face-api/dist/face-api.js

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Int32Array(n));if(t==="bool")return ys(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function Yn(){return C().platform.now()}function Wy(e){e.forEach(t=>{k(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function Vx(e,t){return C().platform.fetch(e,t)}function $y(e,t="utf-8"){return t=t||"utf-8",C().platform.encode(e,t)}function zl(e,t="utf-8"){return t=t||"utf-8",C().platform.decode(e,t)}function ei(e,t,n){if(t===0)return 0;if(t===1)return e[0];let s=e[e.length-1];for(let i=0;i<e.length-1;++i)s+=n[i]*e[i];return s}function va(e,t,n){if(t===0)return[];if(t===1)return[e];const s=new Array(t);for(let i=0;i<s.length-1;++i)s[i]=Math.floor(e/n[i]),e-=s[i]*n[i];return s[s.length-1]=e,s}var nk=Object.freeze({__proto__:null,shuffle:Ry,clamp:Pl,nearestLargerEven:Oy,sum:$x,randUniform:XD,distSquared:JD,assert:k,assertShapesMatch:pt,assertNonNull:yo,flatten:qi,sizeFromShape:we,isScalarShape:ZD,arraysEqual:ot,isInt:Bt,tanh:QD,sizeToSquarishShape:pd,createShuffledIndices:ek,rightPad:bo,repeatedTry:Ey,inferFromImplicitShape:md,parseAxisParam:gt,squeezeShape:Ir,getTypedArrayFromDType:bn,getArrayFromDType:wo,checkConversionForErrors:Ux,isValidDtype:Bx,hasEncodingLoss:Dy,isTypedArray:wn,bytesPerElement:ky,bytesFromStringArray:Mx,isString:xr,isBoolean:Px,isNumber:fd,inferDtype:Ta,isFunction:Tr,nearestDivisor:gd,computeStrides:Ot,createScalarValue:zx,toTypedArray:Ar,toNestedArray:ys,makeOnesTypedArray:Fy,makeZerosTypedArray:Aa,makeZerosNestedTypedArray:_y,now:Yn,assertNonNegativeIntegerDimensions:Wy,fetch:Vx,encodeString:$y,decodeString:zl,locToIndex:ei,indexToLoc:va});class sk{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new rk)}profileKernel(e,t,n){let 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Error(`Cannot compute gradient: gradient function not found for ${o.kernelName}.`);const c=o.gradient(a);for(const h in o.inputs){if(!(h in c))throw new Error(`Cannot backprop through input ${h}. 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To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=Ii().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>zl(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. 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Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${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 sk(this.backendInstance),!0}setupRegisteredKernels(){const e=ud(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=ud(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 ql.nextTensorId++}nextVariableId(){return ql.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,al,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=vy(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=Ny(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"&&xr(e[0])&&(i=e.map(c=>$y(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=Mx(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 Hl(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*ky(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 Hl||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=Ny(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=h=>(h=h.map((d,m)=>{if(d==null){const y=n[m],b=Aa(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=ji(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=ok(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?gk(i.shape):n,ak(a,o,h=>this.tidy(h),yk);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(Tr(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(Tr(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=Yn(),n=await this.backend.time(e);return n.wallMs=Yn()-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 Xx;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}}ql.nextTensorId=0,ql.nextVariableId=0;function gk(e){const t=Fy(we(e),"float32");return V.makeTensor(t,e,"float32")}function Jx(){const e=ne();if(e._tfengine==null){const t=new S(e);e._tfengine=new ql(t)}return U(e._tfengine.ENV),hk(()=>e._tfengine),e._tfengine}const V=Jx();function yk(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 bk(){return typeof navigator!="undefined"&&navigator!=null}function Zx(){if(bk()){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 Gy(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var wk=Object.freeze({__proto__:null,isMobile:Zx,isBrowser:Gy});const Ki=C();Ki.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.")}),Ki.registerFlag("IS_BROWSER",()=>Gy()),Ki.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined"),Ki.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)),Ki.registerFlag("PROD",()=>!1),Ki.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Ki.getBool("DEBUG")),Ki.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0),Ki.registerFlag("IS_TEST",()=>!1);function xi(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")&&Qx(e,s,[]),s}function Qx(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 ${e.length} elements`);const s=t.slice(1);for(let i=0;i<e.length;++i)Qx(e[i],s,n.concat(i))}function eT(e,t,n,s){if(e==null)return;if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}function W(e,t,n,s="numeric"){if(e instanceof Q)return eT(s,e.dtype,t,n),e;let i=Ta(e);if(i!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(i=s),eT(s,i,t,n),e==null||!wn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){const h=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${h}'`)}const o=xi(e,i);!wn(e)&&!Array.isArray(e)&&(e=[e]);const a=!0,c=i!=="string"?Ar(e,i):qi(e,[],a);return V.makeTensor(c,o,i)}function jl(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 tT="__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+tT;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 Lk(e,t){const n=W(e,"real","complex"),s=W(t,"imag","complex");pt(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);const i=a=>a.complex(n,s),o={real:n,imag:s};return V.runKernelFunc(i,o,null,Ug)}const Ti=P({complex_:Lk});function Nr(e,t,n,s){if(s==null&&(s=Ta(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!wn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Wy(t);const i=we(t),o=we(n);k(i===o,()=>`Based on the provided shape, [${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 (${t}). `)}}return!wn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?Ar(e,s):qi(e,[],!0),V.makeTensor(e,t,s)}function en(e,t,n){const s=xi(e,n);return Nr(e,t,s,n)}const Vy={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};const Ld=4;async function Yy(e,t){const n=[],s=[],i=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);for(let a=0;a<i.length;++a){const c=i[a],h=Array.isArray(e)?e[a].tensor:e[c];if(h.dtype!=="float32"&&h.dtype!=="int32"&&h.dtype!=="bool"&&h.dtype!=="string"&&h.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${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)+Ld*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+=Ld,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:Sk(o),specs:n}}function Sd(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=Vy[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=Nk()),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}': ${c}`);i+=d*b}else if(c==="string"){const y=we(o.shape);m=[];for(let b=0;b<y;b++){const w=new Uint32Array(e.slice(i,i+Ld))[0];i+=Ld;const L=new Uint8Array(e.slice(i,i+w));m.push(L),i+=w}}else{const y=Vy[c],b=e.slice(i,i+d*y);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),L=new Float32Array(m.length/2);for(let N=0;N<w.length;N++)w[N]=m[N*2],L[N]=m[N*2+1];const T=en(w,h,"float32"),A=en(L,h,"float32");n[a]=Ti(T,A)}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*y}c!=="complex64"&&(n[a]=en(m,h,c))}return n}function Sk(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 Hy=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function nT(e){return Hy?Buffer.byteLength(e):new Blob([e]).size}function Ik(e){if(Hy)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 xk(e){if(Hy){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 Id(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 sT(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 Kl(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:nT(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:nT(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function Tk(){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 Ak(){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 vk(){const e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function Nk(){const e=Tk(),t=Ak(),n=vk();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 Ck=e=>Xt.registerSaveRouter(e),Rk=e=>Xt.registerLoadRouter(e),qy=e=>Xt.getSaveHandlers(e),jy=(e,t)=>Xt.getLoadHandlers(e,t);const xd="tensorflowjs",Ky=1,Lo="models_store",Cr="model_info_store";async function _Z(){const e=Xy();return new Promise((t,n)=>{const s=e.deleteDatabase(xd);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function Xy(){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 Jy(e){const t=e.result;t.createObjectStore(Lo,{keyPath:"modelPath"}),t.createObjectStore(Cr,{keyPath:"modelPath"})}class So{constructor(e){if(this.indexedDB=Xy(),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(xd,Ky);i.onupgradeneeded=()=>Jy(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(Lo,"readonly"),c=a.objectStore(Lo),h=c.get(this.modelPath);h.onsuccess=()=>{if(h.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(h.result.modelArtifacts)},h.onerror=d=>(o.close(),s(h.error)),a.oncomplete=()=>o.close()}else{const a=Kl(t),c=o.transaction(Cr,"readwrite");let h=c.objectStore(Cr);const d=h.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;d.onsuccess=()=>{m=o.transaction(Lo,"readwrite");const y=m.objectStore(Lo),b=y.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{h=c.objectStore(Cr);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)})}}So.URL_SCHEME="indexeddb://";const iT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(So.URL_SCHEME))?Ok(e.slice(So.URL_SCHEME.length)):null;Xt.registerSaveRouter(iT),Xt.registerLoadRouter(iT);function Ok(e){return new So(e)}function Ek(e){return e.startsWith(So.URL_SCHEME)?e.slice(So.URL_SCHEME.length):e}class Dk{constructor(){this.indexedDB=Xy()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(xd,Ky);n.onupgradeneeded=()=>Jy(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(Cr,"readonly"),o=i.objectStore(Cr),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=Ek(e),new Promise((t,n)=>{const s=this.indexedDB.open(xd,Ky);s.onupgradeneeded=()=>Jy(s),s.onsuccess=()=>{const i=s.result,o=i.transaction(Cr,"readwrite"),a=o.objectStore(Cr),c=a.get(e);let h;c.onsuccess=()=>{if(c.result==null)return i.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const d=a.delete(e),m=()=>{h=i.transaction(Lo,"readwrite");const y=h.objectStore(Lo),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 Ai="/",Io="tensorflowjs_models",rT="info",kk="model_topology",Fk="weight_specs",_k="weight_data",Wk="model_metadata";function WZ(){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=Io+Ai;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=aT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function oT(e){return{info:[Io,e,rT].join(Ai),topology:[Io,e,kk].join(Ai),weightSpecs:[Io,e,Fk].join(Ai),weightData:[Io,e,_k].join(Ai),modelMetadata:[Io,e,Wk].join(Ai)}}function aT(e){const t=e.split(Ai);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ai)}function $k(e){return e.startsWith(xo.URL_SCHEME)?e.slice(xo.URL_SCHEME.length):e}class xo{constructor(e){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=oT(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=Kl(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,Ik(e.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,userDefinedMetadata:e.userDefinedMetadata})),{modelArtifactsInfo:s}}catch(i){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=xk(o),t}}xo.URL_SCHEME="localstorage://";const cT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(xo.URL_SCHEME))?Uk(e.slice(xo.URL_SCHEME.length)):null;Xt.registerSaveRouter(cT),Xt.registerLoadRouter(cT);function Uk(e){return new xo(e)}class Bk{constructor(){k(C().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),k(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=Io+Ai,n=Ai+rT;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=aT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=$k(e);const t=oT(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);const n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}}const Ca="://";class bs{constructor(){this.managers={}}static getInstance(){return bs.instance==null&&(bs.instance=new bs),bs.instance}static registerManager(e,t){k(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Ca)&&(e=e.slice(0,e.indexOf(Ca))),k(e.length>0,()=>"scheme must not be an empty string.");const n=bs.getInstance();k(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${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 Td(e){if(e.indexOf(Ca)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${bs.getSchemes().join(",")}`);return{scheme:e.split(Ca)[0],path:e.split(Ca)[1]}}async function lT(e,t,n=!1){k(e!==t,()=>`Old path and new path are the same: '${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=Td(e).scheme,h=Td(e).path,d=c===Td(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 Mk(){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+Ca+i;t[o]=s[i]}}return t}async function Pk(e){const t=Td(e),n=bs.getManager(t.scheme);return n.removeModel(t.path)}async function zk(e,t){const n=!1;return lT(e,t,n)}async function Gk(e,t){const n=!0;return lT(e,t,n)}class Vk{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 Vk);try{bs.registerManager(xo.URL_SCHEME,new Bk)}catch(e){}try{bs.registerManager(So.URL_SCHEME,new Dk)}catch(e){}}const Yk={importFetch:()=>RC()};let Ra;function $Z(){Ra=null}function UZ(e){Ra=e}function BZ(){return Ra}class Hk{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return C().global.fetch!=null?C().global.fetch(e,t):(Ra==null&&(Ra=Yk.importFetch()),Ra(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 Hk);function Ze(e,t="float32",n){return t=t||"float32",Wy(e),new vr(e,t,n)}function qk(e,t){const n=W(e,"x","cast");if(!Bx(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,al,i)}const ve=P({cast_:qk});function jk(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,yl)}const Rr=P({clone_:jk});function hT(e,t=!1){console.log(e.toString(t))}Jx();const Kk={buffer:Ze,cast:ve,clone:Rr,print:hT};uk(Kk);const Xk="model",Jk=".json",Zk=".weights.bin";function uT(e){return new Promise(t=>setTimeout(t)).then(e)}class Oa{constructor(e){if(!C().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Oa.URL_SCHEME)&&(e=e.slice(Oa.URL_SCHEME.length)),(e==null||e.length===0)&&(e=Xk),this.modelTopologyFileName=e+Jk,this.weightDataFileName=e+Zk}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 uT(()=>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 uT(()=>a.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Kl(e)}}}}Oa.URL_SCHEME="downloads://";class Qk{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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Labels and predictions should have the same number of elements.`),k(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);const o=To(ve(s,"int32"),n),a=To(ve(i,"int32"),n),c=Me(o);return ve(at(c,a),"int32")}const fF=P({confusionMatrix_:mF});var gF=Object.freeze({__proto__:null,confusionMatrix:fF});function yT(e,t,n){if(yo(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");const s=xi(e,n);if(s.length!==3&&s.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Nr(e,t,s,n)}let Ea;function yF(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(e==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,s=!1,i=!1,o=!1,a=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)s=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)i=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)o=!0;else if(e.getContext!=null)a=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(i){const w=2;if(i&&e.readyState<w)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}const c=vy(ld,V.backendName);if(c!=null){const w={pixels:e},L={numChannels:t};return V.runKernel(ld,w,L)}const[h,d]=i?[e.videoWidth,e.videoHeight]:[e.width,e.height];let m;a?m=e.getContext("2d").getImageData(0,0,h,d).data:s||n?m=e.data:(o||i)&&(Ea==null&&(Ea=document.createElement("canvas").getContext("2d")),Ea.canvas.width=h,Ea.canvas.height=d,Ea.drawImage(e,0,0,h,d),m=Ea.getImageData(0,0,h,d).data);let y;if(t===4)y=new Int32Array(m);else{const w=h*d;y=new Int32Array(w*t);for(let L=0;L<w;L++)for(let T=0;T<t;++T)y[L*t+T]=m[L*4+T]}const b=[d,h,t];return yT(y,b,"int32")}async function bF(e,t){let n=W(e,"img","toPixels");if(!(e instanceof Q)){const d=n;n=ve(d,"int32"),d.dispose()}if(n.rank!==2&&n.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);const[s,i]=n.shape.slice(0,2),o=n.rank===2?1:n.shape[2];if(o>4||o===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${o}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);const a=await n.data(),c=n.dtype==="float32"?255:1,h=new Uint8ClampedArray(i*s*4);for(let d=0;d<s*i;++d){const m=[0,0,0,255];for(let b=0;b<o;b++){const w=a[d*o+b];if(n.dtype==="float32"){if(w<0||w>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${w}.`)}else if(n.dtype==="int32"&&(w<0||w>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${w}.`);o===1?(m[0]=w*c,m[1]=w*c,m[2]=w*c):m[b]=w*c}const y=d*4;h[y+0]=Math.round(m[0]),h[y+1]=Math.round(m[1]),h[y+2]=Math.round(m[2]),h[y+3]=Math.round(m[3])}if(t!=null){t.width=i,t.height=s;const d=t.getContext("2d"),m=new ImageData(h,i,s);d.putImageData(m,0,0)}return n!==e&&n.dispose(),h}const bT=P({fromPixels_:yF});var wF=Object.freeze({__proto__:null,toPixels:bF,fromPixels:bT});function vd(e,t){if(e.rank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${e.rank}.`);if(t.rank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[t.rank-1]>e.rank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[t.rank-1]} vs. ${e.rank}`);if(e.size===0)throw new Error(`Requested more than 0 entries, but input is empty. 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sh=P({batchToSpaceND_:y_});function b_(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 w_(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=b_(a),b=(A,N)=>(N([y,c,h,d]),A.batchNorm(y,Wd(c),Wd(h),Wd(m),Wd(d),o)),w={x:y,scale:d,offset:m,mean:c,variance:h},L={varianceEpsilon:o},T=V.runKernelFunc(b,w,null,gl,L);return K(T,a.shape)}function Wd(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}.`),No(a,c,h,m,d,o)}const WT=P({batchNorm3d_:S_});function I_(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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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 wB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY){const o=W(e,"boxes","nonMaxSuppression"),a=W(t,"scores","nonMaxSuppression"),c=Ya(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,iy,h)}const LB=P({nonMaxSuppression_:wB});function SB(e,t,n){const s=IB(e,t,n),i=s<0?-(s+1):s;e.splice(i,0,t)}function IB(e,t,n){return TB(e,t,n||xB)}function xB(e,t){return e>t?1:e<t?-1:0}function TB(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 up(e,t,n,s,i){return tw(e,t,n,s,i,0).selectedIndices}function dp(e,t,n,s,i,o){return tw(e,t,n,s,i,0,!1,o,!0)}function pp(e,t,n,s,i,o){return tw(e,t,n,s,i,o,!0)}function tw(e,t,n,s,i,o,a=!1,c=!1,h=!1){const d=[];for(let A=0;A<t.length;A++)t[A]>i&&d.push({score:t[A],boxIndex:A,suppressBeginIndex:0});d.sort(CA);const m=o>0?-.5/o:0,y=[],b=[];for(;y.length<n&&d.length>0;){const A=d.pop(),{score:N,boxIndex:E,suppressBeginIndex:D}=A;if(N<i)break;let F=!1;for(let _=y.length-1;_>=D;--_){const B=AB(e,E,y[_]);if(B>=s){F=!0;break}if(A.score=A.score*vB(s,m,B),A.score<=i)break}A.suppressBeginIndex=y.length,F||(A.score===N?(y.push(E),b.push(A.score)):A.score>i&&SB(d,A,CA))}const w=y.length,L=n-w;c&&L>0&&(y.push(...new Array(L).fill(0)),b.push(...new Array(L).fill(0)));const T={selectedIndices:ss(y,"int32")};return a&&(T.selectedScores=ss(b,"float32")),h&&(T.validOutputs=Ne(w,"int32")),T}function AB(e,t,n){const 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 vB(e,t,n){const s=Math.exp(t*n*n);return n<=e?s:0}function CA(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function NB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY){const o=W(e,"boxes","nonMaxSuppressionAsync"),a=W(t,"scores","nonMaxSuppressionAsync"),c=Ya(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=up(d,m,n,s,i);return o!==e&&o.dispose(),a!==t&&a.dispose(),y}const CB=NB;function RB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=0){const a=W(e,"boxes","nonMaxSuppression"),c=W(t,"scores","nonMaxSuppression"),h=Ya(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(sd,d,m);return{selectedIndices:y[0],selectedScores:y[1]}}const OB=P({nonMaxSuppressionWithScore_:RB});async function EB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=0){const a=W(e,"boxes","nonMaxSuppressionAsync"),c=W(t,"scores","nonMaxSuppressionAsync"),h=Ya(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=pp(m,y,n,s,i,o);return a!==e&&a.dispose(),c!==t&&c.dispose(),b}const DB=EB;function kB(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=!1){const a=W(e,"boxes","nonMaxSuppression"),c=W(t,"scores","nonMaxSuppression"),h=Ya(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(nd,b,w);return{selectedIndices:L[0],validOutputs:L[1]}}const FB=P({nonMaxSuppressionPadded_:kB});async function _B(e,t,n,s=.5,i=Number.NEGATIVE_INFINITY,o=!1){const a=W(e,"boxes","nonMaxSuppressionAsync"),c=W(t,"scores","nonMaxSuppressionAsync"),h=Ya(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=dp(b,w,d,m,y,o);return a!==e&&a.dispose(),c!==t&&c.dispose(),L}const WB=_B;function $B(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 ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=K(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const[a,c]=t,h=(b,w)=>(w([i]),b.resizeBilinear(i,a,c,n)),d={images:i},m={alignCorners:n,size:t},y=V.runKernelFunc(h,d,null,dy,m);return o?K(y,[y.shape[1],y.shape[2],y.shape[3]]):y}const RA=P({resizeBilinear_:$B});function UB(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,uy,d);return o?K(y,[y.shape[1],y.shape[2],y.shape[3]]):y}const OA=P({resizeNearestNeighbor_:UB});function BB(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 (${a}).