const g=[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(g.forEach((f,I)=>{const S=I+1,x=eZ(S);p=Q9(p,f.depthwise_conv,x),p=Hs(p,f.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 UE(r,l,u,p,g){const f=r.shape[0],I=Math.min(u,f),S=l.map((O,C)=>({score:O,boxIndex:C})).filter(O=>O.score>g).sort((O,C)=>C.score-O.score),x=O=>O<=p?1:0,v=[];return S.forEach(O=>{if(v.length>=I)return;const C=O.score;for(let U=v.length-1;U>=0;--U){const G=tZ(r,O.boxIndex,v[U]);if(G===0)continue;if(O.score*=x(G),O.score<=g)break}C===O.score&&v.push(O.boxIndex)}),v}function tZ(r,l,u){const p=r.arraySync(),g=Math.min(p[l][0],p[l][2]),f=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]),x=Math.min(p[u][0],p[u][2]),v=Math.min(p[u][1],p[u][3]),O=Math.max(p[u][0],p[u][2]),C=Math.max(p[u][1],p[u][3]),U=(I-g)*(S-f),G=(O-x)*(C-v);if(U<=0||G<=0)return 0;const ne=Math.max(g,x),te=Math.max(f,v),oe=Math.min(I,O),ge=Math.min(S,C),fe=Math.max(oe-ne,0)*Math.max(ge-te,0);return fe/(U+G-fe)}const De=Ke(Xe());function nZ(r){const l=De.unstack(De.transpose(r,[1,0])),u=[De.sub(l[2],l[0]),De.sub(l[3],l[1])],p=[De.add(l[0],De.div(u[0],De.scalar(2))),De.add(l[1],De.div(u[1],De.scalar(2)))];return{sizes:u,centers:p}}function sZ(r,l){const{sizes:u,centers:p}=nZ(r),g=De.unstack(De.transpose(l,[1,0])),f=De.div(De.mul(De.exp(De.div(g[2],De.scalar(5))),u[0]),De.scalar(2)),I=De.add(De.mul(De.div(g[0],De.scalar(10)),u[0]),p[0]),S=De.div(De.mul(De.exp(De.div(g[3],De.scalar(5))),u[1]),De.scalar(2)),x=De.add(De.mul(De.div(g[1],De.scalar(10)),u[1]),p[1]);return De.transpose(De.stack([De.sub(I,f),De.sub(x,S),De.add(I,f),De.add(x,S)]),[1,0])}function BE(r,l,u){return De.tidy(()=>{const p=r.shape[0];let g=sZ(De.reshape(De.tile(u.extra_dim,[p,1,1]),[-1,4]),De.reshape(r,[-1,4]));g=De.reshape(g,[p,g.shape[0]/p,4]);const f=De.sigmoid(De.slice(l,[0,0,1],[-1,-1,-1]));let I=De.slice(f,[0,0,0],[-1,-1,1]);I=De.reshape(I,[p,I.shape[1]]);const S=De.unstack(g),x=De.unstack(I);return{boxes:S,scores:x}})}const Cu=Ke(Xe());function aa(r,l){return Cu.tidy(()=>{const u=r.shape[0],p=Cu.reshape(ia(r,l.box_encoding_predictor),[u,-1,1,4]),g=Cu.reshape(ia(r,l.class_predictor),[u,-1,3]);return{boxPredictionEncoding:p,classPrediction:g}})}const Ru=Ke(Xe());function ME(r,l,u){return Ru.tidy(()=>{const p=Hs(r,u.conv_0,[1,1]),g=Hs(p,u.conv_1,[2,2]),f=Hs(g,u.conv_2,[1,1]),I=Hs(f,u.conv_3,[2,2]),S=Hs(I,u.conv_4,[1,1]),x=Hs(S,u.conv_5,[2,2]),v=Hs(x,u.conv_6,[1,1]),O=Hs(v,u.conv_7,[2,2]),C=aa(l,u.box_predictor_0),U=aa(r,u.box_predictor_1),G=aa(g,u.box_predictor_2),ne=aa(I,u.box_predictor_3),te=aa(x,u.box_predictor_4),oe=aa(O,u.box_predictor_5),ge=Ru.concat([C.boxPredictionEncoding,U.boxPredictionEncoding,G.