`);t<0&&(t=o),n<0&&(n=a);const c=K(ph(0,o,1,"int32"),[-1,1]),h=ph(0,a,1,"int32"),d=Ce(c,h),m=$s(kr(d,Ne(+t,"int32")),Ji(d,Ne(-n,"int32"))),y=ct([o,a],s.dtype);return K(rs(Ei(K(s,[-1,o,a])).map(b=>_n(m,b,y))),i)}const MB=P({bandPart_:BB});function PB(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=Pd(n),o=Rr(e);const a=Wr([[1]],[1,1]);let c=Rr(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=rp(w),T=nt(o,[d,d],[1,1]),A=_n(Ss(T,0),Wr([[-1]]),Wr([[1]])),N=Ce(T,X(A,L)),E=_e(w,N);E.shape[0]===1?c=Rr(a):c=Pt([a,nt(E,[1,0],[E.shape[0]-1,E.shape[1]])],0);const D=zt(_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=Pt([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=Pt([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 VB=P({qr_:GB});(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 YB(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 Gt(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,ni(s.shape)),c=ve(Ue(Fr(a,Ne(0))),"float32");return _e(Ue(o),c)}}throw Error(`Unknown reduction: ${n}`)}const Zi=P({computeWeightedLoss_:YB});function HB(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")),pt(i.shape,o.shape,"Error in absoluteDifference: ");const c=sn(Ce(i,o));return Zi(c,a,s)}const qB=P({absoluteDifference_:HB});function jB(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")),pt(o.shape,a.shape,"Error in cosineDistance: ");const h=Ne(1),d=Ce(h,Ue(X(o,a),n,!0));return Zi(d,c,i)}const KB=P({cosineDistance_:jB});function XB(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")),pt(i.shape,o.shape,"Error in hingeLoss: ");const c=Ne(1);i=Ce(X(Ne(2),i),c);const h=Oi(Ce(c,X(i,o)));return Zi(h,a,s)}const JB=P({hingeLoss_:XB});function ZB(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")),pt(o.shape,a.shape,"Error in huberLoss: ");const h=Ne(s),d=sn(Ce(a,o)),m=Eo(d,h),y=Ce(d,m),b=be(X(Ne(.5),wt(m)),X(h,y));return Zi(b,c,i)}const QB=P({huberLoss_:ZB});function eM(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")),pt(o.shape,a.shape,"Error in logLoss: ");const h=Ne(1),d=Ne(s),m=zt(X(o,ns(be(a,d)))),y=X(Ce(h,o),ns(be(Ce(h,a),d))),b=Ce(m,y);return Zi(b,c,i)}const tM=P({logLoss_:eM});function nM(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")),pt(i.shape,o.shape,"Error in meanSquaredError: ");const c=yh(i,o);return Zi(c,a,s)}const sM=P({meanSquaredError_:nM});function iM(e,t){const n=W(e,"labels","sigmoidCrossEntropyWithLogits"),s=W(t,"logits","sigmoidCrossEntropyWithLogits");pt(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const i=Oi(s),o=X(s,n),a=Gd(Ls(zt(sn(s))));return be(Ce(i,o),a)}function rM(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")),pt(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=iM(o,a);return Zi(h,c,i)}const oM=P({sigmoidCrossEntropy_:rM});function aM(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 ${n}`);const s=Ci((i,o,a)=>{const c=!0,h=_b(o,[n],c),d=Ce(ve(o,"float32"),h);a([i,d]);const m=zt(X(d,i)),y=Ue(m,[n]),b=(w,L)=>{const[T,A]=L,N=On(w.shape,[n]);return[X(K(w,N),Ce(ve(T,"float32"),Ls(A))),X(K(w,N),Ce(Ls(A),ve(T,"float32")))]};return{value:y,gradFunc:b}});return s(e,t)}function cM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"onehotLabels","softmaxCrossEntropy");const a=W(t,"logits","softmaxCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","softmaxCrossEntropy")),pt(o.shape,a.shape,"Error in softmaxCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(o.shape[1]);o=be(X(o,Ce(m,d)),_e(d,y))}const h=aM(o,a);return Zi(h,c,i)}const lM=P({softmaxCrossEntropy_:cM});const hM={fft:fh,ifft:za,rfft:gh,irfft:np},uM={hammingWindow:cB,hannWindow:vA,frame:NA,stft:dB},$r={flipLeftRight:gB,resizeNearestNeighbor:OA,resizeBilinear:RA,rotateWithOffset:bB,cropAndResize:mB,nonMaxSuppression:LB,nonMaxSuppressionAsync:CB,nonMaxSuppressionWithScore:OB,nonMaxSuppressionWithScoreAsync:DB,nonMaxSuppressionPadded:FB,nonMaxSuppressionPaddedAsync:WB},DA={bandPart:MB,gramSchmidt:zB,qr:VB},dM={absoluteDifference:qB,computeWeightedLoss:Zi,cosineDistance:KB,hingeLoss:JB,huberLoss:QB,logLoss:tM,meanSquaredError:sM,sigmoidCrossEntropy:oM,softmaxCrossEntropy:lM};class Qi extends Ao{minimize(e,t=!1,n){const{value:s,grads:i}=this.computeGradients(e,n);if(n!=null){const o=n.map(a=>({name:a.name,tensor:i[a.name]}));this.applyGradients(o)}else this.applyGradients(i);return He(i),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Fb(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(Qi,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class wh extends Qi{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:ee(()=>Qe(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,h=this.accumulatedUpdates[s].variable;ee(()=>{const d=be(X(c,this.rho),X(wt(a),1-this.rho)),m=X(_e(Ln(be(h,this.epsilon)),Ln(be(c,this.epsilon))),a),y=be(X(h,this.rho),X(wt(m),1-this.rho));c.assign(d),h.assign(y);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(He(this.accumulatedGrads.map(e=>e.variable)),He(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}wh.className="Adadelta",me(wh);class Lh extends Qi{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:ee(()=>oh(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)}}Lh.className="Adagrad",me(Lh);class Sh extends Qi{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(si(this.beta1,this.iterations_+1)),this.accBeta2.assign(si(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)}}Sh.className="Adam",me(Sh);class Ih extends Qi{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=Ws(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)}}Ih.className="Adamax",me(Ih);class Ha extends Qi{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=Cn(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)}}Ha.className="SGD",me(Ha);class xh extends Ha{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:`${n}/momentum`,variable:ee(()=>Qe(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;ee(()=>{let c;const h=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(h,this.m))),i):c=be(X(this.c,h),i),o.assign(h),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&He(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}xh.className="Momentum",me(xh);class Th extends Qi{constructor(e,t=.9,n=0,s=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,s==null&&(this.epsilon=V.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${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)}}Th.className="RMSProp",me(Th);class $o{static sgd(e){return new Ha(e)}static momentum(e,t,n=!1){return new xh(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,i=!1){return new Th(e,t,n,s,i)}static adam(e=.001,t=.9,n=.999,s=null){return new Sh(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new wh(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,i=0){return new Ih(e,t,n,s,i)}static adagrad(e,t=.1){return new Lh(e,t)}}const Uo={sgd:$o.sgd,momentum:$o.momentum,adadelta:$o.adadelta,adagrad:$o.adagrad,rmsprop:$o.rmsprop,adamax:$o.adamax,adam:$o.adam};const pM=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function mp(){return new Promise(e=>pM(()=>e()))}function nw(e,t,n){const s=n*(typeof e=="number"?e:e[0]),i=t*(typeof e=="number"?e:e[1]);return[s,i]}function Ah(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 vh(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 Nh(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 sw(e,t){const n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function iw(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 fp=1.7580993408473768,gp=1.0507009873554805;const rw=.3275911,ow=.254829592,aw=-.284496736,cw=1.421413741,lw=-1.453152027,hw=1.061405429;function qa(...e){C().getBool("IS_TEST")||console.warn(...e)}function mM(...e){C().getBool("IS_TEST")||console.log(...e)}function er(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 kA(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 FA(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 _A(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 uw(e,t){const n=e[t*2],s=e[t*2+1];return{real:n,imag:s}}function WA(e,t,n,s){e[s*2]=t,e[s*2+1]=n}function $A(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 UA(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 BA(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(!Dy(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 ${t}`)}function MA(e,t){return V.makeTensorFromDataId(e.dataId,t,e.dtype)}function dw(e,t,n){const s=(t-e)/(n-1),i=Aa(n,"float32");i[0]=e;for(let o=1;o<i.length;o++)i[o]=i[o-1]+s;return ss(i,"float32")}var pw=Object.freeze({__proto__:null,slice_util:CT,segment_util:aW,castTensor:BA,reshapeTensor:MA,linspaceImpl:dw,upcastType:Nn,axesAreInnerMostDims:pb,combineLocations:FT,computeOutAndReduceShapes:Rn,expandShapeToKeepDim:On,assertAxesAreInnerMostDims:ts,getAxesPermutation:kn,getUndoAxesPermutation:Xl,getInnerMostAxes:ws,getBroadcastDims:Ro,getReductionAxes:on,assertAndGetBroadcastShape:tt,assertParamsConsistent:xb,computeOutShape:Er,computeDilation2DInfo:Fd,computePool2DInfo:Fn,computePool3DInfo:Ql,computeConv2DInfo:vi,computeConv3DInfo:eh,computeDefaultPad:Lb,tupleValuesAreOne:Or,eitherStridesOrDilationsAreOne:rn,convertConv2DDataFormat:th,getFusedDyActivation:op,getFusedBiasGradient:ap,applyActivation:cp,shouldFuse:lp,PARALLELIZE_THRESHOLD:Db,computeOptimalWindowSize:ah,getImageCenter:nw,getReshaped:Ah,getPermuted:vh,getReshapedPermuted:Nh,getSliceBeginCoords:sw,getSliceSize:iw,prepareAndValidate:vd,validateUpdateShape:nb,validateInput:sb,calculateShapes:Da,SELU_SCALEALPHA:fp,SELU_SCALE:gp,ERF_P:rw,ERF_A1:ow,ERF_A2:aw,ERF_A3:cw,ERF_A4:lw,ERF_A5:hw,warn:qa,log:mM,mergeRealAndImagArrays:er,splitRealAndImagArrays:kA,complexWithEvenIndex:FA,complexWithOddIndex:_A,getComplexWithIndex:uw,assignToTypedArray:WA,exponents:$A,exponent:UA,prepareSplitSize:tA});function 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Xn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Xn.className}}Tp.className="GlorotNormal",me(Tp);class Ap extends Xn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Xn.className}}Ap.className="HeNormal",me(Ap);class vp extends Xn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Xn.className}}vp.className="HeUniform",me(vp);class Np extends Xn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Xn.className}}Np.className="LeCunNormal",me(Np);class Cp extends Xn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Xn.className}}Cp.className="LeCunNormal",me(Cp);class _w extends Bs{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=Sp(n,0,1,"float32");let i=DA.gramSchmidt(s);return e[0]>e[1]&&(i=i.transpose()),X(this.gain,i)})}getConfig(){return{gain:this.gain,seed:this.seed}}}_w.className="Orthogonal",me(_w);const lv={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function hv(e,t={}){return Ch(e,Fs.getMap().classNameMap,t,"initializer")}function Yt(e){return yw(e)}function Ft(e){if(typeof e=="string"){const t=e in lv?lv[e]:e;if(t==="GlorotNormal")return new Tp;if(t==="GlorotUniform")return new xp;if(t==="HeNormal")return new Ap;if(t==="HeUniform")return new vp;if(t==="LeCunNormal")return new Np;if(t==="LeCunUniform")return new Cp;{const n={};return n.className=t,n.config={},hv(n)}}else return e instanceof Bs?e:hv(e)}function Pz(){return new Rw}function zz(){return new Ip}function Gz(e){return new Ow(e)}function Vz(e){return new Ew(e)}function Yz(e){return new Dw(e)}function Hz(e){return new kw(e)}function qz(e){return new Fw(e)}function jz(e){return new Xn(e)}function Kz(e){return new xp(e)}function Xz(e){return new Tp(e)}function Jz(e){return new Ap(e)}function Zz(e){return new vp(e)}function Qz(e){return new Np(e)}function e3(e){return new Cp(e)}function t3(e){return new _w(e)}var n3=Object.freeze({__proto__:null,zeros:Pz,ones:zz,constant:Gz,randomUniform:Vz,randomNormal:Yz,truncatedNormal:Hz,identity:qz,varianceScaling:jz,glorotUniform:Kz,glorotNormal:Xz,heNormal:Jz,heUniform:Zz,leCunNormal:Qz,leCunUniform:e3,orthogonal:t3});let s3=0;function uv(){return s3++}const Rp={};function Op(e=""){return e in Rp||(Rp[e]=0),Rp[e]+=1,e+Rp[e].toString()}function Ww(e){return Array.isArray(e)&&Array.isArray(e[0])}function Ep(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Ke(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 St(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new j(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Dp(e){let t=0;for(const n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((s,i)=>s*i);return t}const dv="Variable";class ai{constructor(e,t="float32",n=dv,s=!0,i=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=uv(),n=n==null?dv:n,this.originalName=nv(n),this.name=sv(this.originalName),this.trainable_=s,this.constraint=i,this.val=nA(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),i3(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function i3(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function aQ(e,t,n,s){return new ai(e,t,n,!0,s)}function cQ(e,t,n){return new ai(ct(e),t,n)}function lQ(e,t,n){return new ai(Qe(e),t,n)}function hQ(e,t,n){const s=ni(e);return new ai(s,t,n)}function uQ(e,t,n){const s=En(e);return new ai(s,t,n)}function dQ(e,t,n){return new ai(Pd(e),t,n)}function pQ(e,t,n,s,i,o="randomUniform"){return new ai(_o(e,t,n,s),s,o)}function mQ(e,t=0,n=1,s,i,o="truncatedNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new ze(`randomNormal does not support dType ${s}.`);return new ai(bh(e,t,n,s,i),s,o)}function fQ(e,t=0,n=1,s,i,o="randomNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new ze(`randomNormalVariable does not support dType ${s}.`);return new ai(Bb(e,t,n,s,i),s,o)}function gQ(e,t){return e.write(t)}function yQ(e,t){return e.write(be(e.read(),t))}function bQ(e,t){return e.write(Ce(e.read(),t))}function $w(e){return e.map(t=>t.read())}function Uw(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function wQ(e,t){const n=t.map(i=>i.read()),s=Fb(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 ci{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=uv(),o!=null&&(this.originalName=nv(o),this.name=sv(this.originalName)),this.rank=t.length}}let r3=0;class kp{constructor(e,t){this.callArgs=t,this.id=r3++,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 o3=0;class lt extends Ao{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=o3++,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=nr(n)+"_"+Op(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 ri(`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 Kn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Kn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new tr(`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 tr(`Layer ${this.name} is not connected, no input to return.`);return Kn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new tr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new tr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Kn(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=Rt(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=Rt(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=Rt(e);let s=!0;for(const o of n)if(!(o instanceof ci)){s=!1;break}let i=!0;for(const o of n)if(o instanceof ci){i=!1;break}if(s===i)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Po(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of Rt(e))o.push(a.shape);this.build(Kn(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=Rt(o),c=[];for(let h of a)n.indexOf(h)!==-1&&(h=h.clone()),c.push(h);if(o=Kn(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=a3(e),a=this.computeOutputShape(o);let c;const h=c3(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 ci(h,d,this,Rt(e),t,this.name,m)):c=new ci(h,a,this,Rt(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 tr(`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 tr(`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 ri(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Dp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return $w(e?this.trainableWeights:this.weights)}setWeights(e){ee(()=>{const t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;const n=[],s=$w(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])}Uw(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 ai(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=Rt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=Rt(e);t=Rt(t),n=Rt(n),s=Rt(s),i=Ep(i),o=Ep(o);const h=[],d=[],m=[];for(const y of c)h.push(y.sourceLayer),d.push(y.nodeIndex),m.push(y.tensorIndex);new kp({outboundLayer:this,inboundLayers:h,nodeIndices:d,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let y=0;y<t.length;y++)t[y].sourceLayer=this,t[y].nodeIndex=this.inboundNodes.length-1,t[y].tensorIndex=y}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 a3(e){e=Rt(e);const t=[];for(const n of e)t.push(n.shape);return Kn(t)}function c3(e){return"float32"}function pv(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=pv(a,c,h);for(const m of d)i.indexOf(m)===-1&&i.push(m)}return i}}}class Ja extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Op("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 ci(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new kp({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}}}Ja.className="InputLayer",me(Ja);function mv(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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n=[],s={};if(e.length===1){const i=Ev(e[0],t);n=i.sorted,s=i.recipientMap}else{const i=new Set;for(const o of e){const{sorted:a,recipientMap:c}=Ev(o,t);for(const h of a)i.has(h.name)||(n.push(h),i.add(h.name));for(const h in c)s[h]==null&&(s[h]=new Set),c[h].forEach(d=>s[h].add(d))}}return{sorted:n,recipientCounts:W3(s)}}function W3(e){const t={};for(const n in e)t[n]=e[n].size;return t}function Ev(e,t){const n=new Set,s=[],i={};for(const c of t.names())n.add(c);const o=[],a=[];for(o.push(e);o.length>0;){const c=o[o.length-1];if(n.has(c.name)){o.pop();continue}const h=a[a.length-1]===o.length-1;if(c.inputs.length===0||h)o.pop(),s.push(c),n.add(c.name),h&&a.pop();else{a.push(o.length-1);for(const d of c.inputs){if(i[d.name]==null&&(i[d.name]=new Set),i[d.name].add(c.name),n.has(d.name))continue;o.push(d)}}}return{sorted:s,recipientMap:i}}function $3(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let 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 Fi extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=Op(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],Ur(this.inputs).length!==this.inputs.length)throw new j(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(N=>N.name)}`);Ur(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(N=>N.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const N of this.outputs){const 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 Ja))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 ri(`The tensor ${N.name} at layer "${F.name}" is part of a cycle.`);if(E.indexOf($)!==-1)return;this.containerNodes.add(Fi.nodeKey(F,_)),F.id in o||(o[F.id]=Object.keys(o).length),D.indexOf($)===-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(bp);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 Fi&&this.internalContainerRefs.push(D),this.layers.push(D)}this.layersByDepth=b,w=Object.keys(y).map(N=>parseInt(N,10)).sort(bp);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 ri(`Graph disconnected: cannot obtain value for tensor ${F} at layer "${D.name}". The following previous layers were accessed without issue: ${T}`);for(const F of E.outputTensors)L.push(F);T.push(D.name)}}this.nodesByDepth=y;const A=this.layers.map(N=>N.name);for(const N of A){const E=A.filter(D=>D===N).length;if(E!==1)throw new ri(`The name "${N}" is used ${E} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new kp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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}`)}Uw(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${zp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=Hw(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ee(()=>{e=Rt(e);const n=new Go;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return $h(this.outputs,n,t)})}computeMask(e,t){return ee(()=>{e=Rt(e);let n;return t==null?n=Bo(null,e.length):n=Rt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=Ep(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],h=t[a],d=c.name+"_0_0";n[d]=h}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(bp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const h of c){const d=h.outboundLayer;if(this.inputLayers.map(L=>L.id).indexOf(d.id)!==-1)continue;const m=[];for(let L=0;L<h.inboundLayers.length;L++){const T=h.inboundLayers[L],A=h.nodeIndices[L],N=h.tensorIndices[L],E=`${T.name}_${A}_${N}`,D=n[E];m.push(D)}const y=d.computeOutputShape(Kn(m)),b=Ep(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 Kn(i)}runInternalGraph(e,t){t==null&&(t=Bo(null,e.length));const n={};for(let c=0;c<this.inputs.length;++c){const h=this.inputs[c],d=e[c],m=t[c];n[h.id]=[d,m]}const s=Object.keys(this.nodesByDepth).map(c=>parseInt(c,10)).sort(bp);for(const c of s){const h=this.nodesByDepth[c];for(const d of h){const m=d.outboundLayer,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=Rt(m.call(D,L)),E=Rt(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=Rt(m.call(T,L)),E=Rt(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 Fi?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=Fi.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=Fi.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=Fi.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=Fi.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=Fi.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=Fi.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(Kn(N),E)}function h(T){const A=T.name,N=li(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(;!mz(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 Dv(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: ${JSON.stringify(e)}`)}function kv(e,t){return Dv(e,t,"classWeight")}function kQ(e,t){return Dv(e,t,"sampleWeight")}async function Fv(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){const i=ee(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const c=1;return e.argMax(c)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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])}),ss(a,"float32")}else return null}function U3(e,t){return X(e,t)}const B3=32;function _v(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=Wv("input",e.inputNames,n),a=Wv("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 Wv(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 M3(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 P3(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 ${n.batchesPerEpoch}`),k(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{const i=n.validationData!=null;let o,a;if(i)if($v(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=M3(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=Lv(n.callbacks,n.yieldEvery),y=n.verbose==null?1:n.verbose,{callbackList:b,history:w}=Sv(m,y,n.epochs,null,null,z3(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). 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Use LayersModel.compile(modelCompileArgs).");const i=[];for(let o=0;o<this.feedOutputShapes.length;++o){const a=this.feedOutputShapes[o],c=this.feedLossFns[o];c===_p?i.push(a.slice(0,a.length-1).concat([1])):i.push(a)}if(e=Mv(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Mv(t,this.feedOutputNames,i,!1,"target"),j3(e,t,null),K3(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new j(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,i=!0,o){const[a,c]=this.standardizeUserDataXY(e,t,i,o);if(n!=null)throw new Error("sample weight is not supported yet.");let h=null;if(s!=null){const d=kv(s,this.outputNames);h=[];for(let m=0;m<d.length;++m)h.push(await Fv(c[m],null,d[m]))}return[a,c,h]}testLoop(e,t,n,s=0,i){return ee(()=>{const o=this.checkNumSamples(t,n,i,"steps"),a=[];if(s>0)throw new ze("Verbose mode is not implemented yet.");if(i!=null)throw new ze("steps mode in testLoop() is not implemented yet");{const c=Xw(o,n),h=ss(oi(0,o));for(let d=0;d<c.length;++d){const m=c[d][0],y=c[d][1],b=zo(h,m,y-m),w=Kw(t,b),L=e(w);if(d===0)for(let T=0;T<L.length;++T)a.push(Ne(0));for(let T=0;T<L.length;++T){const A=L[T];a[T]=be(a[T],X(y-m,A))}}for(let d=0;d<a.length;++d)a[d]=_e(a[d],o)}return a})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let i=s;if(KA(e,s)>1){const o=KA(e.slice(0,n),s);i+=`_${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 Go(m),b=$h(this.outputs,y,{training:!0});let w;for(let L=0;L<this.lossFunctions.length;++L){const T=this.lossFunctions[L];let A=T(s[L],b[L]);i[L]!=null&&(A=U3(A,i[L]));const N=Gt(A);t.push(N),L===0?w=A:w=be(w,A)}for(let L=0;L<this.metricsTensors.length;++L){let T;if(this.outputs.length>1&&L<this.outputs.length)T=t[L];else{const A=this.metricsTensors[L][0],N=this.metricsTensors[L][1];T=Gt(A(s[N],b[N]))}Cn(T),o.push(T)}return w=Gt(w),this.calculateLosses().forEach(L=>{w=be(w,L)}),w},c=this.collectedTrainableWeights.map(m=>m.read()),h=!0,d=this.optimizer_.minimize(a,h,c);return[d].concat(o)}}makeTestFunction(){this.testFunction=e=>ee(()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),o=[];for(let h=0;h<this.inputs.length;++h)o.push({key:this.inputs[h],value:s[h]});const a=new Go(o),c=$h(this.outputs,a);for(let h=0;h<this.lossFunctions.length;++h){const d=this.lossFunctions[h],m=Gt(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=Gt(d(i[m],c[m]));t.push(y)}return t})}async fit(e,t,n={}){return H3(this,e,t,n)}async fitDataset(e,t){return P3(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),Kn(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=Ed().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Ed().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=nr(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=>nr(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]=nr(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[nr(Mp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>nr(Mp(e)));{const e={};for(const t in this.metrics)e[t]=nr(Mp(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=Wh(e.optimizer_config),n=li(t);let s;if(typeof e.loss=="string")s=Mo(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>Mo(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=Mo(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>Mo(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=Mo(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const h=qy(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 '${e}'`);e=h[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await Yy(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:J3,generatedBy:`TensorFlow.js tfjs-layers v${zp}`,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=Id([n.data,d])}if(this.userDefinedMetadata!=null){const h=!0;Cv(this.userDefinedMetadata,this.name,h),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Cv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}ir.className="Model",me(ir);class zv extends ir{}zv.className="Functional",me(zv);async function Z3(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=Wh(n),i=li(s,t);if(e.weightsManifest!=null){const o=await mT(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 Q3(e,t){if(t==null&&(t={}),typeof e=="string"){const n=jy(e,t);if(n.length===0)n.push(Ad(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 eG(e,void 0,t)}async function eG(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=li(Wh(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}=tG(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 tG(e,t){const n=Sd(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 ec extends ir{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Op("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 ec||e instanceof ir;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=mv({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=pv(this.outputs[0])}this.inboundNodes=[],new kp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Bo(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 ir({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 ri("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 ri("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 ri("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 ri("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 ec))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const h=void 0,d=li(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}}}ec.className="Sequential",me(ec);function nG(e){return new ir(e)}function sG(e){return new ec(e)}function iG(e,t){return t==null&&(t={}),Q3(e,t)}function Gv(e){return mv(e)}function rG(e,t){Ms.registerCallbackConstructor(e,t)}class os extends Ao{getConfig(){return{}}}class Vv extends os{apply(e,t=1){return kz(e,t)}}Vv.className="elu",me(Vv);class Yv extends os{apply(e){return Jd(e)}}Yv.className="selu",me(Yv);class Hv extends os{apply(e){return Oi(e)}}Hv.className="relu",me(Hv);class qv extends os{apply(e){return ee(()=>Eo(6,Oi(e)))}}qv.className="relu6",me(qv);class jv extends os{apply(e){return e}}jv.className="linear",me(jv);class Kv extends os{apply(e){return Ni(e)}}Kv.className="sigmoid",me(Kv);class Xv extends os{apply(e){return _z(e)}}Xv.className="hardSigmoid",me(Xv);class Jv extends os{apply(e){return Ua(e)}}Jv.className="softplus",me(Jv);class Zv extends os{apply(e){return Fz(e)}}Zv.className="softsign",me(Zv);class Qv extends os{apply(e){return Fa(e)}}Qv.className="tanh",me(Qv);class Zw extends os{apply(e,t=-1){return Wo(e,t)}}Zw.className="softmax",me(Zw);class eN extends os{apply(e,t=-1){return Yd(e,t)}}eN.className="logSoftmax",me(eN);class tN extends os{apply(e,t=1){return ee(()=>Ni(e.mul(t)).mul(e))}}tN.className="swish",me(tN);function Gr(e){return e.getClassName()}function Qw(e,t={}){return Ch(e,Fs.getMap().classNameMap,t,"activation")}function Vr(e){if(e==null){const t={};return t.className="linear",t.config={},Qw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Qw(t)}else return e instanceof os?e:Qw(e)}function eL(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 nN extends Ao{}class Bh extends nN{constructor(e){super();eL(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,kh(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Bh.className="L1L2",me(Bh);function oG(e){return eL(e),new Bh({l1:e!=null?e.l1:null,l2:0})}function aG(e){return eL(e),new Bh({l2:e!=null?e.l2:null,l1:0})}const sN={l1l2:"L1L2"};function It(e){return yw(e)}function iN(e,t={}){return Ch(e,Fs.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){const t=e in sN?sN[e]:e,n={className:t,config:{}};return iN(n)}else return e instanceof nN?e:iN(e)}class tL extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ke(e);let n=Oi(e);return this.maxValue!=null&&(n=Hn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}tL.className="ReLU",me(tL);class nL 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=Ke(e);return zd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}nL.className="LeakyReLU",me(nL);class sL 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 ${e.sharedAxes}`)}build(e){e=St(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new mn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ke(e),dh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Yt(this.alphaInitializer),alphaRegularizer:It(this.alphaRegularizer),alphaConstraint:cn(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}sL.className="PReLU",me(sL);class iL extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${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=Ke(e);return Oo(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}iL.className="ELU",me(iL);class rL 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=Ke(e);return n.mul(Eh(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}rL.className="ThresholdedReLU",me(rL);class oL extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Zw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=Ke(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}}oL.className="Softmax",me(oL);function tc(e,t,n){if(typeof e=="number")return Bo(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(!Nz(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 hi(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 Gp(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Mr([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function aL(e,t){return ee(()=>(Vt(t),t==="channelsFirst"?Me(e,[0,2,3,1]):e))}function rN(e,t){return ee(()=>(Vt(t),t==="channelsFirst"?Me(e,[0,2,3,4,1]):e))}function oN(e,t,n,s=1,i="valid",o,a=1){return ee(()=>{if(o==null&&(o=ii()),Vt(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 ${t.shape.length} instead`);if(o==="channelsFirst"&&(e=Me(e,[0,2,1])),i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=$d(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=ki(c,n)),c})}function FQ(e,t,n=1,s="valid",i,o=1){return ee(()=>(Vt(i),oN(e,t,null,n,s,i,o)))}function _Q(e,t,n=[1,1],s="valid",i,o){return ee(()=>(Vt(i),cL(e,t,null,n,s,i,o)))}function cL(e,t,n,s=[1,1],i="valid",o,a,c=null){return ee(()=>{if(o==null&&(o=ii()),Vt(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=aL(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return h=ew({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 WQ(e,t,n=[1,1,1],s="valid",i,o){return ee(()=>(Vt(i),aN(e,t,null,n,s,i,o)))}function aN(e,t,n,s=[1,1,1],i="valid",o,a){return ee(()=>{if(o==null&&(o=ii()),Vt(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 ${e.rank}.`);let c=rN(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=vb(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=ki(c,n)),o==="channelsFirst"&&(c=Me(c,[0,4,1,2,3])),c})}class lL extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",lL.verifyArgs(t),this.rank=e,pn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=tc(t.kernelSize,e,"kernelSize"),this.strides=tc(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,Vt(this.dataFormat),this.activation=Vr(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=tc(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"&&!ww(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:Gr(this.activation),useBias:this.useBias,biasInitializer:Yt(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:cn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Mh extends lL{constructor(e,t){super(e,t);this.kernel=null,Mh.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=Ke(e);let n;const s=this.bias==null?null:this.bias.read(),i=JA(this.activation.getClassName());if(i!=null&&this.rank===2)n=cL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=oN(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=cL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=aN(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=hi(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:Yt(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 Ph extends Mh{constructor(e){super(2,e);Ph.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!ww(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)}.`)}}Ph.className="Conv2D",me(Ph);class Vp extends Mh{constructor(e){super(3,e);Vp.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)}.`)}}Vp.className="Conv3D",me(Vp);class hL extends Ph{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 ${this.padding}`)}build(e){if(e=St(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new mn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{let n=Ke(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=Gp(c,y,d,this.padding),L=Gp(h,b,m,this.padding),T=[i,w,L,this.filters];this.dataFormat!=="channelsLast"&&(n=Me(n,[0,2,3,1]));let A=Ud(n,this.kernel.read(),T,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Me(A,[0,3,1,2])),this.bias!=null&&(A=ki(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]=Gp(t[s],c,o,this.padding),t[i]=Gp(t[i],h,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}hL.className="Conv2DTranspose",me(hL);class cN extends Mh{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=Ke(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=Gb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ki(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=Yt(this.depthwiseInitializer),e.pointwiseInitializer=Yt(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=cn(this.depthwiseConstraint),e.pointwiseConstraint=cn(this.pointwiseConstraint),e}}cN.className="SeparableConv";class uL extends cN{constructor(e){super(2,e)}}uL.className="SeparableConv2D",me(uL);class Yp extends Mh{constructor(e){super(1,e);Yp.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"&&!ww(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)}.`)}}Yp.className="Conv1D",me(Yp);class dL 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=Ke(e),this.dataFormat==="channelsLast"){const n=Lp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Lp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=Lp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Lp(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}}dL.className="Cropping2D",me(dL);class pL 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=Ke(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}}pL.className="UpSampling2D",me(pL);function cG(e,t,n=[1,1],s="valid",i,o){return ee(()=>{i==null&&(i=ii()),Vt(i);let a=aL(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=Co(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}class mL extends lL{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=Ke(e);let n=cG(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ki(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=hi(t,this.kernelSize[0],this.padding,this.strides[0]),o=hi(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=Yt(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=cn(this.depthwiseRegularizer),e}}mL.className="DepthwiseConv2D",me(mL);function lN(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 hN(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 ${h}D.`);const d=[1,0].concat(oi(2,h));if(t=Me(t,d),o!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===h-1&&(i=qn(i,-1)),i=Me(i,d)),s&&(t=Is(t,0),i!=null&&(i=Is(i,0)));const m=[];let y,b=n;const w=t.shape[0],L=Ei(t);let T;i!=null&&(T=Ei(i));for(let N=0;N<w;++N){const E=L[N],D=ee(()=>e(E,b));if(i==null)y=D[0],b=D[1];else{const F=ee(()=>{const _=T[N],B=En(_).sub(_),$=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=rs(m,N)}return[y,A,b]})}class _i 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 jp({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 oi(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ww(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.");Ww(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 tr("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=>Cn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=lN(e,n,s,this.numConstants);e=i.inputs,n=i.initialState,s=i.constants;let o=[],a=[];if(n!=null){t.initialState=n,o=o.concat(n),this.stateSpec=[];for(const h of n)this.stateSpec.push(new 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 ci;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=Ke(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=hN(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=Dh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Nw(t,[1,n]):t):this.cell.stateSize>1?[Nw(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()===_i.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=li(s,n);return new e(Object.assign(t,{cell:i}))}}_i.className="RNN",me(_i);class nc extends lt{}class Hp extends nc{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=Vr(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=Xa([1,Mr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xa([1,Mr([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=Yr({ones:()=>En(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Di(X(e,o),this.kernel.read()):i=Di(e,this.kernel.read()),this.bias!=null&&(i=ki(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Di(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:Gr(this.activation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),recurrentInitializer:Yt(this.recurrentInitializer),biasInitializer:Yt(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)}}Hp.className="SimpleRNNCell",me(Hp);class fL extends _i{constructor(e){e.cell=new Hp(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)}}fL.className="SimpleRNN",me(fL);class qp extends nc{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=Vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vr(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=Xa([1,Mr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xa([1,Mr([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=Yr({ones:()=>En(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(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=Di(e,this.kernel.read());this.useBias&&(d=ki(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[y,b]=is(m,[2*this.units,this.units],m.rank-1),w=Di(s,y),[L,T,A]=is(d,3,d.rank-1),[N,E]=is(w,2,w.rank-1);a=this.recurrentActivation.apply(be(L,N)),c=this.recurrentActivation.apply(be(T,E));const D=Di(X(c,s),b);h=this.activation.apply(be(A,D));const F=be(X(a,s),X(be(1,zt(a)),h));return[F,F]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Gr(this.activation),recurrentActivation:Gr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),recurrentInitializer:Yt(this.recurrentInitializer),biasInitializer:Yt(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)}}qp.className="GRUCell",me(qp);class gL extends _i{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 qp(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)}}gL.className="GRU",me(gL);class zh extends nc{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=Vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vr(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=Xa([1,Mr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xa([1,Mr([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 Bs{apply(c,h){const d=i.apply([o]),m=new Ip().apply([o]),y=i.apply([o*2]);return ov(ov(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=Yr({ones:()=>En(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(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=Di(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),y=be(y,Di(s,this.recurrentKernel.read())),this.useBias&&(y=ki(y,this.bias.read()));const[b,w,L,T]=is(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:Gr(this.activation),recurrentActivation:Gr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),recurrentInitializer:Yt(this.recurrentInitializer),biasInitializer:Yt(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)}}zh.className="LSTMCell",me(zh);class yL extends _i{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 zh(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)}}yL.className="LSTM",me(yL);class jp extends nc{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){Ww(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Po(`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(li(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 $w(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]])}Uw(t)}}jp.className="StackedRNNCells",me(jp);function Yr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>cv(t(),n),a=()=>Fh(o,t,s);if(!i||i<=1)return Cn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(h=>Cn(h.clone()))}var lG=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 nc{}class uN extends _i{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 tr("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=>Cn(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=hi(h,s[0],i,o[0],a[0]),y=hi(d,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,y]:[m,y,n]];return b}}uN.className="ConvRNN2D";class Kp extends zh{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=tc(n,2,"kernelSize"),this.kernelSize.forEach(c=>pn(c,"kernelSize")),this.strides=tc(s||1,2,"strides"),this.strides.forEach(c=>pn(c,"strides")),this.padding=i||"valid",Ts(this.padding),this.dataFormat=o||"channelsLast",Vt(this.dataFormat),this.dilationRate=tc(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 Bs{apply(y,b){const w=h.apply([d]),L=ni([d]),T=h.apply([d*2]);return vw([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 ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yr({ones:()=>En(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,h=(Ie,Se,Ee)=>!Se||!Se[Ee]?Ie:X(Se[Ee],Ie);let d=h(s,c,0),m=h(s,c,1),y=h(s,c,2),b=h(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let L=h(i,w,0),T=h(i,w,1),A=h(i,w,2),N=h(i,w,3);const E=3,[D,F,_,B]=is(this.kernel.read(),a,E),[$,H,q,J]=this.useBias?is(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]=is(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=lG(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=Xi(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ki(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return Xi(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}Kp.className="ConvLSTM2DCell",me(Kp);class bL extends uN{constructor(e){const t=new Kp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}bL.className="ConvLSTM2D",me(bL);class Xp 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=Ke(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Fh(()=>cv(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()}}Xp.className="Dropout",me(Xp);class wL extends Xp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}wL.className="SpatialDropout1D",me(wL);class LL 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=Vr(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=Ke(e),s=JA(this.activation.getClassName());let i;return s!=null?i=Di(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Di(n,this.kernel.read()),this.bias!=null&&(i=ki(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:Gr(this.activation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),biasInitializer:Yt(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}}LL.className="Dense",me(LL);class SL 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],Br(e,1)]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Ke(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 Dz(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}SL.className="Flatten",me(SL);class IL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Vr(e.activation)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e);return this.activation.apply(n)})}getConfig(){const e={activation:Gr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}IL.className="Activation",me(IL);class xL 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=Ke(e),Oz(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}xL.className="RepeatVector",me(xL);class TL 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=Br(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=Ke(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}}TL.className="Reshape",me(TL);class AL 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=oi(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(Ke(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}AL.className="Permute",me(AL);class vL 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=Ke(e),s=-1;return Jl(Fr(n,this.maskValue),s)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e),s=-1,i=!0,o=Jl(Fr(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}vL.className="Masking",me(vL);class NL 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(Rt(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=Ke(e),Fr(e,Qe(e))):null)}computeOutputShape(e){if(e=St(e),this.inputLength==null)return[...e,this.outputDim];const t=Rt(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=Ke(e);n.dtype!