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,oe.boxPredictionEncoding],1),fe=Ru.concat([C.classPrediction,U.classPrediction,G.classPrediction,ne.classPrediction,te.classPrediction,oe.classPrediction],1);return{boxPredictions:ge,classPredictions:fe}})}class yi{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(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}const bi=Ke(Xe());class zc extends En{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return bi.tidy(()=>{const u=bi.cast(r.toBatchTensor(512,!1),"float32"),p=bi.sub(bi.mul(u,bi.scalar(.007843137718737125)),bi.scalar(1)),g=$E(p,l.mobilenetv1),{boxPredictions:f,classPredictions:I}=ME(g.out,g.conv11,l.prediction_layer);return BE(f,I,l.output_layer)})}async forward(r){return this.forwardInput(await Rt(r))}async locateFaces(r,l={}){const{maxResults:u,minConfidence:p}=new yi(l),g=await Rt(r),{boxes:f,scores:I}=this.forwardInput(g),S=f[0],x=I[0];for(let fe=1;fe<f.length;fe++)f[fe].dispose(),I[fe].dispose();const v=Array.from(await x.data()),O=.5,C=UE(S,v,u,O,p),U=g.getReshapedInputDimensions(0),G=g.inputSize,ne=G/U.width,te=G/U.height,oe=S.arraySync(),ge=C.map(fe=>{const[Ae,Te]=[Math.max(0,oe[fe][0]),Math.min(1,oe[fe][2])].map(vt=>vt*te),[Ve,rt]=[Math.max(0,oe[fe][1]),Math.min(1,oe[fe][3])].map(vt=>vt*ne);return new Ht(v[fe],new su(Ve,Ae,rt-Ve,Te-Ae),{height:g.getInputHeight(0),width:g.getInputWidth(0)})});return S.dispose(),x.dispose(),ge}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(r){return WE(r)}extractParams(r){return _E(r)}}function PE(r){const l=new zc;return l.extractWeights(r),l}function iZ(r){return PE(r)}class rZ extends zc{}const zE=.4,GE=[new Je(.738768,.874946),new Je(2.42204,2.65704),new Je(4.30971,7.04493),new Je(10.246,4.59428),new Je(12.6868,11.8741)],VE=[new Je(1.603231,2.094468),new Je(6.041143,7.080126),new Je(2.882459,3.518061),new Je(4.266906,5.178857),new Je(9.041765,10.66308)],YE=[117.001,114.697,97.404],HE="tiny_yolov2_model",qE="tiny_yolov2_separable_conv_model";const ig=r=>typeof r=="number";function zI(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(!ig(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=>ig(l.x)&&ig(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(ig)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const qs=Ke(Xe());function Gc(r){return qs.tidy(()=>{const l=qs.mul(r,qs.scalar(.10000000149011612));return qs.add(qs.relu(qs.sub(r,l)),l)})}const js=Ke(Xe());function br(r,l){return js.tidy(()=>{let u=js.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=js.conv2d(u,l.conv.filters,[1,1],"valid"),u=js.sub(u,l.bn.sub),u=js.mul(u,l.bn.truediv),u=js.add(u,l.conv.bias),Gc(u)})}const oo=Ke(Xe());function wr(r,l){return oo.tidy(()=>{let u=oo.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=oo.separableConv2d(u,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),u=oo.add(u,l.bias),Gc(u)})}const GI=Ke(Xe());function oZ(r,l){const u=_c(r,l);function p(I,S){const x=GI.tensor1d(r(I)),v=GI.tensor1d(r(I));return l.push({paramPath:`${S}/sub`},{paramPath:`${S}/truediv`}),{sub:x,truediv:v}}function g(I,S,x){const v=u(I,S,3,`${x}/conv`),O=p(S,`${x}/bn`);return{conv:v,bn:O}}const f=Wc(r,l);return{extractConvParams:u,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}}function jE(r,l,u,p){const{extractWeights:g,getRemainingWeights:f}=zn(r),I=[],{extractConvParams:S,extractConvWithBatchNormParams:x,extractSeparableConvParams:v}=oZ(g,I);let O;if(l.withSeparableConvs){const[C,U,G,ne,te,oe,ge,fe,Ae]=p,Te=l.isFirstLayerConv2d?S(C,U,3,"conv0"):v(C,U,"conv0"),Ve=v(U,G,"conv1"),rt=v(G,ne,"conv2"),vt=v(ne,te,"conv3"),$t=v(te,oe,"conv4"),Kt=v(oe,ge,"conv5"),Dn=fe?v(ge,fe,"conv6"):void 0,Tn=Ae?v(fe,Ae,"conv7"):void 0,An=S(Ae||fe||ge,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:vt,conv4:$t,conv5:Kt,conv6:Dn,conv7:Tn,conv8:An}}else{const[C,U,G,ne,te,oe,ge,fe,Ae]=p,Te=x(C,U,"conv0"),Ve=x(U,G,"conv1"),rt=x(G,ne,"conv2"),vt=x(ne,te,"conv3"),$t=x(te,oe,"conv4"),Kt=x(oe,ge,"conv5"),Dn=x(ge,fe,"conv6"),Tn=x(fe,Ae,"conv7"),An=S(Ae,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:vt,conv4:$t,conv5:Kt,conv6:Dn,conv7:Tn,conv8:An}}if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{params:O,paramMappings:I}}function aZ(r,l){const u=ms(r,l);function p(S){const x=u(`${S}/sub`,1),v=u(`${S}/truediv`,1);return{sub:x,truediv:v}}function g(S){const x=u(`${S}/filters`,4),v=u(`${S}/bias`,1);return{filters:x,bias:v}}function f(S){const x=g(`${S}/conv`),v=p(`${S}/bn`);return{conv:x,bn:v}}const I=$c(u);return{extractConvParams:g,extractConvWithBatchNormParams:f,extractSeparableConvParams:I}}function KE(r,l){const u=[],{extractConvParams:p,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}=aZ(r,u);let I;if(l.withSeparableConvs){const S=l.filterSizes&&l.filterSizes.length||9;I={conv0:l.isFirstLayerConv2d?p("conv0"):f("conv0"),conv1:f("conv1"),conv2:f("conv2"),conv3:f("conv3"),conv4:f("conv4"),conv5:f("conv5"),conv6:S>7?f("conv6"):void 0,conv7:S>8?f("conv7"):void 0,conv8:p("conv8")}}else I={conv0:g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:g("conv6"),conv7:g("conv7"),conv8:p("conv8")};return Pn(r,u),{params:I,paramMappings:u}}var VI;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(VI||(VI={}));class Lr{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 kt=Ke(Xe());class Vc extends En{constructor(r){super("TinyYolov2");zI(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=br(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=br(u,l.conv6),u=br(u,l.conv7),ia(u,l.conv8,"valid",!1)}runMobilenet(r,l){let u=this.config.isFirstLayerConv2d?Gc(ia(r,l.conv0,"valid",!1)):wr(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=l.conv6?wr(u,l.conv6):u,u=l.conv7?wr(u,l.conv7):u,ia(u,l.conv8,"valid",!1)}forwardInput(r,l){const{params:u}=this;if(!u)throw new Error("TinyYolov2 - load model before inference");return kt.tidy(()=>{let p=kt.cast(r.toBatchTensor(l,!1),"float32");return p=this.config.meanRgb?di(p,this.config.meanRgb):p,p=p.div(kt.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(p,u):this.runTinyYolov2(p,u)})}async forward(r,l){return await this.forwardInput(await Rt(r),l)}async detect(r,l={}){const{inputSize:u,scoreThreshold:p}=new Lr(l),g=await Rt(r),f=await this.forwardInput(g,u),I=kt.tidy(()=>kt.unstack(f)[0].expandDims()),S={width:g.getInputWidth(0),height:g.getInputHeight(0)},x=await this.extractBoxes(I,g.getReshapedInputDimensions(0),p);f.dispose(),I.dispose();const v=x.map(te=>te.box),O=x.map(te=>te.score),C=x.map(te=>te.classScore),U=x.map(te=>this.config.classes[te.label]),G=gS(v.map(te=>te.rescale(u)),O,this.config.iouThreshold,!0),ne=G.map(te=>new gc(O[te],C[te],U[te],v[te],S));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return KE(r,this.config)}extractParams(r){const l=this.config.filterSizes||Vc.