=="int32"&&(n=Eh(n,"int32"));const s=av(this.embeddings.read(),n.as1D());return s.reshape(St(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Yt(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}}NL.className="Embedding",me(NL);class Yo 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=Ur(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&Ur(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ee(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=Mr(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=Dh(o,1);n.push(o)}return this.mergeFunction(n)}else{let i=!1;for(const c of e){const h=c.rank;if(h==null){const d=c.shape,m=d[0],y=d.slice(1).concat([m]);let b=c.reshape([m].concat(Br(d.slice(1))));b=Me(b,[1,0]),b=b.reshape(y),n.push(b),i=!0}else if(h>1){const d=oi(1,h).concat([0]);n.push(Me(c,d)),i=!0}else n.push(c)}let o=this.mergeFunction(n);const a=o.rank;if(i){if(a==null){const c=o.shape,h=c.length,d=c[h-1],m=[d].concat(c.slice(0,c.length-1));o=Me(o.reshape([-1,d]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(oi(0,a-1));o=Me(o,c)}}return o}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){const i=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let n=[];for(const s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Ur(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return ee(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:qn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=$s(n,t[s]);return n})}}class Gh extends Yo{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})}}Gh.className="Add",me(Gh);function UQ(e){if(Array.isArray(e)){const t=new Gh({});return t.apply(e)}else return new Gh(e)}class Vh extends Yo{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})}}Vh.className="Multiply",me(Vh);function BQ(e){if(Array.isArray(e)){const t=new Vh({});return t.apply(e)}else return new Vh(e)}class Yh extends Yo{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)})}}Yh.className="Average",me(Yh);function MQ(e){if(Array.isArray(e)){const t=new Yh({});return t.apply(e)}else return new Yh(e)}class Hh extends Yo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ws(t,e[n]);return t})}}Hh.className="Maximum",me(Hh);function PQ(e){if(Array.isArray(e)){const t=new Hh({});return t.apply(e)}else return new Hh(e)}class qh extends Yo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Eo(t,e[n]);return t})}}qh.className="Minimum",me(qh);function zQ(e){if(Array.isArray(e)){const t=new qh({});return t.apply(e)}else return new qh(e)}class jh extends Yo{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(()=>vw(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(En(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(qn(t[o],-1)):s.push(t[o]);const i=Pt(s,this.axis);return kd(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}jh.className="Concatenate",me(jh);function GQ(e){if(Array.isArray(e)){const t=new jh({});return t.apply(e)}else return new jh(e)}function Kh(e,t){for(;e<0;)e+=t;return e}function hG(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 CL extends Yo{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)=>Kh(i,e[o].shape.length)):s=[Kh(this.axes,t.shape.length),Kh(this.axes,n.shape.length)],this.normalize&&(t=Fp(t,s[0]),n=Fp(n,s[1])),hG(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Kh(this.axes,e.length),Kh(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}}CL.className="Dot",me(CL);class RL 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=Ke(e),s=()=>Sp(n.shape,0,this.stddev).add(n),i=Fh(s,()=>n,t.training||!1);return i})}}RL.className="GaussianNoise",me(RL);class OL 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=Ke(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(Sp(n.shape,1,i))};return Fh(s,()=>n,t.training||!1)}return n})}}OL.className="GaussianDropout",me(OL);class EL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ke(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=Ke(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let h=Ji(_o(n),this.rate);h=Eh(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 Fh(s,()=>Ke(e),t.training||!1)}return e})}}EL.className="AlphaDropout",me(EL);function Xh(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=_T(e,t,n,s,i,o);else if(e.rank===3)a=WT(e,t,n,s,i,o);else if(e.rank===4)a=$T(e,t,n,s,i,o);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function uG(e,t,n,s,i=.001){return ee(()=>{const o=jd(e,s),a=o.mean,c=o.variance,h=Xh(e,a,c,n,t,i);return[h,a,c]})}function dG(e,t,n,s,i=.001){return ee(()=>{const o=jd(e,s),a=o.mean,c=o.variance,h=[];for(const L of oi(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=Xh(e,d,m,b,y,i);return[w,a,c]})}function pG(e,t,n,s,i=.001){return ot(s.slice().sort(),oi(0,e.rank-1))?uG(e,t,n,s,i):dG(e,t,n,s,i)}class DL 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=Ke(e),i=s.shape,o=i.length,a=oi(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const h=Bo(1,o);h[c]=i[c];const d=a.slice();d.sort();const m=!ot(d,oi(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 Xh(s,N,E,D,F,this.epsilon)}else return Xh(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]=pG(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:Yt(this.betaInitializer),gammaInitializer:Yt(this.gammaInitializer),movingMeanInitializer:Yt(this.movingMeanInitializer),movingVarianceInitializer:Yt(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}}DL.className="BatchNormalization",me(DL);class kL 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!==Ur(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=Ke(e),s=n.shape,i=s.length;return ee(()=>{const o=!0;let{mean:a,variance:c}=jd(n,this.axis,o);const h=Bo(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),Xh(n,a,c,y,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Yt(this.betaInitializer),gammaInitializer:Yt(this.gammaInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}kL.className="LayerNormalization",me(kL);function VQ(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 Ri(e,n)})}function mG(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=ii()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Ri(e,s)})}class FL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ii():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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(()=>mG(Ke(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}FL.className="ZeroPadding2D",me(FL);function Jp(e,t,n,s,i,o){return ee(()=>{Vt(i),ev(o),Ts(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=ii()),o==null&&(o="max"),e=aL(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=hh(e,t,n,c):a=nh(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}function dN(e,t,n,s,i,o){return ee(()=>{Vt(i),ev(o),Ts(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=ii()),o==null&&(o="max"),e=rN(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=Wb(e,t,n,c):a=Ib(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,4,1,2,3])),a})}class pN 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=hi(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=Dh(Ke(e),2);const n=this.poolingFunction(Ke(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return _r(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class _L extends pN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"max")}}_L.className="MaxPooling1D",me(_L);class WL extends pN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"avg")}}WL.className="AveragePooling1D",me(WL);class mN 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,Vt(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=hi(t,this.poolSize[0],this.padding,this.strides[0]),n=hi(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(Ke(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 $L extends mN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"max")}}$L.className="MaxPooling2D",me($L);class UL extends mN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"avg")}}UL.className="AveragePooling2D",me(UL);class fN 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,Vt(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=hi(t,this.poolSize[0],this.padding,this.strides[0]),n=hi(n,this.poolSize[1],this.padding,this.strides[1]),s=hi(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(Ke(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 BL extends fN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),dN(e,t,n,s,i,"max")}}BL.className="MaxPooling3D",me(BL);class ML extends fN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),dN(e,t,n,s,i,"avg")}}ML.className="AveragePooling3D",me(ML);class gN 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 gN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Ke(e);return Gt(n,1)})}}PL.className="GlobalAveragePooling1D",me(PL);class zL extends gN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Ke(e);return jn(n,1)})}}zL.className="GlobalMaxPooling1D",me(zL);class yN extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(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 GL extends yN{call(e,t){return ee(()=>{const n=Ke(e);return this.dataFormat==="channelsLast"?Gt(n,[1,2]):Gt(n,[2,3])})}}GL.className="GlobalAveragePooling2D",me(GL);class VL extends yN{call(e,t){return ee(()=>{const n=Ke(e);return this.dataFormat==="channelsLast"?jn(n,[1,2]):jn(n,[2,3])})}}VL.className="GlobalMaxPooling2D",me(VL);class bN 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=li(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class YL extends bN{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=Ke(e);const n=(o,a)=>{const c=Ke(this.layer.call(o,t));return[c,[]]},s=hN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}YL.className="TimeDistributed",me(YL);function fG(e){ja(Tz,"BidirectionalMergeMode",e)}const gG="concat";class HL extends bN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=li(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=li(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?gG:e.mergeMode,fG(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()):Kn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=lN(e,n,s,this.numConstants);if(e=i.inputs,n=i.initialState,s=i.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);const o=[],a=[];if(n!=null){const h=n.length;if(h%2>0)throw new 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 ci;for(const h of o)if(h instanceof ci!==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=vw([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){Po(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Po(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=li(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)}}HL.className="Bidirectional",me(HL);function yG(e){return new Ja(e)}function bG(e){return new iL(e)}function wG(e){return new tL(e)}function LG(e){return new nL(e)}function SG(e){return new sL(e)}function IG(e){return new oL(e)}function xG(e){return new rL(e)}function TG(e){return new Yp(e)}function AG(e){return new Ph(e)}function vG(e){return new hL(e)}function 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t.size=="number"?t.size:parseInt(t.size,10)):[]}function sS(e,t,n){const s=e[t];return s&&s.shape?EN(s.shape):n}function iS(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 rS(e,t,n,s=!1){const i=e[t];return i&&i.list&&i.list.s?i.list.s.map(o=>RN(o,s)):n}function oS(e,t,n){const s=e[t];return s&&s.list&&s.list.shape?s.list.shape.map(i=>EN(i)):n}function aS(e,t,n){const s=e[t];return s&&s.list&&s.list.b?s.list.b:n}class DY{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 Jn(e,this.tensorMap,this.context)}getAttr(e,t){const n=this.node.rawAttrs[e];if(n.tensor!=null)return Jn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return QL(this.node.rawAttrs,e,t);if(n.s!=null)return JL(this.node.rawAttrs,e,t);if(n.b!=null)return ZL(this.node.rawAttrs,e,t);if(n.shape!=null)return sS(this.node.rawAttrs,e,t);if(n.type!=null)return tS(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return iS(this.node.rawAttrs,e,t);if(n.list.s!=null)return rS(this.node.rawAttrs,e,t);if(n.list.shape!=null)return oS(this.node.rawAttrs,e,t);if(n.list.b!=null)return aS(this.node.rawAttrs,e,t);if(n.list.type!=null)return nS(this.node.rawAttrs,e,t)}return t}}const kY=(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[kT(R("tensors",e,t,n))];case"FloorMod":case"Mod":return[qd(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[Rb(R("a",e,t,n),R("b",e,t,n))];case"FloorDiv":return[Dd(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[Eo(R("a",e,t,n),R("b",e,t,n))];case"Maximum":return[Ws(R("a",e,t,n),R("b",e,t,n))];case"Pow":return[si(R("a",e,t,n),R("b",e,t,n))];case"SquaredDifference":return[yh(R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},HQ="arithmetic";const FY=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[sn(R("x",e,t,n))];case"Acos":return[ub(R("x",e,t,n))];case"Acosh":return[db(R("x",e,t,n))];case"Asin":return[fb(R("x",e,t,n))];case"Asinh":return[gb(R("x",e,t,n))];case"Atan":return[yb(R("x",e,t,n))];case"Atan2":return[bb(R("x",e,t,n),R("y",e,t,n))];case"Atanh":return[wb(R("x",e,t,n))];case"Ceil":return[Tb(R("x",e,t,n))];case"Complex":return[Ti(R("real",e,t,n),R("imag",e,t,n))];case"Cos":return[rh(R("x",e,t,n))];case"Cosh":return[Bd(R("x",e,t,n))];case"Elu":return[Oo(R("x",e,t,n))];case"Erf":return[Ob(R("x",e,t,n))];case"Exp":return[Ls(R("x",e,t,n))];case"Expm1":return[Eb(R("x",e,t,n))];case"Floor":return[_a(R("x",e,t,n))];case"Log":return[ns(R("x",e,t,n))];case"Log1p":return[Gd(R("x",e,t,n))];case"Imag":return[$a(R("x",e,t,n))];case"Neg":return[zt(R("x",e,t,n))];case"Reciprocal":return[Mb(R("x",e,t,n))];case"Real":return[Do(R("x",e,t,n))];case"Relu":return[Oi(R("x",e,t,n))];case"Round":return[zb(R("x",e,t,n))];case"Selu":return[Jd(R("x",e,t,n))];case"Sigmoid":return[Ni(R("x",e,t,n))];case"Sin":return[Zd(R("x",e,t,n))];case"Sign":return[Vb(R("x",e,t,n))];case"Sinh":return[Qd(R("x",e,t,n))];case"Softplus":return[Ua(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[Fa(R("x",e,t,n))];case"Tan":return[qb(R("x",e,t,n))];case"Relu6":case"ClipByValue":return[Hn(R("x",e,t,n),R("clipValueMin",e,t,n),R("clipValueMax",e,t,n))];case"Rsqrt":return[Xd(Jn(e.inputNames[0],t,n))];case"Prod":return[Kd(R("x",e,t,n),R("axes",e,t,n))];case"LeakyRelu":return[zd(R("x",e,t,n),R("alpha",e,t,n))];case"Prelu":return[dh(R("x",e,t,n),R("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},qQ="basic_math";function Ps(e,t,n=""){k(_Y(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function _Y(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 WY{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),Cn(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},
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        ${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 Jh{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}`);Ps(t,i.shape,"TensorList shape mismatch: "),Cn(i)}),this.idTensor=Ne(0),this.maxNumElements=s,Cn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Jh([...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 Ps(e,this.elementShape,"TensorList shape mismatch: "),ee(()=>{const s=this.tensors.map(i=>K(i,e));return rs(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 Ps(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(Ps(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Cn(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 Ps(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.`);Ps(this.elementShape,t.shape,"TensorList shape mismatch: "),Cn(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 Ps(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 rs(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return Ps(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 Pt(n,0)})}}function $Y(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);Ps(i,t,"TensorList shape mismatch: ");const o=Ei(e);return new Jh(o,t,s)}function UY(e,t,n){return new Jh([],e,t,n)}function BY(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 Jh([],n,e.dtype,s),a=Ei(e,0);return t.forEach((c,h)=>{o.setItem(c,a[h])}),o}function MY(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
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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[or(s)]}case"Switch":{const s=R("pred",e,t,n);let i=R("data",e,t,n);return i.kept||(i=or(i)),(await s.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const s=e.inputNames.find(i=>Jn(i,t,n)!==void 0);if(s){const i=Jn(s,t,n);return[or(i)]}return}case"Enter":{const s=R("frameName",e,t,n),i=R("tensor",e,t,n);return n.enterFrame(s),[or(i)]}case"Exit":{const s=R("tensor",e,t,n);return n.exitFrame(),[or(s)]}case"NextIteration":{const s=R("tensor",e,t,n);return n.nextIteration(),[or(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 WY(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("axis",e,t,n);return[_r(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[Ri(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[uh(R("x",e,t,n),s,i)]}case"BatchToSpaceND":{const s=R("blockShape",e,t,n),i=R("crops",e,t,n);return[sh(R("x",e,t,n),s,i)]}case"DepthToSpace":{const s=R("blockSize",e,t,n),i=R("dataFormat",e,t,n).toUpperCase();return[Nb(R("x",e,t,n),s,i)]}case"BroadcastTo":return[ih(R("x",e,t,n),R("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},aee="transformation";function kN(e,t,n){const s=((i,o,a)=>{switch(i.category){case"arithmetic":return ee(()=>kY(i,o,a));case"basic_math":return ee(()=>FY(i,o,a));case"control":return PY(i,o,a);case"convolution":return ee(()=>zY(i,o,a));case"creation":return ee(()=>GY(i,o,a));case"dynamic":return VY(i,o,a);case"evaluation":return ee(()=>YY(i,o,a));case"image":return ee(()=>qY(i,o,a));case"graph":return ee(()=>HY(i,o,a));case"logical":return ee(()=>jY(i,o,a));case"matrices":return ee(()=>KY(i,o,a));case"normalization":return ee(()=>XY(i,o,a));case"reduction":return ee(()=>JY(i,o,a));case"slice_join":return ee(()=>ZY(i,o,a));case"spectral":return ee(()=>QY(i,o,a));case"transformation":return ee(()=>eH(i,o,a));case"custom":const c=NN(i.op);if(c&&c.customExecutor)return c.customExecutor(new DY(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 FN{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 _N(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const h=new Set,d=Object.keys(e).map(b=>as(b)[0]);let m=[];s!=null&&(m=s.map(b=>as(b.name)[0]));const y=[...t];for(;y.length>0;){const b=y.pop();if((WN(b)||iH(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 tH(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>as(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 nH=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],sH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function WN(e){return nH.indexOf(e.op)>=0}function iH(e){return sH.indexOf(e.op)>=0}class lS{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 lS(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=_N(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 tH(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[as(m)[0]]),i=t.map(m=>as(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 FN(this.weightMap,h,d,this.functionExecutorMap),y=Object.assign({},this.weightMap);Object.keys(e).forEach(L=>{const[T,A]=as(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=kN(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=>Jn(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=ZV(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 FN(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(y=>Jn(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[as(E)[0]]),a=n.map(E=>as(E)[0]),c=a.map(E=>this.graph.nodes[E]),{usedNodes:h,missingInputs:d,dynamicNode:m,syncInputs:y}=_N(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]=as(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=>!WN(E)&&!Jn(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}]. ${E}`)}return w}processStack(e,t,n,s,i,o,a,c,h){const d=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let y="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([y]=rr(m.node.name,n)),e.indexOf(m.node)===-1){const b=kN(m.node,s,n);y||([y]=rr(m.node.name,n));const w=n.currentContext;b instanceof Promise?d.push(b.then(L=>(s[y]=L,n.currentContext=w,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h),L))):(s[y]=b,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h))}else this.processChildNodes(m.node,t,n,s,i,h)}return d}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=rr(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(h=>!!Jn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(h=>!!Jn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=as(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,h)=>o[h]===-1||o[h]===c);k(a,()=>`The shape of dict['${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 ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=as(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=as(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}const rH="?tfjs-format=file",oH="model.json";class $N{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ad(e,this.loadOptions);else{const t=jy(e,this.loadOptions);if(t.length===0)t.push(Ad(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;const s=Sd(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new lS(CN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),e.modelInitializer!=null){const i=CN.Instance.transformGraph(e.modelInitializer);this.initializer=new lS(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=qy(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function aH(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${oH}${rH}`));const n=new $N(e,t);return await n.load(),n}const UN="2.6.0";function cH(e,t){return tm(e,t)}function tm(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(sc(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],h=tm(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 lH(e,t=MN){return BN(e,t)}function BN(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(sc(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(d=>d[a]),h=BN(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 MN(e){return e===null?null:sc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function PN(e,t){const n=new Map;tm(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=tm(e,t,n);return s}function sc(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Q))}function hH(e){return e==null||uH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Q||wn(e)}function uH(e){return e===null||typeof e!="object"&&typeof e!="function"}function dH(e){return cH(e,pH)}function pH(e){return e instanceof Q?{value:e.clone(),recurse:!1}:sc(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class zN{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 hS extends zN{constructor(){super(hS.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}}hS.INITIAL_CAPACITY=32;function GN(e){return new fH(e)}function cee(e){let t=e;return Zh(()=>({value:t++,done:!1}))}function Zh(e){return new gH(e)}function VN(e,t){return new HN(e,t)}function lee(e,t,n){return VN(Zh(e).take(t),n)}function mH(e,t=Hr.FAIL){return new AH(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 xH(this,e)}filter(e){return new SH(this,e)}map(e){return new IH(this,e)}mapAsync(e){return new YN(this,e)}serialMapAsync(e){return new YN(this,e).serial()}flatmap(e){return new TH(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 LH(this,e,t)}columnMajorBatch(e,t=!0,n=MN){const s=this.rowMajorBatch(e,t);return s.map(i=>lH(i,n))}concatenate(e,t){return new HN(GN([this,e]),t)}take(e){return e<0||e==null?this:new wH(this,e)}skip(e){return e<0||e==null?this:new bH(this,e)}prefetch(e){return new qN(this,e)}shuffle(e,t){return new vH(this,e,t)}serial(){return new yH(this)}}class fH 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:dH(e),done:!1}}}class gH 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: ${e.message}`,e}}}class yH extends fn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class bH 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`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;He(e.value)}return this.upstream.next()}}class wH extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class LH 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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class SH extends fn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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 IH extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=ji(e.value),n=this.transform(e.value),s=ji(n);for(const i of t)wd(i,s)||i.dispose();return{value:n,done:!1}}}class xH extends fn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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 YN extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=ji(e.value),n=await this.transform(e.value),s=ji(n);for(const i of t)wd(i,s)||i.dispose();return{value:n,done:!1}}}class uS extends fn{constructor(){super();this.outputQueue=new hS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class TH extends uS{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=ji(e.value),n=this.transform(e.value),s=ji(n);this.outputQueue.pushAll(n);for(const i of t)wd(i,s)||i.dispose();return!0}}class HN 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`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Hr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Hr||(Hr={}));class AH extends fn{constructor(e,t=Hr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${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 PN(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Hr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Hr.SHORTEST:return{value:null,done:!0};case Hr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class qN extends fn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new zN(t)}summary(){return`${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 vH extends qN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Pa(n||Yn().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 ic{constructor(){this.size=null}batch(e,t=!0){const n=this;k(e>0,()=>`batchSize needs to be positive, but it is