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 ${u} filterSizes in config`);return jE(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,u){const{width:p,height:g}=l,f=Math.max(p,g),I=f/p,S=f/g,x=r.shape[1],v=this.config.anchors.length,[O,C,U]=kt.tidy(()=>{const oe=r.reshape([x,x,v,this.boxEncodingSize]),ge=oe.slice([0,0,0,0],[x,x,v,4]),fe=oe.slice([0,0,0,4],[x,x,v,1]),Ae=this.withClassScores?kt.softmax(oe.slice([0,0,0,5],[x,x,v,this.config.classes.length]),3):kt.scalar(0);return[ge,fe,Ae]}),G=[],ne=await C.array(),te=await O.array();for(let oe=0;oe<x;oe++)for(let ge=0;ge<x;ge++)for(let fe=0;fe<v;fe++){const Ae=nu(ne[oe][ge][fe][0]);if(!u||Ae>u){const Te=(ge+nu(te[oe][ge][fe][0]))/x*I,Ve=(oe+nu(te[oe][ge][fe][1]))/x*S,rt=Math.exp(te[oe][ge][fe][2])*this.config.anchors[fe].x/x*I,vt=Math.exp(te[oe][ge][fe][3])*this.config.anchors[fe].y/x*S,$t=Te-rt/2,Kt=Ve-vt/2,Dn={row:oe,col:ge,anchor:fe},{classScore:Tn,label:An}=this.withClassScores?await this.extractPredictedClass(U,Dn):{classScore:1,label:0};G.push({box:new tu($t,Kt,$t+rt,Kt+vt),score:Ae,classScore:Ae*Tn,label:An,...Dn})}}return O.dispose(),C.dispose(),U.dispose(),G}async extractPredictedClass(r,l){const{row:u,col:p,anchor:g}=l,f=await r.array();return Array(this.config.classes.length).fill(0).map((I,S)=>f[u][p][g][S]).map((I,S)=>({classScore:I,label:S})).reduce((I,S)=>I.classScore>S.classScore?I:S)}}Vc.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class Ou extends Vc{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:zE,classes:["face"]},r?{anchors:VE,meanRgb:YE}:{anchors:GE,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 Ht(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?qE:HE}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function cZ(r,l=!0){const u=new Ou(l);return u.extractWeights(r),u}class YI extends Lr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class wi{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const HI=Ke(Xe());async function ca(r,l,u,p,g=({alignedRect:f})=>f){const f=r.map(x=>oa(x)?g(x):x.detection),I=p||(l instanceof HI.Tensor?await kc(l,f):await Dc(l,f)),S=await u(I);return I.forEach(x=>x instanceof HI.Tensor&&x.dispose()),S}async function Yc(r,l,u,p,g){return ca([r],l,async f=>u(f[0]),p,g)}const XE=.4,JE=[new Je(1.603231,2.094468),new Je(6.041143,7.080126),new Je(2.882459,3.518061),new Je(4.266906,5.178857),new Je(9.041765,10.66308)],ZE=[117.001,114.697,97.404];class Eu extends Vc{constructor(){const r={withSeparableConvs:!0,iouThreshold:XE,classes:["face"],anchors:JE,meanRgb:ZE,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 Ht(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const pt={ssdMobilenetv1:new zc,tinyFaceDetector:new Eu,tinyYolov2:new Ou,faceLandmark68Net:new Au,faceLandmark68TinyNet:new BI,faceRecognitionNet:new Nu,faceExpressionNet:new FI,ageGenderNet:new UI},QE=(r,l)=>pt.ssdMobilenetv1.locateFaces(r,l),lZ=(r,l)=>pt.tinyFaceDetector.locateFaces(r,l),hZ=(r,l)=>pt.tinyYolov2.locateFaces(r,l),eD=r=>pt.faceLandmark68Net.detectLandmarks(r),uZ=r=>pt.faceLandmark68TinyNet.detectLandmarks(r),dZ=r=>pt.faceRecognitionNet.computeFaceDescriptor(r),pZ=r=>pt.faceExpressionNet.predictExpressions(r),mZ=r=>pt.ageGenderNet.predictAgeAndGender(r),tD=r=>pt.ssdMobilenetv1.load(r),fZ=r=>pt.tinyFaceDetector.load(r),gZ=r=>pt.tinyYolov2.load(r),yZ=r=>pt.faceLandmark68Net.load(r),bZ=r=>pt.faceLandmark68TinyNet.load(r),wZ=r=>pt.faceRecognitionNet.load(r),LZ=r=>pt.faceExpressionNet.load(r),SZ=r=>pt.ageGenderNet.load(r),IZ=tD,xZ=QE,TZ=eD;class nD extends wi{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class Fu extends nD{async run(){const r=await this.parentTask,l=await ca(r,this.input,async u=>await Promise.all(u.map(p=>pt.faceExpressionNet.predictExpressions(p))),this.extractedFaces);return r.map((u,p)=>Jf(u,l[p]))}withAgeAndGender(){return new Du(this,this.input)}}class _u extends nD{async run(){const r=await this.parentTask;if(!r)return;const l=await Yc(r,this.input,u=>pt.faceExpressionNet.predictExpressions(u),this.extractedFaces);return Jf(r,l)}withAgeAndGender(){return new ku(this,this.input)}}class jc extends Fu{withAgeAndGender(){return new Hc(this,this.input)}withFaceDescriptors(){return new la(this,this.input)}}class Kc extends _u{withAgeAndGender(){return new qc(this,this.input)}withFaceDescriptor(){return new ha(this,this.input)}}class sD extends wi{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class Du extends sD{async run(){const r=await this.parentTask,l=await ca(r,this.input,async u=>await Promise.all(u.map(p=>pt.ageGenderNet.predictAgeAndGender(p))),this.extractedFaces);return r.map((u,p)=>{const{age:g,gender:f,genderProbability:I}=l[p];return ng(sg(u,f,I),g)})}withFaceExpressions(){return new Fu(this,this.input)}}class ku extends sD{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:u,genderProbability:p}=await Yc(r,this.input,g=>pt.ageGenderNet.predictAgeAndGender(g),this.extractedFaces);return ng(sg(r,u,p),l)}withFaceExpressions(){return new _u(this,this.input)}}class Hc extends Du{withFaceExpressions(){return new jc(this,this.input)}withFaceDescriptors(){return new la(this,this.input)}}class qc extends ku{withFaceExpressions(){return new Kc(this,this.input)}withFaceDescriptor(){return new ha(this,this.input)}}class qI extends wi{constructor(r,l){super();this.parentTask=r;this.input=l}}class la extends qI{async run(){const r=await this.parentTask,l=await ca(r,this.input,u=>Promise.all(u.map(p=>pt.faceRecognitionNet.computeFaceDescriptor(p))),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return l.map((u,p)=>tg(r[p],u))}withFaceExpressions(){return new jc(this,this.input)}withAgeAndGender(){return new Hc(this,this.input)}}class ha extends qI{async run(){const r=await this.parentTask;if(!r)return;const l=await Yc(r,this.input,u=>pt.faceRecognitionNet.computeFaceDescriptor(u),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return tg(r,l)}withFaceExpressions(){return new Kc(this,this.input)}withAgeAndGender(){return new qc(this,this.input)}}const Wu=Ke(Xe());class jI extends wi{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=u}get landmarkNet(){return this.useTinyLandmarkNet?pt.faceLandmark68TinyNet:pt.faceLandmark68Net}}class