      ${e}`);let s;return this.size===Infinity||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),cs(async()=>(await n.iterator()).columnMajorBatch(e,t,RH),s)}concatenate(e){const t=this;let n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,cs(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,cs(async()=>(await t.iterator()).filter(s=>ee(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return cs(async()=>(await t.iterator()).map(n=>ee(()=>e(n))),this.size)}mapAsync(e){const t=this;return cs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return cs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,cs(async()=>{const s=Zh(async()=>({value:await t.iterator(),done:!1}));return VN(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,cs(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=Pa(t||Yn().toString());return cs(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,cs(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()}}ic.MAX_BUFFER_SIZE=1e4;function cs(e,t=null){return new class extends ic{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function NH(e){return cs(async()=>GN(e),e.length)}function CH(e){if(!sc(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 cs(async()=>{const n=await PN(e,s=>{if(s instanceof ic)return{value:s.iterator(),recurse:!1};if(sc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return mH(n,Hr.SHORTEST)},t)}function RH(e){if(e===null)return null;const t=e[0];if(hH(t)){const n=OH(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function OH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Q?rs(e):en(e)}class jN extends ic{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 nm='"',Qh=Symbol("out"),KN=Symbol("field"),sm=Symbol("quote"),dS=Symbol("quoteafterquote"),XN=Symbol("quoteinquote");class JN extends ic{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 jN(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=Qh;for(let a=0;a<i;a++)switch(o){case Qh:switch(e.charAt(a)){case nm:s=a+1,o=sm;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=Qh;break;default:o=KN,s=a;break}break;case KN:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=Qh,s=a+1;break;default:}break;case sm:switch(e.charAt(a)){case nm:o=dS;break;default:}break;case dS:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=Qh,s=a+1;break;case nm:o=sm;break;default:o=XN;break}break;case XN:switch(e.charAt(a)){case nm:o=sm;break;default:}break;default:}if(o===dS?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 ZN 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 ZN(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 QN 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=ss([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=Wr([o,i,c,a],[1,4])}else this.cropBox=Wr([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 QN(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=bT(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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return ee(()=>{const t=e.toFloat().expandDims(0);let n;n=$r.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 e0{}class t0 extends fn{split(e){return new EH(this,e)}}class EH extends t0{constructor(e,t){super();this.upstream=e,this.impl=new DH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class DH extends uS{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 kH extends fn{decodeUTF8(){return new FH(this)}}class FH extends t0{constructor(e){super();this.upstream=e,this.impl=new _H(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class _H extends uS{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()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let n;return C().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}}class n0 extends kH{constructor(e,t={}){super();this.file=e,this.options=t,k(e instanceof Uint8Array||(C().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,n)=>{const s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{const i=new FileReader;i.onload=a=>{let c=i.result;if(c instanceof ArrayBuffer&&(c=new Uint8Array(c)),!(c instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(c)},i.onabort=a=>n(new Error("Aborted")),i.onerror=a=>n(new Error(a.type));const o=this.file.slice(this.offset,s);i.readAsArrayBuffer(o)}this.offset=s});return{value:await e,done:!1}}}async function WH(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=$H(e));const i=await Vx(n,s);if(i.ok){const o=new Uint8Array(await i.arrayBuffer());return new n0(o,t)}else throw new Error(i.statusText)}const $H=e=>{const t={method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity};return t};function s0(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class i0 extends e0{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(s0(this.input)&&C().get("IS_NODE")){const e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new n0(this.input,this.options)}}class r0 extends e0{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return s0(this.url)?new i0(this.url,this.fileOptions).iterator():WH(this.url,this.fileOptions)}}function UH(e,t={}){return new JN(new r0(e),t)}function BH(e){const t=Zh(e);return cs(async()=>t)}function MH(e){return cs(async()=>{const t=await e();return Zh(()=>t.next())})}async function PH(e,t){return QN.create(e,t)}async function zH(e){return ZN.create(e)}const o0="2.6.0";var GH=Object.freeze({__proto__:null,array:NH,Dataset:ic,zip:CH,CSVDataset:JN,TextLineDataset:jN,csv:UH,func:BH,generator:MH,microphone:zH,webcam:PH,FileDataSource:i0,URLDataSource:r0,version_data:o0});function xe(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}const VH=up,YH=mw,HH=fw,qH=gw,jH=ip;function pS(e,t,n,s){if(n==="linear")return e.linear(t);if(n==="relu")return e.relu(t);if(n==="elu")return Oo(t);if(n==="relu6")return e.relu6(t);if(n==="prelu")return e.prelu(t,s);throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}class KH extends g{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new p(this,_s())}write(e,t,n){this.firstUse&&(this.firstUse=!1,C().get("IS_NODE")&&qa(`
============================
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 er(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=>zl(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 _s().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=Yn();e();const n=Yn()-t;return{kernelMs:n}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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Va(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 Va(y,e.shape)}multinomial(e,t,n,s){xe(e,"multinomial");const i=t?e:Wo(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=Pa(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 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YH(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}=Da(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]=vd(e,t);if(o===0)return en([],i,e.dtype);const h=new vr([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}=Da(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||Ta(t);const s=wo(n,we(e));return s.fill(t),_s().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=wo(e.dtype,we(e.shape));return this.makeOutput(t,e.shape,e.dtype)}linspace(e,t,n){return dw(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 vr(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 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N=fS({inputs:{x:c},backend:n,attrs:{begin:[A,0],size:[1,o]}}),E=fS({inputs:{x:h},backend:n,attrs:{begin:[A,0],size:[1,o]}}),D=di({inputs:{real:N,imag:E},backend:n}),{real:F,imag:_}=u4(D,t,n),B=er(F,_);for(let $=0;$<o;$++){const H=uw(B,$);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=di({inputs:{real:w,imag:L},backend:n});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(L),T}function u4(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(d4(s)){const c=wS(o,a,s,t,n),h=[e.shape[0],e.shape[1]];if(t){const 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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=gS(b,c,s.dtype,y,D),m=ws(m.length,h),c=D}xe(s,"max"),ts("max",m,h);const[w,L]=Rn(c,m),T=we(L),A=f0(b,T,w,s.dtype),N=a.write(A,w,s.dtype);let E=w;if(o){const D=On(w,d);E=D}return{dataId:N,shape:E,dtype:s.dtype}}};function O4(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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${e}.`)});const{simpleAbsImpl:cK,addImpl:lK,ceilImpl:hK,expImpl:uK,expm1Impl:dK,floorImpl:pK,logImpl:mK,maxImpl:fK,multiplyImpl:gK,rsqrtImpl:yK,sliceImpl:bK,subImpl:wK,transposeImpl:D0,uniqueImpl:LK}=Sq;class SK{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((i,o)=>`T${o}`);const n=[];this.variableNames.forEach(i=>{n.push(`float v${i} = get${i}AtOutCoords();`)});const s=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        float result = ${s};
        setOutput(result);
      }
    `}}class IK{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((i,o)=>`T${o}`);const n=[];this.variableNames.forEach(i=>{n.push(`vec4 v${i} = get${i}AtOutCoords();`)});const s=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        vec4 result = ${s};
        setOutput(result);
      }
    `}}class xK{constructor(e,t,n){this.variableNames=["A"];const{windowSize:s,batchSize:i,outSize:o}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[i,o];const a=t==="max"?">":"<",c=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${s};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${s}; i++) {
          int inIdx = ${c};
          float candidate = getA(batch, inIdx);
          if (candidate ${a} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}}function k0(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function ls(e,t){return t===1?[e]:k0(e,t)}function TK(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)));
      }
    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:i,output:o,defineOutput:a,defineSpecialNaN:c,defineSpecialInf:h,defineRound:d}}function jo(e,t,n="index"){const s=Ot(t);return s.map((i,o)=>{const a=`int ${e[o]} = ${n} / ${i}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${i}`:`index -= ${e[o]} * ${i}`;return`${a}; ${c};`}).join("")}function um(e){return e.length===1?`${e[0]}`:`vec${e.length}(${e.join(",")})`}function bee(e,t){if(e.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${e.length} and ${t.length}`);const n=[],s=Math.floor(e.length/4),i=e.length%4;for(let o=0;o<s;o++){const a=e.slice(o*4,o*4+4),c=t.slice(o*4,o*4+4);n.push(`${um(a)}, ${um(c)}`)}if(i!==0){let o=e.slice(s*4),a=t.slice(s*4);o.length===1&&(o=o.map(c=>`float(${c})`),a=a.map(c=>`float(${c})`)),n.push(`${um(o)}, ${um(a)}`)}return n.map((o,a)=>`dot(${o})`).join("+")}function vS(e){const t=Ot(e).map(n=>n.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}const F0=`
  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:_0}=pw;function AK(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=>vK(L,t,s)).join(`
`),c=t.texShape,h=Wn(),d=RK(h);let m,y,b=DK(h);t.isPacked?(m=NK(t.logicalShape,c),y=EK(h)):(m=CK(t.logicalShape,c),y=OK(h)),s&&(b+=WK);const w=[b,d,y,o,m,a,n].join(`
`);return w}function dc(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return jK(e);case 1:return XK(e);case 2:return ZK(e);case 3:return e5(e);case 4:return n5(e);case 5:return s5(e);case 6:return i5(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function W0(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return qK(e);case 1:return KK(e);case 2:return JK(e);case 3:return QK(e);default:return t5(e)}}function vK(e,t,n=!1){let s="";n?s+=W0(e):s+=dc(e);const i=e.shapeInfo.logicalShape,o=t.logicalShape;return i.length<=o.length&&(n?s+=r5(e,t):s+=o5(e,t)),s}function NK(e,t){switch(e.length){case 0:return $0();case 1:return $K(e,t);case 2:return YK(e,t);case 3:return BK(e,t);default:return PK(e,t)}}function CK(e,t){switch(e.length){case 0:return $0();case 1:return UK(e,t);case 2:return HK(e,t);case 3:return MK(e,t);case 4:return zK(e,t);case 5:return GK(e,t);case 6:return VK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function RK(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function OK(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function EK(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function DK(e){const t=`${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);
    }

    ${kK}
    ${FK}
    ${_K}
  `;return t}const kK=`
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);
}
`,FK=`
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);
}
`,_K=`
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);
}
`,WK=`
  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 $0(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function $K(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 UK(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 * ${t[1]} + resTexRC.y;
    }
  `}function BK(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),i=s*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${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 MK(e,t){const n=jo(["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;
      ${n}
      return ivec3(r, c, d);
    }
  `}function PK(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[e.length-1]/2),i=s*Math.ceil(e[e.length-2]/2);let o=i,a="",c="b, r, c";for(let h=2;h<e.length-1;h++)o*=e[e.length-h-1],a=`
      int b${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 zK(e,t){const n=jo(["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;
      ${n}
      return ivec4(r, c, d, d2);
    }
  `}function GK(e,t){const n=jo(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function VK(e,t){const n=jo(["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 YK(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 HK(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 * ${e[1]};
      return ivec2(r, c);
    }
  `}function Ko(e){return`offset${e}`}function qK(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 jK(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=Ko(t);return`
    float ${n}() {
      vec2 uv = uvFromFlat(${o}, ${a}, ${c});
      return sampleTexture(${t}, uv);
    }
  `}function KK(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 XK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
      float ${n}(int index) {
        ${pc(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=Ko(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 JK(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 ZK(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}=Ir(t),c=o;if(c.length<t.length){const y=mc(e,c),b=["row","col"];return`
      ${dc(y)}
      float ${s}(int row, int col) {
        return ${s}(${fc(b,a)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
        ${pc(e)}
      }
    `;const h=i[0],d=i[1],m=Ko(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 QK(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=mc(e,y),L=["b","row","col"];return`
        ${W0(w)}
        vec4 ${s}(int b, int row, int col) {
          return ${s}(${fc(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 e5(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}=Ir(t),h=a;if(h.length<t.length){const L=mc(e,h),T=["row","col","depth"];return`
        ${dc(L)}
        float ${s}(int row, int col, int depth) {
          return ${s}(${fc(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)));
        ${pc(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=Ko(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 t5(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 n5(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}=Ir(t);if(c.length<t.length){const L=mc(e,c),T=["row","col","depth","depth2"];return`
      ${dc(L)}
      float ${s}(int row, int col, int depth, int depth2) {
        return ${s}(${fc(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)));
        ${pc(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=Ko(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 s5(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}=Ir(t);if(h.length<t.length){const T=mc(e,h),A=["row","col","depth","depth2","depth3"];return`
      ${dc(T)}
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        return ${s}(${fc(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;
        ${pc(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=Ko(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 i5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=Ir(t);if(i.length<t.length){const A=mc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
      ${dc(A)}
      float ${s}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${s}(${fc(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)));
        ${pc(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=Ko(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 pc(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 ${t}[i];
      }
    }
  `}function r5(e,t){const n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),i="get"+s+"AtOutCoords",o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,c=_0(e.shapeInfo.logicalShape,t.logicalShape),h=Et(a),d=a-o;let m;const y=["x","y","z","w","u","v"];o===0?m="":a<2&&c.length>=1?m="coords = 0;":m=c.map(E=>`coords.${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 o5(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=_0(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 ${e} is not yet supported`)}function mc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function fc(e,t){return t.map(n=>e[n]).join(", ")}class a5{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k(e.length>2,()=>`Packed arg${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=ls("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=ls("sourceLocR",y-1).concat("inIdx.r"),A=ls("sourceLocG",y-1).concat("inIdx.g"),N=ls("sourceLocB",y-1).concat("inIdx.b"),E=ls("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 c5{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 l5{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 U0={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class B0{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 M0=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,NS="return a + b;",CS="return a - b;",P0="return a * b;",h5=`
  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;
  }
`,u5=`
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);
`,wee="return (a - b) * (a - b);",d5="return float(a == b);",p5="return float(a != b);",m5="return float(a < b);",f5="return float(a <= b);",g5="return float(a > b);",y5="return float(a >= b);",b5="return float(a >= 1.0 && b >= 1.0);",w5="return float(a >= 1.0 || b >= 1.0);",L5=M0+`
  return max(a, b);
`,S5=M0+`
  return min(a, b);
`,I5=`if (b == 0.0) return NAN;
  return mod(a, b);`,x5="return (b >= 1.0) ? a : a * (b + 1.0);",z0="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 dm=`
  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;
`,T5=`
  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);
`,A5=`
  // 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));
  `+dm+`
  return result;
`,G0=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`,v5=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,N5=`
  return vec4(equal(a, b));
`,C5=`
  return vec4(notEqual(a, b));
`,R5=`
  return vec4(lessThan(a, b));
`,O5=`
  return vec4(lessThanEqual(a, b));
`,E5=`
  return vec4(greaterThan(a, b));
`,D5=`
  return vec4(greaterThanEqual(a, b));
`,k5=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,F5=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,_5=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+dm+`
  return result;
`,W5=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+dm+`
  return result;
`,$5=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+dm+`
  return result;
`;class jr{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) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{const c=ls("coords",i);o+=`
            bool nextRowOutOfBounds =
              (${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 U5{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 B5{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 M5{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 P5{constructor(e){this.outputShape=[],this.outputShape=Er(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;

        ${n.join(`
        `)}
      }
    `}}class z5{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=Er(e,t);const n=this.outputShape,s=n.length,i=Et(s),o=ls("coords",s),a=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((L,T)=>`T${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}(${pm(a,h,T)}),
            vec2(${pm(d,h,T)}));
        }`}const b=c.length,w=c[c.length-1];y+=`
        return getChannel(
          getT${b}(${pm(a,h,w)}),
          vec2(${pm(d,h,w)}));`,this.userCode=`
      float getValue(${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 pm(e,t,n){const s=e.indexOf(t),i=e.map((o,a)=>a===s?`${o} - ${n}`:o);return i.join()}class G5{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 V5{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 Y5{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 H5{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 q5{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 j5{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 V0{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}
        ${D}
        setOutput(result);
      }
    `}}class K5{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,i=e.strideDepth,o=e.strideHeight,a=e.strideWidth,c=e.dilationDepth,h=e.dilationHeight,d=e.dilationWidth,m=e.filterDepth,y=e.filterHeight,b=e.filterWidth,w=Math.floor(e.inChannels/4)*4,L=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${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 Y0{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}
        ${A}
        setOutput(result);
      }
    `}}class H0{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;const i=e.inHeight,o=e.inWidth,a=e.padInfo.top,c=e.padInfo.left,h=e.strideHeight,d=e.strideWidth,m=e.dilationHeight,y=e.dilationWidth,b=e.filterHeight,w=e.filterWidth,L=w;let T="int xR; int xC; int xCOffset;";for(let D=0;D<b;D++)for(let F=0;F<w;F++)T+=`
          vec4 xTexelR${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?Oy(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}
        ${N}
        setOutput(result);
      }
    `}}class X5{constructor(e,t,n,s,i){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[o,a,c,h]=e,[d]=t,[m,y]=n;this.outputShape=[d,m,y,h];const b=s==="bilinear"?1:0,[w,L]=[`${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 q0{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;const s=e.length,i=t?"0.0":`getX(${j0(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 = ${K0(s,"coords")};
        float val = ${i};
        int pow2 = int(pow(2.0, index));
        if (${a}) {
          int idx = ${c};
          ${K0(s,"coords")} = idx;
          val += getX(${j0(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 j0(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 ${e} is not yet supported`)}function K0(e,t){if(e===1)return`${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 J5{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=su.DENSE;const t=ru(e),n=Wn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${jo(["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 Z5{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=su.DENSE;const t=ru(e),n=Wn();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${jo(["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 Q5{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

      int in_h = h / ${t};
      int offset_h = imod(h, ${t});
      int in_w = w / ${t};
      int offset_w = imod(w, ${t});
      int offset_d = (offset_h * ${t} + offset_w) *
        ${this.getOutputDepthSize()};
      int in_d = d + offset_d;

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}class e8{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}}class t8{constructor(e){this.variableNames=["A"],this.outTexUsage=As.DOWNLOAD;const t=Wn();this.outputShape=e,this.userCode=`
      ${F0}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}}class n8{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=As.DOWNLOAD;const t=Wn();this.outputShape=e,this.userCode=`
      ${F0}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}}class s8{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=`
      ${vS(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 i8{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=`
      ${vS(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 X0={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"};class J0{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 r8{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 o8{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=a8(e,n);this.userCode=`
      void main() {
        ${i} resRC = getOutputCoords();
        setOutput(getA(${o}));
      }
    `}}function a8(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 c8{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 * ${o};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}}function l8(e){const t=Wn(),n=`${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 Uj(e,n)}function h8(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 Vj(e,t)}function u8(e){const t=new Uint16Array([0,1,2,2,1,3]);return Yj(e,t)}function au(e,t,n,s,i,o){qj(t,n);const a=Hj(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 Z0(e){return e.internalFormatFloat}function d8(e,t,n,s){const[i,o]=iu(t,n);return au(e,i,o,Z0(s),s.textureFormatFloat,e.FLOAT)}function Q0(e){return e.internalFormatHalfFloat}function p8(e,t,n,s){const[i,o]=iu(t,n);return au(e,i,o,Q0(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function eC(e){return e.downloadTextureFormat}function m8(e,t,n,s){const[i,o]=iu(t,n);return au(e,i,o,eC(s),e.RGBA,e.UNSIGNED_BYTE)}function tC(e){return e.internalFormatPackedFloat}function f8(e,t,n,s){const[i,o]=lc(t,n);return au(e,i,o,tC(s),e.RGBA,e.FLOAT)}function nC(e){return e.internalFormatPackedHalfFloat}function g8(e,t,n,s){const[i,o]=lc(t,n);return au(e,i,o,nC(s),e.RGBA,s.textureTypeHalfFloat)}function y8(e,t,n){const s=0,i=3*4,o=3*4+2*4;Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=C0(e,t,"clipSpacePos",n,3,o,s);return a&&C0(e,t,"uv",n,2,o,i)}function b8(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 w8(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 L8(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 S8(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 I8(e,t,n,s){const[i,o]=iu(t,n),a=4,c=new Uint8Array(Ej(t*n,a));return Re(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function x8(e,t,n,s,i,o,a,c){const h=e,d=new Float32Array(Dj(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 T8(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 A8{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,Cj(t,e)):this.gl=Wi(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=rm(this.gl,i),zs(this.gl,o))this.textureHalfFloatExtension=rm(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),zs(this.gl,s))this.colorBufferHalfFloatExtension=rm(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",zs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(zs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=h8(this.gl),this.indexBuffer=u8(this.gl),this.framebuffer=jj(this.gl),this.textureConfig=SS(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(),d8(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),p8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),m8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),w8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),b8(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),g8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),f8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(R0(this.gl,this.framebuffer),this.outputTexture=null),Re(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>I8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return x8(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return S8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=L8(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,()=>T8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=Bj(t,e),s=l8(t),i=zj(t);return Re(t,()=>t.attachShader(i,s)),Re(t,()=>t.attachShader(i,n)),Gj(t,i),this.debug&&IS(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=y8(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&&IS(this.gl,this.program),Re(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Xj(this.gl,e,t):Jj(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(),Zj(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=lc(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&&IS(this.gl,this.program),om(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=rm(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 Ey(()=>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=v8(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;Ey(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),xS(this.gl,e,this.framebuffer),this.debug&&om(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(xS(this.gl,this.outputTexture,this.framebuffer),this.debug&&om(this.gl)):R0(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;xS(s,e,this.framebuffer),this.debug&&om(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 v8(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function N8(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=AK(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 sC(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 ${h} must match`)})}function C8(e,t,n,s,i){sC(t.inShapeInfos,n),sC([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),C().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,h)=>{const d=t.program.variableNames[h],m=t.uniformLocations[d],y=t.uniformLocations[`offset${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 R8(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 O8{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}

        ${T.output} = result;
      }
    `}}class E8{constructor(e,t,n,s,i){this.variableNames=["x"],this.outputShape=[];const o=t,a=e[3]-1;this.outputShape=e;let c;const h=`float(${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 D8{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 * ${i});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}}class k8{constructor(e,t,n,s,i){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const o=t,a=e[3]-1;this.outputShape=e;let c;const h=`float(${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 * ${c};
        setOutput(result);
      }
    `}}class F8{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,i=e.effectiveFilterHeight,o=e.effectiveFilterWidth,a=i-1-e.padInfo.top,c=o-1-e.padInfo.left,h=i*o-1;this.userCode=`
      const ivec2 pads = ivec2(${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 _8{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 RS{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 W8{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 $8{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${s}), float(${n}),
                      float(index == coords.y)));
      }
    `}}class U8{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=ls("rc",t),s=Et(t),i=M8(t,e,n),o=P8(t,e[e.length-1],e[e.length-2],n),a=z8(e,n);this.userCode=`
        void main() {
          ${s} rc = getOutputCoords();

          if(${i}) {
            setOutput(vec4(0));
          } else {
            ${o}

            setOutput(vec4(${a}));
          }
        }
      `}}}function B8(e,t){const n=[];for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=`${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 M8(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 P8(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 z8(e,t){const n=e.length,s=B8(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 G8{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 V8{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=ls("rc",s),h=ls("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 cu{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 OS{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 iC{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 rC{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=`
      ${Y8(t)}
      ${vS(e)}

      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${e[1]};
        int cols = ${e[2]};

        ${n}

        setOutput(result);
      }
    `}}function Y8(e){const t=jo(["r","c","d"],e);return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t}
      return ivec3(r, c, d);
    }
  `}class H8{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 q8{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 j8{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 K8{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 X8{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 J8{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 Z8{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-${n} tensor is not yet supported`);this.outputShape=e;const s=ls("rc",n),i=`${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 oC{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 Q8{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 e6{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,i;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)i="resRC",s="resRC";else{const a=["resRC.x","resRC.y","resRC.z","resRC.w"],c=[],h=[];for(let d=0;d<t.length;d++)h.push(`${a[d]}`),d<e&&c.push(`${a[d]}`);s=c.join(),i=h.join()}const o=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 t6{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=Et(this.rank),n=`uniform int start[${this.rank}];`,s=n6(this.rank);let i;const o=e.map((a,c)=>`sourceLoc.${ES[c]} = start[${c}] + coords.${ES[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 ES=["x","y","z","w","u","v"];function n6(e){if(e===1)return"sourceLoc";if(e<=6)return ES.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}class s6{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;const t=Et(this.rank),n=ls("coords",this.rank),s=ls("sourceLoc",this.rank),i=this.rank===1?"sourceLoc":`vec2(${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 (${e.length})`);return(t,n)=>{if(this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null))return;t.gl.uniform1iv(this.startLoc,e)}}}class i6{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const s=n.length,i=Et(n.length),o=Et(n.length);let a="";if(s===1)a="coords * strides + begin";else{let c=0;a=n.map((h,d)=>(c++,n.length===1?`coords * strides[${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 r6{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=cC(t,n),i=lC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=aC(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=cC(n,s),o=lC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=aC(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 o6(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 aC(e,t,n,s,i){const o=a6(t,s);let a;if(i){const[h,d]=lc(e[0],e[1]);a=h*d}else{const[h,d]=iu(e[0],e[1]);a=h*d}const c=o6(n,o);return a*c}function a6(e,t){switch(e){case Sn.PACKED_2X2_FLOAT32:return tC(t);case Sn.PACKED_2X2_FLOAT16:return nC(t);case Sn.UNPACKED_FLOAT32:return Z0(t);case Sn.UNPACKED_FLOAT16:return Q0(t);case Sn.PACKED_4X1_UNSIGNED_BYTE:return eC(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function c6(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 cC(e,t){if(e===As.UPLOAD)return Sn.PACKED_2X2_FLOAT32;if(e===As.RENDER||e==null)return c6(t);if(e===As.DOWNLOAD||e===As.PIXELS)return Sn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function lC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class l6{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=h6(e);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        setOutput(getA(${i}));
      }
    `}}function h6(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 cr="if (isnan(x)) return x;",u6="return x;",hC="return abs(x);",uC=cr+`
  return (x < 0.0) ? 0.0 : x;
`,dC=cr+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,pC="return (x >= 0.0) ? x : (exp(x) - 1.0);",d6=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${fp};
  float scale = ${gp};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;function p6(e=0){return cr+`
    return x > 0.0 ? 1.0 : float(${e});
  `}const mC="return -x;",fC="return ceil(x);",gC="return floor(x);",m6=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,f6="return float(isnan(x));",g6="return float(isinf(x));",y6="return float(!isnan(x) && !isinf(x));",b6=`
  // 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;
    }
  }
`,yC="return exp(x);",bC="return exp(x) - 1.0;",w6=`if (x < 0.0) return NAN;
  return log(x);`,L6="return log(1.0 + x);",S6="return sqrt(x);",I6="return inversesqrt(x);",x6="return 1.0 / (1.0 + exp(-1.0 * x));",T6=`
  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;
`,A6=cr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,v6=cr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,N6=cr+`
  return atan(x);
`,C6=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,R6=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,O6=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,E6=cr+"return log(x + sqrt(x * x + 1.0));",D6=cr+`
  if (x < 1.0) return NAN;
  return log(x + sqrt(x * x - 1.0));`,k6=cr+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,F6=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${rw};
  float a1 = ${ow};
  float a2 = ${aw};
  float a3 = ${cw};
  float a4 = ${lw};
  float a5 = ${hw};

  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));
`,_6="return 1.0 / x;",W6="return float(!(x >= 1.0));",$6="return float(int(x));",mm="return x;";const U6="return x;",B6=`
  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;
`,wC=`
  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;
`,LC=`
  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;
`,SC=`
  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 lu{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 M6{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;const t=e.length,n=ls("rc",t),s=Et(t),i=TK(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:IC}=pw,P6=mw,z6=fw,G6=gw,V6=ip,Y6=1e-7,H6=1e-4,fm={};function q6(e){return e in fm||(fm[e]={}),fm[e]}function gm(e,t=!1){if(e==="linear")return t?U6:u6;if(e==="relu")return t?wC:uC;if(e==="elu")return t?SC:pC;if(e==="relu6")return t?LC:dC;if(e==="prelu")return t?G0:z0;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const j6=128,K6=600;function X6(){return C().global.screen==null?1024:C().global.screen.height*C().global.screen.width*window.devicePixelRatio*K6/1024/1024}const xC=1e3;class J6 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=Wi(C().getNumber("WEBGL_VERSION"));this.binaryCache=q6(C().getNumber("WEBGL_VERSION")),this.gpgpu=new A8(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 r6(this.gpgpu),this.numMBBeforeWarning=X6(),this.texData=new p(this,_s())}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 lu(a,mm):y=new st(a,mm);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=Yn());let m;if(s==="complex64"){const y=i.real.dataSync(),b=i.imag.dataSync();m=er(y,b)}else m=this.getValuesFromTexture(e);return h&&(this.downloadWaitMs+=Yn()-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 lu(s,mm):w=new st(s,mm);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,...ru(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=er(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(!Wj(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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=we(t);if(C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const y=this.decode(e),b=this.texData.get(y.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...ru(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(y),w}const o=C().getBool("WEBGL_PACK")&&s===!0,a=o?TS(t):t,c=o?new n8(a):new t8(a),h=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),d=this.texData.get(h.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(h),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=qi(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=qi(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=$x(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:Yn(),endMs:null}}endTimer(e){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=Yn(),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=_s().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=j6){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:_s().keep(e.clone()),imag:_s().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=bK(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=rb(e.shape,t,n);if(s||!i){const o=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new s6(n):new t6(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=ob(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=Cd(t,n,s);if(o.some(c=>c===0))return en([],o);const a=new i6(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Z8(e.shape,t):new J8(e.shape,t);return this.compileAndRun(n,[e])}concat(e,t){if(e[0].dtype==="complex64"){const a=e.map(h=>Do(h)),c=e.map(h=>$a(h));return Ti(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 z5(e.map(c=>c.shape),t);return this.compileAndRun(a,e)}const n=Er(e.map(a=>a.shape),t),s=e.map(a=>a.as2D(-1,we(a.shape.slice(t)))),i=new P5(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,mC,e.dtype);const n=new st(e.shape,mC);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>xC){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=Nn(e.dtype,t.dtype),d=new RS(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=Nn(e.dtype,t.dtype),y=i!=null,b=a!=null,w=o?gm(o,!0):null,L=new RS(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 B0(U0.REAL,e.shape,t.shape),c=new B0(U0.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=Nn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=gK(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,P0,e.dtype);const s=new hn(P0,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 k8(e.shape,t,n,s,i):new E8(e.shape,t,n,s,i);return this.compileAndRun(o,[e])}LRNGrad(e,t,n,s,i,o,a){const c=new D8(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=>zl(a)),o=Ze(e.shape,e.dtype,i);return z6(o,t)}const n=new l6(e.shape,t);return this.compileAndRun(n,[e])}pad(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new V8(e.shape,t,n):new G8(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 o8(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=Ah(e.shape,t,s),o=vh(i.length,t.length),a=Nh(e.shape,t,s),c=sw(n,t.length),h=iw(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=Ah(o.shape,t,s,!1),c=vh(a.length,t.length,!1),h=Nh(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=ah(i),a=Math.ceil(i/o),c={windowSize:o,inSize:i,batchSize:s,outSize:a},h=new iC(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=ah(i),a={windowSize:o,inSize:i,batchSize:s,outSize:Math.ceil(i/o)},c=new xK(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=ah(i),a=new a5(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){ts("sum",t,e.rank);const[n,s]=Rn(e.shape,t),i=we(s),o=e.as2D(-1,i),a=bd(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]=Rn(e.shape,t),o=we(i),a=e.as2D(-1,o),c=bd(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=IC.computeOutShape(o.shape,s,n),c=we([o.shape[s]]),h=o.as2D(-1,c),d=bd(e.dtype);let m=this.segOpCompute(h,"unsortedSegmentSum",t,d,n).reshape(a);return i!=null&&(m=Me(m,Xl(i))),m}segOpCompute(e,t,n,s,i){const o=e.shape[0],a=e.shape[1],c=IC.segOpComputeOptimalWindowSize(a,i),h={windowSize:c,inSize:a,batchSize:o,numSegments:i},d=new Q8(h,t),m=this.compileAndRun(d,[e,n],s);return m.shape[1]===i?m:(n=ph(0,i).tile([a/c]),this.segOpCompute(m,t,n,s,i))}argMinMaxReduce(e,t,n){const s=[t];if(ts("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.rank),!C().getBool("WEBGL_PACK_REDUCE")||e.rank<=2){const[i,o]=Rn(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 q0(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 q0(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,N5,"bool");const n=new hn(d5,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,C5,"bool");const n=new hn(p5,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,R5,"bool");const s=new hn(m5,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,O5,"bool");const n=new hn(f5,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,E5,"bool");const s=new hn(g5,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,D5,"bool");const n=new hn(y5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalNot(e){const t=new st(e.shape,W6);return this.compileAndRun(t,[e])}logicalAnd(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,k5,"bool");const n=new hn(b5,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,F5,"bool");const n=new hn(w5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}select(e,t,n){const s=new e6(e.rank,t.shape,t.rank);return this.compileAndRun(s,[e,t,n],Nn(t.dtype,n.dtype))}where(e){qa("tf.where() in webgl locks the UI thread. 