KI extends jI{async run(){const r=await this.parentTask,l=r.map(g=>g.detection),u=this.input instanceof Wu.Tensor?await kc(this.input,l):await Dc(this.input,l),p=await Promise.all(u.map(g=>this.landmarkNet.detectLandmarks(g)));return u.forEach(g=>g instanceof Wu.Tensor&&g.dispose()),r.map((g,f)=>Mc(g,p[f]))}withFaceExpressions(){return new jc(this,this.input)}withAgeAndGender(){return new Hc(this,this.input)}withFaceDescriptors(){return new la(this,this.input)}}class XI extends jI{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,u=this.input instanceof Wu.Tensor?await kc(this.input,[l]):await Dc(this.input,[l]),p=await this.landmarkNet.detectLandmarks(u[0]);return u.forEach(g=>g instanceof Wu.Tensor&&g.dispose()),Mc(r,p)}withFaceExpressions(){return new Kc(this,this.input)}withAgeAndGender(){return new qc(this,this.input)}withFaceDescriptor(){return new ha(this,this.input)}}class JI extends wi{constructor(r,l=new yi){super();this.input=r;this.options=l}}class rg extends JI{async run(){const{input:r,options:l}=this,u=l instanceof YI?p=>pt.tinyFaceDetector.locateFaces(p,l):l instanceof yi?p=>pt.ssdMobilenetv1.locateFaces(p,l):l instanceof Lr?p=>pt.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=>Yo({},u)))})}withFaceLandmarks(r=!1){return new KI(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new Fu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Du(this.runAndExtendWithFaceDetections(),this.input)}}class ZI extends JI{async run(){const r=await new rg(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?Yo({},l):void 0)})}withFaceLandmarks(r=!1){return new XI(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new _u(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ku(this.runAndExtendWithFaceDetection(),this.input)}}function AZ(r,l=new yi){return new ZI(r,l)}function og(r,l=new yi){return new rg(r,l)}async function iD(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await og(r,new yi(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function vZ(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await og(r,new Lr(l)).withFaceLandmarks().withFaceDescriptors()}const NZ=iD;function QI(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((g,f)=>g-p[f]).reduce((g,f)=>g+Math.pow(f,2),0))}class rD{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 g=()=>`person ${p++}`;this._labeledDescriptors=u.map(f=>{if(f instanceof Vo)return f;if(f instanceof Float32Array)return new Vo(g(),[f]);if(f.descriptor&&f.descriptor instanceof Float32Array)return new Vo(g(),[f.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=>QI(u,r)).reduce((u,p)=>u+p,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:u})=>new mm(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 mm("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(u=>Vo.fromJSON(u));return new rD(l,r.distanceThreshold)}}function CZ(r){const l=new Eu;return l.extractWeights(r),l}function oD(r,l){const{width:u,height:p}=new us(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(g=>oD(g,{width:u,height:p}));if(oa(r)){const g=r.detection.forSize(u,p),f=r.unshiftedLandmarks.forSize(g.box.width,g.box.height);return Mc(Yo(r,g),f)}return Ui(r)?Yo(r,r.detection.forSize(u,p)):r instanceof Gs||r instanceof Ht?r.forSize(u,p):r}var aD="0.8.3";const RZ=Ke(Xe()),OZ=typeof process!