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this.reduce(o,"all",o.dtype).reshape(n)}any(e,t){ts("any",t,e.rank);const[n,s]=Rn(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=h5,s="int32";if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,T5,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,NS);const n=Nn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=lK(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,NS,n);const s=new hn(NS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}packedUnaryOp(e,t,n){const s=new lu(e.shape,t);return this.compileAndRun(s,[e],n)}packedBinaryOp(e,t,n,s,i=!1){const o=new jr(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],Nn(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)=>Nn(o,a)),n=e.map(o=>o.shape),s=C().getBool("WEBGL_PACK"),i=s?new IK(e[0].shape,n):new SK(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,CS);const n=Nn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=wK(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,CS,e.dtype);const s=new hn(CS,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 jr(A5,e.shape,t.shape):new hn(u5,e.shape,t.shape),i=Nn(e.dtype,t.dtype);return this.compileAndRun(s,[e,t],i)}ceil(e){if(this.shouldExecuteOnCPU([e])){const n=hK(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,fC,e.dtype);const t=new st(e.shape,fC);return this.compileAndRun(t,[e])}floor(e){if(this.shouldExecuteOnCPU([e])){const n=pK(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,gC,e.dtype);const t=new st(e.shape,gC);return this.compileAndRun(t,[e])}sign(e){const t=new st(e.shape,m6);return this.compileAndRun(t,[e])}isNaN(e){const t=new st(e.shape,f6);return this.compileAndRun(t,[e],"bool")}isInf(e){const t=new st(e.shape,g6);return this.compileAndRun(t,[e],"bool")}isFinite(e){const t=new st(e.shape,y6);return this.compileAndRun(t,[e],"bool")}round(e){const t=new st(e.shape,b6);return this.compileAndRun(t,[e])}exp(e){if(this.shouldExecuteOnCPU([e])){const n=uK(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,yC,e.dtype);const t=new st(e.shape,yC);return this.compileAndRun(t,[e])}expm1(e){if(this.shouldExecuteOnCPU([e])){const n=dK(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,bC,e.dtype);const t=new st(e.shape,bC);return this.compileAndRun(t,[e])}softmax(e,t){const n=gt([t],e.shape),s=jn(e,n),i=On(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=mK(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,B6,e.dtype);const t=new st(e.shape,w6);return this.compileAndRun(t,[e])}log1p(e){const t=new st(e.shape,L6);return this.compileAndRun(t,[e])}sqrt(e){const t=new st(e.shape,S6);return this.compileAndRun(t,[e])}rsqrt(e){if(this.shouldExecuteOnCPU([e])){const n=yK(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}const t=new st(e.shape,I6);return this.compileAndRun(t,[e])}reciprocal(e){const t=new st(e.shape,_6);return this.compileAndRun(t,[e])}relu(e){let t;return C().getBool("WEBGL_PACK")?t=new lu(e.shape,wC):t=new st(e.shape,uC),this.compileAndRun(t,[e])}relu6(e){let t;return C().getBool("WEBGL_PACK")?t=new lu(e.shape,LC):t=new st(e.shape,dC),this.compileAndRun(t,[e])}prelu(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(G0,e.shape,t.shape):new hn(z0,e.shape,t.shape);return this.compileAndRun(n,[e,t])}elu(e){if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,SC,e.dtype);const t=new st(e.shape,pC);return this.compileAndRun(t,[e])}eluDer(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(v5,e.shape,t.shape):new hn(x5,e.shape,t.shape);return this.compileAndRun(n,[e,t])}selu(e){const t=new st(e.shape,d6);return this.compileAndRun(t,[e])}int(e){const t=new st(e.shape,$6);return this.compileAndRun(t,[e],"int32")}clip(e,t,n){let s;C().getBool("WEBGL_PACK_CLIP")?s=new B5(e.shape):s=new U5(e.shape);const i=s.getCustomSetupFunc(t,n);return this.compileAndRun(s,[e],null,i)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){const n=cK(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,hC,e.dtype);const t=new st(e.shape,hC);return this.compileAndRun(t,[e])}complexAbs(e){const t=this.texData.get(e.dataId),n=new M5(e.shape),s=[this.makeComplexComponentTensorInfo(e,t.complexTensors.real),this.makeComplexComponentTensorInfo(e,t.complexTensors.imag)];return this.compileAndRun(n,s)}sigmoid(e){const t=new st(e.shape,x6);return this.compileAndRun(t,[e])}softplus(e){const t=new st(e.shape,T6);return this.compileAndRun(t,[e])}asin(e){const t=new st(e.shape,A6);return this.compileAndRun(t,[e])}acos(e){const t=new st(e.shape,v6);return this.compileAndRun(t,[e])}atan(e){const t=new st(e.shape,N6);return this.compileAndRun(t,[e])}sinh(e){const t=new st(e.shape,C6);return this.compileAndRun(t,[e])}cosh(e){const t=new st(e.shape,R6);return this.compileAndRun(t,[e])}tanh(e){const t=new st(e.shape,O6);return this.compileAndRun(t,[e])}asinh(e){const t=new st(e.shape,E6);return this.compileAndRun(t,[e])}acosh(e){const t=new st(e.shape,D6);return this.compileAndRun(t,[e])}atanh(e){const t=new st(e.shape,k6);return this.compileAndRun(t,[e])}erf(e){const t=new st(e.shape,F6);return this.compileAndRun(t,[e])}step(e,t){const n=new st(e.shape,p6(t));return this.compileAndRun(n,[e])}conv2dByMatMul(e,t,n,s,i,o){const a=e.shape,c=this.texData.get(e.dataId),h=n.inChannels,d=a[0]*a[1]*a[2],m=n.outChannels,y=n.dataFormat==="channelsLast",b=!1,w=!1,L=(d===1||m===1)&&h>xC,T=a[2]%2!==0&&!!c.isPacked;if(L||!C().getBool("WEBGL_LAZILY_UNPACK")||!C().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!T){const B=y?a[0]*a[1]*a[2]:a[0]*a[2]*a[3],$=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(cm(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);const D=K(t,[1,n.inChannels,n.outChannels]),F=this.fusedBatchMatMul({a:N,b:D,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o}),_=this.texData.get(F.dataId);return k(_.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=E,_.shape=n.outShape,_s().makeTensorFromDataId(F.dataId,n.outShape,F.dtype)}conv2dWithIm2Row(e,t,n,s,i,o){const{filterWidth:a,filterHeight:c,inChannels:h,outWidth:d,outHeight:m,dataFormat:y}=n,b=y==="channelsLast",w=a*c*h,L=m*d,T=[w,L],A=!0,N=!1,E=e.squeeze([0]),D=t.reshape([1,w,-1]),F=new O8(T,E.shape,n),_=this.compileAndRun(F,[E]).reshape([1,T[0],T[1]]),B=s!=null,$=o!=null,H=i?gm(i,!0):null,q=new RS(_.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?gm(i,!1):null,d=new V0(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 V0(n);return this.compileAndRun(s,[e,t])}conv2dDerInput(e,t,n){const s=new V5(n);return this.compileAndRun(s,[e,t])}conv2dDerFilter(e,t,n){const s=new G5(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?gm(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 H0(n,d,c,m),this.compileAndRun(y,h)):(y=new Y0(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 H0(n),this.compileAndRun(s,[e,t])):(s=new Y0(n),this.compileAndRun(s,[e,t]))}depthwiseConv2DDerInput(e,t,n){const s=new j5(n);return this.compileAndRun(s,[e,t])}depthwiseConv2DDerFilter(e,t,n){const s=new q5(n);return this.compileAndRun(s,[e,t])}conv3d(e,t,n){const s=new K5(n);return this.compileAndRun(s,[e,t])}conv3dDerInput(e,t,n){const s=new H5(n);return this.compileAndRun(s,[e,t])}conv3dDerFilter(e,t,n){const s=new Y5(n);return this.compileAndRun(s,[e,t])}cast(e,t){return BA(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 OS(t,"avg",!1);return this.compileAndRun(n,[e],"float32")}avgPool3dBackprop(e,t,n){const s=new l5(n);return this.compileAndRun(s,[e],t.dtype)}maxPool3d(e,t){const n=new OS(t,"max",!1);return this.compileAndRun(n,[e],"float32")}maxPool3dBackprop(e,t,n,s){const i=!0,o=new OS(s,"max",i),a=this.compileAndRun(o,[t]),c=new _8(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 j8(e.shape,t,n,s):new q8(e.shape,t,n,s);return this.compileAndRun(i,[e],"float32")}resizeBilinearBackprop(e,t,n){const s=new H8(e,t,n);return this.compileAndRun(s,[e])}resizeNearestNeighbor(e,t,n,s){const i=new X8(e.shape,t,n,s);return this.compileAndRun(i,[e])}resizeNearestNeighborBackprop(e,t,n){const s=new K8(e,t,n);return this.compileAndRun(s,[e])}multinomial(e,t,n,s){const i=t?e:Wo(e),o=i.shape[0],a=i.shape[1],c=new W8(o,a,n),h=c.getCustomSetupFunc(s);return this.compileAndRun(c,[i],"int32",h)}oneHot(e,t,n,s){const i=new $8(e.size,t,n,s);return this.compileAndRun(i,[e])}diag(e){const t=new e8(e.size);return this.compileAndRun(t,[e])}cropAndResize(e,t,n,s,i,o){const a=new X5(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 Q5(m,t,n);return this.compileAndRun(y,[e])}split(e,t,n){return P6(e,t,n)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=Da(t,e,n),h=[c/o,o],d=e.reshape([i,s]),m=t.reshape([i,o]);if(c===0)return MA(en([]),n);const y=Ne(0),b=new oC(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}=Da(t,e,n),h=!1,d=new oC(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 J0(X0.REAL,e.shape,t),i=new J0(X0.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]=vd(e,t),h=t.reshape([o,s]),d=e.reshape([e.size/a,a]),m=new c8(s,c,[o,a]),y=this.compileAndRun(m,[d,h]);return y.reshape(i)}fill(e,t,n){if(n=n||Ta(t),n==="string"){const s=wo(n,we(e));return s.fill(t),_s().makeTensor(s,e,n,this)}else{const s=new r8(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 dw(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 _s().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new M6(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new U8(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){const n=[hc(e.shape),...uc(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},i=[hc(t),...uc(t)],o=new rC(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=TS(s);let a;n?a=new Z5(o):a=new J5(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===su.DENSE){const L=ru(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&&!cm(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=R8(e,h,d),y=this.getAndSaveBinary(m,()=>N8(this.gpgpu,e,h,d)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),C8(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 _s().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?Y6:H6}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=Yn());let m=t.texShape;if(m==null&&(m=eK(n,c),t.texShape=m),i!=null){const y=TS(n);let b,w=m[1],L=m[0];const T=i instanceof Uint8Array;c?([w,L]=lc(m[0],m[1]),b=new i8(y,[L,w],T)):b=new s8(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+=Yn()-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=Z6(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]*ky(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 Z6(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 Q6="2.6.0";function eX(){C().set("WEBGL_FORCE_F16_TEXTURES",!0)}Gy()&&hb("webgl",()=>new J6,2);const Lee={forceHalfFloat:eX},TC="if (isnan(x)) return x;",tX=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,nX=`
  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 ym(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 DS(e,t,n,s){return({inputs:i,backend:o})=>{const{a,b:c}=i,h=o,d=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(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 sX=tX+`
  return atan(a, b);
`,iX=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+nX+`
  return result;
`,rX=DS(sX,iX),oX={kernelName:Si,backendName:"webgl",kernelFunc:rX};function kS(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 aX={kernelName:yl,backendName:"webgl",kernelFunc:kS};function cX(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;ou(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 kS({inputs:{x:i},backend:n});const y=new cu(m,"avg",!1);return n.runWebGLProgram(y,[i],"float32")}const lX={kernelName:Qs,backendName:"webgl",kernelFunc:cX};function hX(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;ou([i,o],"avgPoolBackprop");const{filterSize:c,strides:h,pad:d}=s,m=Fn(a.shape,c,h,1,d),y=new c5(m);return n.runWebGLProgram(y,[i],a.dtype)}const uX={kernelName:ya,backendName:"webgl",kernelFunc:hX};class dX{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 pX{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 mX=({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 pX(s.shape,i.shape,o.shape,m,y,h):new dX(s.shape,i.shape,o.shape,m,y,h),w=t.runWebGLProgram(b,d,d[0].dtype);return w},fX={kernelName:gl,backendName:"webgl",kernelFunc:mX};const gX=TC+`
  return cos(x);
`,yX=ym(gX),bX={kernelName:ba,backendName:"webgl",kernelFunc:yX};const wX=`
if (a == b) {
  return 1.0;
};
return a / b;`,LX=`
  // 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;
`,SX=DS(wX,LX,!0),IX={kernelName:wa,backendName:"webgl",kernelFunc:SX};class xX{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 < ${t}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}}const TX={kernelName:Ju,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,s=t,i=new xX(n.shape),o=s.runWebGLProgram(i,[n],n.dtype);return o}};class AX{constructor(e){this.variableNames=["A"];const t=Wn(),[n,s]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${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 vX{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);
          }
        }

        ${t.output} = result;
      }
    `}}const NX={kernelName:ld,backendName:"webgl",kernelFunc:CX};let gc;function CX(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:i}=t;const{numChannels:o}=s,a=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,c=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[h,d]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[d,h],y=[d,h,o];(c||a)&&(gc==null&&(gc=document.createElement("canvas").getContext("2d")),gc.canvas.width=h,gc.canvas.height=d,gc.drawImage(i,0,0,h,d),i=gc.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=As.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=C().getBool("WEBGL_PACK")?new vX(y):new AX(y),L=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),L}function RX(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const n=t.length?t[t.length-1].outSize:e[1],s=ah(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function OX(e,t,n,s){const i=RX(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:h,outSize:d}=i[a],m=new iC({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d},n),y=o;o=s.runWebGLProgram(m,[o],t),y.dataId!==e.dataId&&s.disposeData(y.dataId)}return o}function EX(e,t,n){const s=[hc(e.shape),...uc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[hc(t),...uc(t)],a=new rC(o,s),c=!0,h=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:h.dataId,shape:t,dtype:h.dtype}}function FS(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=we(i.shape),h=md(o,c),d=we(h);k(c===d,()=>`The new shape (${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&&!cm(i.shape,h)&&!(m.texture!==null&&cm(m.shape,h))?EX(i,h,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:h,dtype:i.dtype})}const DX={kernelName:Nl,backendName:"webgl",kernelFunc:FS};function kX(e,t,n,s){const i=we(t),o=we(e.shape),a=o/i,c=FS({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),h=OX(c,e.dtype,"max",s),d=FS({inputs:{x:h},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(h),d}class FX{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=_X(t);this.userCode=`
    void main() {
      ${s} resRC = getOutputCoords();
      setOutput(getA(${i}));
    }
    `}}function _X(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 WX{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 ${this.rank} is not yet supported.`);const s=Et(this.rank),i=k0("rc",this.rank),o=new Array(this.rank);for(let d=0;d<t.length;d++)o[t[d]]=i[d];const a=`vec2(${o.slice(-2).join()})`,c=`++${i[this.rank-1]} < ${n[this.rank-1]}`,h=`getChannel(getA(${o.join()}), ${a})`;this.userCode=`
    void main() {
      ${s} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${h};
      if(${c}) {
        result[1] = ${h};
      }
      --${i[this.rank-1]};
      if(++${i[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${h};
        if(${c}) {
          result[3] = ${h};
        }
      }
      setOutput(result);
    }
    `}}function AC(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new WX(e.shape,t):new FX(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}const $X={kernelName:xl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n,c=s.shape.length,h=gt(i,s.shape);let d=h;const m=kn(d,c),y=m!=null,b=a.shouldExecuteOnCPU([s]);let w=s;if(y){if(b){const E=a.texData.get(w.dataId),D=E.values,F=new Array(c);for(let $=0;$<F.length;$++)F[$]=s.shape[m[$]];const _=D0(D,s.shape,s.dtype,m,F);w=a.makeTensorInfo(F,s.dtype);const B=a.texData.get(w.dataId);B.values=_}else w=AC(s,m,a);d=ws(d.length,c)}ts("max",d,c);const[L,T]=Rn(w.shape,d);let A=L;o&&(A=On(L,h));let N;if(b){const E=a.texData.get(w.dataId),D=E.values,F=fK(D,we(T),A,s.dtype);N=a.makeTensorInfo(A,s.dtype);const _=a.texData.get(N.dataId);_.values=F}else N=kX(w,T,A,a);return y&&a.disposeIntermediateTensorInfo(w),N}};function UX(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;ou(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. 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 kS({inputs:{x:i},backend:n});const y=new cu(m,"max",!1);return n.runWebGLProgram(y,[i],i.dtype)}const BX={kernelName:Tl,backendName:"webgl",kernelFunc:UX};function MX(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o,output:a}=t,c=o;ou([o,a],"maxPoolBackprop");const{filterSize:h,strides:d,pad:m,dimRoundingMode:y}=s,b=Fn(c.shape,h,d,1,m,y),w=!0,L=new cu(b,"max",w),T=n.runWebGLProgram(L,[c],c.dtype),A=new F8(b),N=n.runWebGLProgram(A,[i,T],c.dtype);return n.disposeIntermediateTensorInfo(T),N}const PX={kernelName:Qu,backendName:"webgl",kernelFunc:MX};function zX(e,t,n,s){let i=new cu(n,"max",!1);const o=s.runWebGLProgram(i,[e],"float32");i=new cu(n,"max",!0,!0,t);const a=s.runWebGLProgram(i,[e],"float32");return[o,a]}const GX={kernelName:ed,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,h=n;k(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);const d=[1,1];k(rn(o,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);const m=Fn(s.shape,i,o,d,a),[y,b]=zX(s,c,m,h);return[y,b]}};const VX={kernelName:iy,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=n,h=t,d=h.readSync(s.dataId),m=h.readSync(i.dataId),y=o,b=a,w=c;return up(d,m,y,b,w)}};const YX=dp,HX={kernelName:nd,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,padToMaxOutputSize:h}=n,d=t,m=d.readSync(s.dataId),y=d.readSync(i.dataId),{selectedIndices:b,validOutputs:w}=YX(m,y,o,a,c,h);return[b,w]}};const qX=pp,jX={kernelName:sd,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}=n,d=t,m=d.readSync(s.dataId),y=d.readSync(i.dataId),b=o,w=a,L=c,T=h,{selectedIndices:A,selectedScores:N}=qX(m,y,b,w,L,T);return[A,N]}};class KX{constructor(e,t,n,s){this.variableNames=["Image"],this.outputShape=[];const i=e[1],o=e[2],a=Math.sin(t).toFixed(3),c=Math.cos(t).toFixed(3);this.outputShape=e;const[h,d]=nw(s,i,o),m=h.toFixed(3),y=d.toFixed(3);let b="";typeof n=="number"?b=`float outputValue = ${n.toFixed(2)};`:b=`
        vec3 fill = vec3(${n.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - ${m}) * ${c} - (float(y) - ${y}) * ${a};
          float coordYFloat = (float(x) - ${m}) * ${a} + (float(y) - ${y}) * ${c};
          int coordX = int(round(coordXFloat + ${m}));
          int coordY = int(round(coordYFloat + ${y}));
          ${b}
          if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${i}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}}const XX={kernelName:hd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:s}=e,{radians:i,fillValue:o,center:a}=t,c=n,h=new KX(s.shape,i,o,a),d=c.runWebGLProgram(h,[s],s.dtype);return d}};const JX=TC+`
  return sin(x);
`,ZX=ym(JX),QX={kernelName:La,backendName:"webgl",kernelFunc:ZX};const e7="return x * x;",t7=ym(e7),n7={kernelName:ad,backendName:"webgl",kernelFunc:t7};const vC="return (a - b) * (a - b);",s7=DS(vC,vC),i7={kernelName:Sa,backendName:"webgl",kernelFunc:s7};const r7="return tan(x);",o7=ym(r7),a7={kernelName:Ia,backendName:"webgl",kernelFunc:o7};const c7={kernelName:Ul,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{perm:i}=t,o=n,a=s.shape.length,c=new Array(a);for(let d=0;d<c.length;d++)c[d]=s.shape[i[d]];let h;if(o.shouldExecuteOnCPU([s])){const d=o.texData.get(s.dataId),m=d.values,y=D0(m,s.shape,s.dtype,i,c);h=o.makeTensorInfo(c,s.dtype);const b=o.texData.get(h.dataId);b.values=y}else h=AC(s,i,o);return h}};function l7(e){const{inputs:t,attrs:n,backend:s}=e,{axis:i}=n,{x:o}=t;ou(o,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const a=s.readSync(o.dataId),{outputValues:c,outputShape:h,indices:d}=LK(a,i,o.shape,o.dtype);return[s.makeTensorInfo(h,o.dtype,c),s.makeTensorInfo([d.length],"int32",d)]}const h7={kernelName:cd,backendName:"webgl",kernelFunc:l7};const u7=[oX,lX,uX,fX,bX,IX,TX,NX,aX,$X,BX,PX,GX,VX,HX,jX,DX,XX,QX,n7,i7,a7,c7,h7];for(const e of u7)dd(e);const d7="2.6.0";const 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At({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 Oc extends At{constructor(r,l,u,p,g=!1){super({left:r,top:l,right:u,bottom:p},g)}}class ta{constructor(r,l,u,p,g){this._imageDims=new Zn(g.width,g.height),this._score=r,this._classScore=l,this._className=u,this._box=new At(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 At(this._box).rescale(this.imageDims.reverse())}forSize(r,l){return new ta(this.score,this.classScore,this.className,this.relativeBox,{width:r,height:l})}}class Wt extends 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Mi.tidy(()=>{const[u,p,g]=l,f=Mi.fill([...r.shape.slice(0,3),1],u,"float32"),I=Mi.fill([...r.shape.slice(0,3),1],p,"float32"),S=Mi.fill([...r.shape.slice(0,3),1],g,"float32"),x=Mi.concat([f,I,S],3);return Mi.sub(r,x)})}const io=Xe(Je());function Nm(r,l=!1){return io.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,io.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=>io.cast(U,"float32"));return io.concat(C,I)})}function MC(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 xc(r){return 1/(1+Math.exp(-r))}function PC(r){return Math.log(r/(1-r))}class Cc extends At{constructor(r,l,u,p,g=!1){super({x:r,y:l,width:u,height:p},g)}}const v7=.5,N7=.43,C7=.45;class Ns{constructor(r,l,u=new qe(0,0)){const{width:p,height:g}=l;this._imgDims=new Zn(p,g),this._shift=u,this._positions=r.map(f=>f.mul(new qe(p,g)).add(u))}get shift(){return new qe(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(r=>r.sub(this._shift).div(new qe(this.imageWidth,this.imageHeight)))}forSize(r,l){return new this.constructor(this.relativePositions,{width:r,height:l})}shiftBy(r,l){return new this.constructor(this.relativePositions,this._imgDims,new qe(r,l))}shiftByPoint(r){return this.shiftBy(r.x,r.y)}align(r,l={}){if(r){const g=r instanceof Wt?r.box.floor():new At(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/C7),S=ia(r),x=Math.floor(Math.max(0,S.x-v7*I)),v=Math.floor(Math.max(0,S.y-N7*I));return new Cc(x,v,Math.min(I,this.imageWidth+x),Math.min(I,this.imageHeight+v))}alignMinBbox(r){const l=Rm(this.positions);return l.pad(l.width*r,l.height*r)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class f2 extends Ns{getRefPointsForAlignment(){const r=this.positions;return[r[0],r[1],ia([r[3],r[4]])]}}class Rc extends Ns{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(ia)}}class Lu{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?` (${sa(this.distance)})`:""}`}}class wu extends At{static assertIsValidLabeledBox(r,l){if(At.assertIsValidBox(r,l),!fi(r.label))throw new Error(`${l} - expected property label (${r.label}) to be a number`)}constructor(r,l){super(r);this._label=l}get label(){return this._label}}class so{constructor(r,l){if(!