="undefined",EZ=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",DZ={faceapi:aD,node:OZ,browser:EZ};export{UI as AgeGenderNet,tu as BoundingBox,Ct as Box,wi as ComposableTask,la as ComputeAllFaceDescriptorsTask,qI as ComputeFaceDescriptorsTaskBase,ha as ComputeSingleFaceDescriptorTask,KI as DetectAllFaceLandmarksTask,rg as DetectAllFacesTask,jI as DetectFaceLandmarksTaskBase,JI as DetectFacesTaskBase,XI as DetectSingleFaceLandmarksTask,ZI as DetectSingleFaceTask,us as Dimensions,kI as FACE_EXPRESSION_LABELS,Ht as FaceDetection,rZ as FaceDetectionNet,FI as FaceExpressionNet,ra as FaceExpressions,Au as FaceLandmark68Net,BI as FaceLandmark68TinyNet,V9 as FaceLandmarkNet,Gs as FaceLandmarks,YX as FaceLandmarks5,iu as FaceLandmarks68,mm as FaceMatch,rD as FaceMatcher,Nu as FaceRecognitionNet,gr as Gender,fm as LabeledBox,Vo as LabeledFaceDescriptors,to as NetInput,En as NeuralNetwork,gc as ObjectDetection,Je as Point,HX as PredictedBox,su as Rect,zc as SsdMobilenetv1,yi as SsdMobilenetv1Options,Eu as TinyFaceDetector,YI as TinyFaceDetectorOptions,Ou as TinyYolov2,Lr as TinyYolov2Options,VI as TinyYolov2SizeType,NZ as allFaces,iD as allFacesSsdMobilenetv1,vZ as allFacesTinyYolov2,AS as awaitMediaLoaded,vS as bufferToImage,dZ as computeFaceDescriptor,bc as createCanvas,ou as createCanvasFromMedia,iZ as createFaceDetectionNet,q9 as createFaceRecognitionNet,PE as createSsdMobilenetv1,CZ as createTinyFaceDetector,cZ as createTinyYolov2,og as detectAllFaces,eD as detectFaceLandmarks,uZ as detectFaceLandmarksTiny,TZ as detectLandmarks,AZ as detectSingleFace,WI as draw,gt as env,QI as euclideanDistance,ng as extendWithAge,tg as extendWithFaceDescriptor,Yo as extendWithFaceDetection,Jf as extendWithFaceExpressions,Mc as extendWithFaceLandmarks,sg as extendWithGender,kc as extractFaceTensors,Dc as extractFaces,W9 as fetchImage,OI as fetchJson,$9 as fetchNetWeights,sa as fetchOrThrow,Jn as getContext2dOrThrow,qo as getMediaDimensions,NS as imageTensorToCanvas,RI as imageToSquare,PX as inverseSigmoid,mS as iou,wm as isMediaElement,ru as isMediaLoaded,j9 as isWithAge,Ui as isWithFaceDetection,_I as isWithFaceExpressions,oa as isWithFaceLandmarks,K9 as isWithGender,SZ as loadAgeGenderModel,IZ as loadFaceDetectionModel,LZ as loadFaceExpressionModel,yZ as loadFaceLandmarkModel,bZ as loadFaceLandmarkTinyModel,wZ as loadFaceRecognitionModel,tD as loadSsdMobilenetv1Model,fZ as loadTinyFaceDetectorModel,gZ as loadTinyYolov2Model,EI as loadWeightMap,xZ as locateFaces,U9 as matchDimensions,fS as minBbox,pt as nets,gS as nonMaxSuppression,di as normalize,yS as padToSquare,mZ as predictAgeAndGender,pZ as recognizeFaceExpressions,oD as resizeResults,Ho as resolveInput,MX as shuffleArray,nu as sigmoid,QE as ssdMobilenetv1,RZ as tf,lZ as tinyFaceDetector,hZ as tinyYolov2,Rt as toNetInput,hS as utils,zI as validateConfig,DZ 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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