(typeof r=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(l)||l.some(u=>!(u instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=r,this._descriptors=l}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(r=>Array.from(r))}}static fromJSON(r){const l=r.descriptors.map(u=>new Float32Array(u));return new so(r.label,l)}}class m2 extends wu{static assertIsValidPredictedBox(r,l){if(wu.assertIsValidLabeledBox(r,l),!Ec(r.score)||!Ec(r.classScore))throw new Error(`${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 mi(r){return r.detection instanceof Wt}function Zr(r,l){const u={detection:l};return Object.assign({},r,u)}function qS(){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 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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 mt={getEnv:R7,setEnv:JS,initialize:ZS,createBrowserEnv:qS,createFileSystem:Jm,createNodejsEnv:jS,monkeyPatch:O7,isBrowser:KS,isNodejs:XS.isNodejs};ZS();function eo(r){return!mt.isNodejs()&&typeof r=="string"?document.getElementById(r):r}function Un(r){const{Canvas:l,CanvasRenderingContext2D:u}=mt.getEnv();if(r instanceof u)return r;const p=eo(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 Pi;(function(r){r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Pi||(Pi={}));class Zm{constructor(r={}){const{anchorPosition:l,backgroundColor:u,fontColor:p,fontSize:g,fontStyle:f,padding:I}=r;this.anchorPosition=l||Pi.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 Dc{constructor(r,l,u={}){this.text=typeof r=="string"?[r]:r instanceof Dc?r.text:r,this.anchor=l,this.options=new Zm(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===Pi.BOTTOM_RIGHT||u===Pi.TOP_RIGHT,g=u===Pi.BOTTOM_LEFT||u===Pi.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=eo(r),u=Un(l),{backgroundColor:p,fontColor:g,fontSize:f,fontStyle:I,padding:S}=this.options;u.font=`${f}px ${I}`;const x=this.measureWidth(u),v=this.measureHeight();u.fillStyle=p;const O=this.getUpperLeft(u,l);u.fillRect(O.x,O.y,x,v),u.fillStyle=g,this.text.forEach((C,U)=>{const G=S+O.x,ne=S+O.y+(U+1)*f;u.fillText(C,G,ne)})}}class b2{constructor(r={}){const{boxColor:l,lineWidth:u,label:p,drawLabelOptions:g}=r;this.boxColor=l||"rgba(0, 0, 255, 1)",this.lineWidth=u||2,this.label=p;const f={anchorPosition:Pi.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Zm(Object.assign({},f,g))}}class QS{constructor(r,l={}){this.box=new At(r),this.options=new b2(l)}draw(r){const l=Un(r),{boxColor:u,lineWidth:p}=this.options,{x:g,y:f,width:I,height:S}=this.box;l.strokeStyle=u,l.lineWidth=p,l.strokeRect(g,f,I,S);const{label:x}=this.options;x&&new Dc([x],{x:g-p/2,y:f},this.options.drawLabelOptions).draw(r)}}function E7(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const g=p instanceof Wt?p.score:mi(p)?p.detection.score:void 0,f=p instanceof Wt?p.box:mi(p)?p.detection.box:new At(p),I=g?`${sa(g)}`:void 0;new QS(f,{label:I}).draw(r)})}function vc(r){const{Image:l,Video:u}=mt.getEnv();return r instanceof l&&r.complete||r instanceof u&&r.readyState>=3}function qm(r){return new Promise((l,u)=>{if(r instanceof mt.getEnv().Canvas||vc(r))return l(null);function p(f){if(!f.currentTarget)return;f.currentTarget.removeEventListener("load",p),f.currentTarget.removeEventListener("error",g),l(f)}function 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To get the original bytes, call tensor.bytes().")}return r}async bytes(){this.throwIfDisposed();const r=await zi().read(this.dataId);return this.dtype==="string"?r:new Uint8Array(r.buffer)}dispose(){if(this.isDisposed)return;zi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(r=!1){return Uc.print(this,r)}clone(){return this.throwIfDisposed(),Uc.clone(this)}toString(r=!1){const l=this.dataSync();return aO(l,this.shape,this.dtype,r)}cast(r){return this.throwIfDisposed(),Uc.cast(this,r)}variable(r=!0,l,u){return this.throwIfDisposed(),zi().makeVariable(this,r,l,u)}}Object.defineProperty(xn,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});class ng extends xn{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(!ra(r.shape,this.shape))throw new Error(`shape of the new value (${r.shape}) and previous value (${this.shape}) must match`);zi().disposeTensor(this),this.dataId=r.dataId,zi().incRef(this,null)}dispose(){zi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(ng,Symbol.hasInstance,{value:r=>r instanceof xn&&r.assign!=null&&r.assign instanceof Function});var pO;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(pO||(pO={}));var dI;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(dI||(dI={}));var pI;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(pI||(pI={}));var mI;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(mI||(mI={}));var fI;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(fI||(fI={}));const q7={float32:mI,int32:dI,bool:pI,complex64:fI};function mO(r,l){if(r==="string"||l==="string"){if(r==="string"&&l==="string")return"string";throw new Error(`Can not upcast ${r} with ${l}`)}return q7[r][l]}function ft(r,l){if(r.dtype===l.dtype)return[r,l];const u=mO(r.dtype,l.dtype);return[r.cast(u),l.cast(u)]}function sg(r){const l=[],u=new Set;return fO(r,l,u),l}function fO(r,l,u){if(r==null)return;if(r instanceof xn){l.push(r);return}if(!j7(r))return;const p=r;for(const g in p){const f=p[g];u.has(f)||(u.add(f),fO(f,l,u))}}function j7(r){return Array.isArray(r)||typeof r=="object"}class gO{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const r in this.registeredVariables)this.registeredVariables[r].dispose()}}class Nu{constructor(r){this.ENV=r,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new gO}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const u=r[l],p=await this.initializeBackend(u).success;if(p){await this.setBackend(u);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 sO(this.backendInstance),!0}setupRegisteredKernels(){const r=oI(this.backendName);r.forEach(l=>{l.setupFunc!=null&&l.setupFunc(this.backendInstance)})}disposeRegisteredKernels(r){const l=oI(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 w2)&&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 Nu.nextTensorId++}nextVariableId(){return Nu.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,Fc,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=Jf(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=rI(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"&&Su(r[0])&&(g=r.map(S=>nO(S)));const f=p.write(g,l,u),I=new xn(l,u,f,this.nextTensorId());if(this.incRef(I,p),u==="string"){const S=this.state.tensorInfo.get(f),x=eO(g);this.state.numBytes+=x-S.bytes,S.bytes=x}return I}makeTensorFromDataId(r,l,u,p){u=u||"float32";const g=new xn(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 ng(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*QR(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 ng||this.track(r)}disposeTensor(r){if(!this.state.tensorInfo.has(r.dataId))return;this.state.numTensors--,r.dtype==="string"&&this.state.numStringTensors--;const l=this.state.tensorInfo.get(r.dataId),u=l.refCount;u<=1?(r.dtype!=="complex64"&&(this.state.numBytes-=l.bytes),this.state.numDataBuffers--,l.backend.disposeData(r.dataId),this.state.tensorInfo.delete(r.dataId)):this.state.tensorInfo.get(r.dataId).refCount--}disposeVariables(){for(const r in this.state.registeredVariables){const l=this.state.registeredVariables[r];this.disposeVariable(l)}}disposeVariable(r){this.disposeTensor(r),this.state.registeredVariables[r.name]!=null&&delete this.state.registeredVariables[r.name]}memory(){const r=this.backend.memory();return r.numTensors=this.state.numTensors,r.numDataBuffers=this.state.numDataBuffers,r.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(r.unreliable=!0,r.reasons==null&&(r.reasons=[]),r.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),r}async profile(r){this.state.profiling=!0;const l=this.state.numBytes,u=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await r(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(p=>p.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-l,this.state.activeProfile.newTensors=this.state.numTensors-u;for(const p of this.state.activeProfile.kernels)p.kernelTimeMs=await p.kernelTimeMs,p.extraInfo=await p.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(r,l,u,p,g,f){const I={id:this.state.nextTapeNodeId++,kernelName:r,inputs:l,outputs:u,saved:g},S=rI(r);S!=null&&(p=S.gradFunc),p!=null&&(I.gradient=x=>(x=x.map((v,O)=>{if(v==null){const C=u[O],U=oa(C.size,C.dtype);return this.makeTensor(U,C.shape,C.dtype)}return v}),p(x.length>1?x:x[0],g,f))),this.state.activeTape.push(I)}keep(r){return r.kept=!0,r}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(r){const l={track:[],name:"unnamed scope",id:this.state.nextScopeId++};r&&(l.name=r),this.state.scopeStack.push(l),this.state.activeScope=l}endScope(r){const l=sg(r),u=new Set(l.map(g=>g.id));for(let g=0;g<this.state.activeScope.track.length;g++){const f=this.state.activeScope.track[g];!f.kept&&!u.has(f.id)&&f.dispose()}const p=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],l.forEach(g=>{!g.kept&&g.scopeId===p.id&&this.track(g)})}gradients(r,l,u,p=!1){if(Z(l.length>0,()=>"gradients() received an empty list of xs."),u!=null&&u.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${u.dtype}'`);const g=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",r));Z(g instanceof xn,()=>"The result y returned by f() must be a tensor.");const f=iO(this.state.activeTape,l,g);if(!p&&f.length===0&&l.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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In{constructor(r){this._name=r;this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(r){const{obj:l,objProp:u}=this.traversePropertyPath(r);return l[u]}reassignParamFromPath(r,l){const{obj:u,objProp:p}=this.traversePropertyPath(r);u[p].dispose(),u[p]=l}getParamList(){return this._paramMappings.map(({paramPath:r})=>({path:r,tensor:this.getParamFromPath(r)}))}getTrainableParams(){return this.getParamList().filter(r=>r.tensor instanceof br.Variable)}getFrozenParams(){return this.getParamList().filter(r=>!(r.tensor instanceof br.Variable))}variable(){this.getFrozenParams().forEach(({path:r,tensor:l})=>{this.reassignParamFromPath(r,l.variable())})}freeze(){this.getTrainableParams().forEach(({path:r,tensor:l})=>{const u=br.tensor(l.dataSync());l.dispose(),this.reassignParamFromPath(r,u)})}dispose(r=!0){this.getParamList().forEach(l=>{if(r&&l.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${l.path}`);l.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:r})=>Array.from(r.dataSync())).reduce((r,l)=>r.concat(l)))}async load(r){if(r instanceof Float32Array){this.extractWeights(r);return}await this.loadFromUri(r)}async loadFromUri(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const l=await zm(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}=mt.getEnv(),{manifestUri:u,modelBaseUri:p}=Lg(r,this.getDefaultModelName()),g=x=>Promise.all(x.map(v=>l(v).then(O=>O.buffer))),f=br.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 br.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${r}`);return{obj:u,objProp:p}}}const Hc=Xe(Je());function es(r,l,u){return Hc.tidy(()=>{let p=Hc.separableConv2d(r,l.depthwise_filter,l.pointwise_filter,u,"same");return p=Hc.add(p,l.bias),p})}const Dt=Xe(Je());function Sg(r,l,u=!1){return Dt.tidy(()=>{const p=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,[2,2],"same"),l.conv0.bias):es(r,l.conv0,[2,2])),g=es(p,l.conv1,[1,1]),f=Dt.relu(Dt.add(p,g)),I=es(f,l.conv2,[1,1]);return Dt.relu(Dt.add(p,Dt.add(g,I)))})}function Wu(r,l,u=!1,p=!0){return Dt.tidy(()=>{const g=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,p?[2,2]:[1,1],"same"),l.conv0.bias):es(r,l.conv0,p?[2,2]:[1,1])),f=es(g,l.conv1,[1,1]),I=Dt.relu(Dt.add(g,f)),S=es(I,l.conv2,[1,1]),x=Dt.relu(Dt.add(g,Dt.add(f,S))),v=es(x,l.conv3,[1,1]);return Dt.relu(Dt.add(g,Dt.add(f,Dt.add(S,v))))})}const uo=Xe(Je());function ma(r,l,u="same",p=!1){return uo.tidy(()=>{const g=uo.add(uo.conv2d(r,l.filters,[1,1],u),l.bias);return p?uo.relu(g):g})}function zn(r,l){Object.keys(r).forEach(u=>{l.some(p=>p.originalPath===u)||r[u].dispose()})}const Ig=Xe(Je());function qc(r,l){return function(u,p,g,f){const I=Ig.tensor4d(r(u*p*g*g),[g,g,u,p]),S=Ig.tensor1d(r(p));return l.push({paramPath:`${f}/filters`},{paramPath:`${f}/bias`}),{filters:I,bias:S}}}const xg=Xe(Je());function Tg(r,l){return function(u,p,g){const f=xg.tensor2d(r(u*p),[u,p]),I=xg.tensor1d(r(p));return l.push({paramPath:`${g}/weights`},{paramPath:`${g}/bias`}),{weights:f,bias:I}}}class tx{constructor(r,l,u){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=u}}const $u=Xe(Je());function jc(r,l){return function(u,p,g){const f=$u.tensor4d(r(3*3*u),[3,3,u,1]),I=$u.tensor4d(r(u*p),[1,1,u,p]),S=$u.tensor1d(r(p));return l.push({paramPath:`${g}/depthwise_filter`},{paramPath:`${g}/pointwise_filter`},{paramPath:`${g}/bias`}),new tx(f,I,S)}}function Kc(r){return function(l){const u=r(`${l}/depthwise_filter`,4),p=r(`${l}/pointwise_filter`,4),g=r(`${l}/bias`,1);return new tx(u,p,g)}}function ms(r,l){return function(u,p,g){const f=r[u];if(!na(f,p))throw new Error(`expected weightMap[${u}] to be a Tensor${p}D, instead have ${f}`);return l.push({originalPath:u,paramPath:g||u}),f}}function Gn(r){let l=r;function u(g){const f=l.slice(0,g);return l=l.slice(g),f}function p(){return l}return{extractWeights:u,getRemainingWeights:p}}function Ag(r,l){const u=qc(r,l),p=jc(r,l);function g(I,S,x,v=!1){const O=v?u(I,S,3,`${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 oD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Gn(r),{extractDenseBlock4Params:g}=Ag(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: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S,dense3:x}}}function vg(r){return function(l){const u=r(`${l}/filters`,4),p=r(`${l}/bias`,1);return{filters:u,bias:p}}}function Ng(r,l){const u=ms(r,l),p=vg(u),g=Kc(u);function f(S,x=!1){const v=x?p(`${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 aD(r){const l=[],{extractDenseBlock4Params:u}=Ng(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2"),dense3:u("dense3")};return zn(r,l),{params:p,paramMappings:l}}const po=Xe(Je());class Cg extends In{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return po.tidy(()=>{const u=po.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=Ys(u,p).div(po.scalar(255));let f=Wu(g,l.dense0,!0);return f=Wu(f,l.dense1),f=Wu(f,l.dense2),f=Wu(f,l.dense3),f=po.avgPool(f,[7,7],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Tt(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return aD(r)}extractParams(r){return oD(r)}}const Xc=Xe(Je());function Uu(r,l){return Xc.tidy(()=>Xc.add(Xc.matMul(r,l.weights),l.bias))}function cD(r,l,u){const p=[],{extractWeights:g,getRemainingWeights:f}=Gn(r),I=Tg(g,p),S=I(l,u,"fc");if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{paramMappings:p,params:{fc:S}}}function lD(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:p("fc")};return zn(r,l),{params:g,paramMappings:l}}function Rg(r){const l={},u={};return Object.keys(r).forEach(p=>{const g=p.startsWith("fc")?u:l;g[p]=r[p]}),{featureExtractorMap:l,classifierMap:u}}const hD=Xe(Je());class Og extends In{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return hD.tidy(()=>{const u=r instanceof hr?this.faceFeatureExtractor.forwardInput(r):r;return Uu(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 cD(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Rg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),lD(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 Bm=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class Qr{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);Bm.forEach((l,u)=>{this[l]=r[u]})}asSortedArray(){return Bm.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const Jc=Xe(Je());class Mm extends Og{constructor(r=new Cg){super("FaceExpressionNet",r)}forwardInput(r){return Jc.tidy(()=>Jc.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Tt(r))}async predictExpressions(r){const l=await Tt(r),u=await this.forwardInput(l),p=await Promise.all(Jc.unstack(u).map(async f=>{const I=await f.data();return f.dispose(),I}));u.dispose();const g=p.map(f=>new Qr(f));return l.isBatchInput?g:g[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function $m(r){return r.expressions instanceof Qr}function fu(r,l){const u={expressions:l};return Object.assign({},r,u)}function bZ(r,l,u=.1,p){const g=Array.isArray(l)?l:[l];g.forEach(f=>{const I=f instanceof Qr?f:$m(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=mi(f)?f.detection.box.bottomLeft:p||new qe(0,0),O=new Dc(x.map(C=>`${C.expression} (${sa(C.probability)})`),v);O.draw(r)})}function Jr(r){return mi(r)&&r.landmarks instanceof Ns&&r.unshiftedLandmarks instanceof Ns&&r.alignedRect instanceof Wt}function Jo(r,l){const{box:u}=r.detection,p=l.shiftBy(u.x,u.y),g=p.align(),{imageDims:f}=r.detection,I=new Wt(r.detection.score,g.rescale(f.reverse()),f),S={landmarks:p,unshiftedLandmarks:l,alignedRect:I};return Object.assign({},r,S)}class uD{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 dD{constructor(r,l={}){this.faceLandmarks=r,this.options=new uD(l)}draw(r){const l=Un(r),{drawLines:u,drawPoints:p,lineWidth:g,lineColor:f,pointSize:I,pointColor:S}=this.options;if(u&&this.faceLandmarks instanceof Rc&&(l.strokeStyle=f,l.lineWidth=g,ur(l,this.faceLandmarks.getJawOutline()),ur(l,this.faceLandmarks.getLeftEyeBrow()),ur(l,this.faceLandmarks.getRightEyeBrow()),ur(l,this.faceLandmarks.getNose()),ur(l,this.faceLandmarks.getLeftEye(),!0),ur(l,this.faceLandmarks.getRightEye(),!0),ur(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 wZ(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const g=p instanceof Ns?p:Jr(p)?p.landmarks:void 0;if(!g)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new dD(g).draw(r)})}const Im={};uu(Im,{AnchorPosition:()=>Pi,DrawBox:()=>QS,DrawBoxOptions:()=>b2,DrawFaceLandmarks:()=>dD,DrawFaceLandmarksOptions:()=>uD,DrawTextField:()=>Dc,DrawTextFieldOptions:()=>Zm,drawContour:()=>ur,drawDetections:()=>E7,drawFaceExpressions:()=>bZ,drawFaceLandmarks:()=>wZ});function LZ(r,l){const u=qc(r,l),p=jc(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 pD(r,l){const u=[],{extractWeights:p,getRemainingWeights:g}=Gn(r),{extractConvParams:f,extractSeparableConvParams:I,extractReductionBlockParams:S,extractMainBlockParams:x}=LZ(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={};Bi(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: ${g().length}`);return{paramMappings:u,params:{entry_flow:U,middle_flow:G,exit_flow:oe}}}function SZ(r,l){const u=ms(r,l),p=vg(u),g=Kc(u);function f(S){const x=g(`${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(`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}}function mD(r,l){const u=[],{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}=SZ(r,u),S=p("entry_flow/conv_in"),x=f("entry_flow/reduction_block_0"),v=f("entry_flow/reduction_block_1"),O={conv_in:S,reduction_block_0:x,reduction_block_1:v},C={};Bi(l,0,1).forEach(te=>{C[`main_block_${te}`]=I(`middle_flow/main_block_${te}`)});const U=f("exit_flow/reduction_block"),G=g("exit_flow/separable_conv"),ne={reduction_block:U,separable_conv:G};return zn(r,u),{params:{entry_flow:O,middle_flow:C,exit_flow:ne},paramMappings:u}}const tn=Xe(Je());function fD(r,l,u){return tn.add(tn.conv2d(r,l.filters,u,"same"),l.bias)}function nx(r,l,u=!0){let p=u?tn.relu(r):r;return p=es(p,l.separable_conv0,[1,1]),p=es(tn.relu(p),l.separable_conv1,[1,1]),p=tn.maxPool(p,[3,3],[2,2],"same"),p=tn.add(p,fD(r,l.expansion_conv,[2,2])),p}function IZ(r,l){let u=es(tn.relu(r),l.separable_conv0,[1,1]);return u=es(tn.relu(u),l.separable_conv1,[1,1]),u=es(tn.relu(u),l.separable_conv2,[1,1]),u=tn.add(u,r),u}class gD extends In{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return tn.tidy(()=>{const u=tn.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=Ys(u,p).div(tn.scalar(256));let f=tn.relu(fD(g,l.entry_flow.conv_in,[2,2]));return f=nx(f,l.entry_flow.reduction_block_0,!1),f=nx(f,l.entry_flow.reduction_block_1),Bi(this._numMainBlocks,0,1).forEach(I=>{f=IZ(f,l.middle_flow[`main_block_${I}`])}),f=nx(f,l.exit_flow.reduction_block),f=tn.relu(es(f,l.exit_flow.separable_conv,[1,1])),f})}async forward(r){return this.forwardInput(await Tt(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return mD(r,this._numMainBlocks)}extractParams(r){return pD(r,this._numMainBlocks)}}function yD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Gn(r),g=Tg(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: ${p().length}`);return{paramMappings:l,params:{fc:{age:f,gender:I}}}}function bD(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:{age:p("fc/age"),gender:p("fc/gender")}};return zn(r,l),{params:g,paramMappings:l}}var Ui;(function(r){r.FEMALE="female",r.MALE="male"})(Ui||(Ui={}));const Hi=Xe(Je());class jm extends In{constructor(r=new gD(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Hi.tidy(()=>{const u=r instanceof hr?this.faceFeatureExtractor.forwardInput(r):r,p=Hi.avgPool(u,[7,7],[2,2],"valid").as2D(u.shape[0],-1),g=Uu(p,l.fc.age).as1D(),f=Uu(p,l.fc.gender);return{age:g,gender:f}})}forwardInput(r){return Hi.tidy(()=>{const{age:l,gender:u}=this.runNet(r);return{age:l,gender:Hi.softmax(u)}})}async forward(r){return this.forwardInput(await Tt(r))}async predictAgeAndGender(r){const l=await Tt(r),u=await this.forwardInput(l),p=Hi.unstack(u.age),g=Hi.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?Ui.MALE:Ui.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 yD(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Rg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),bD(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=Xe(Je());class Eg extends Og{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 Tt(r))}async detectLandmarks(r){const l=await Tt(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)=>Km(O)),x=I.filter((v,O)=>!Km(O));return new Rc(Array(68).fill(0).map((v,O)=>new qe(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 Ac extends Eg{constructor(r=new Cg){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function wD(r){const l=[],{extractDenseBlock3Params:u}=Ng(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return zn(r,l),{params:p,paramMappings:l}}function LD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Gn(r),{extractDenseBlock3Params:g}=Ag(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 mo=Xe(Je());class SD extends In{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return mo.tidy(()=>{const u=mo.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=Ys(u,p).div(mo.scalar(255));let f=Sg(g,l.dense0,!0);return f=Sg(f,l.dense1),f=Sg(f,l.dense2),f=mo.avgPool(f,[14,14],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Tt(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return wD(r)}extractParams(r){return LD(r)}}class Um extends Eg{constructor(r=new SD){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class h2 extends Ac{}const Dg=Xe(Je());function ID(r,l){return Dg.add(Dg.mul(r,l.weights),l.biases)}const Zc=Xe(Je());function sx(r,l,u,p,g="same"){const{filters:f,bias:I}=l.conv;let S=Zc.conv2d(r,f,u,g);return S=Zc.add(S,I),S=ID(S,l.scale),p?Zc.relu(S):S}function xD(r,l){return sx(r,l,[1,1],!0)}function ix(r,l){return sx(r,l,[1,1],!1)}function kg(r,l){return sx(r,l,[2,2],!0,"valid")}const gs=Xe(Je());function xZ(r,l){function u(S,x,v){const O=r(S),C=O.length/(x*v*v);if(YS(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 TD(r){const{extractWeights:l,getRemainingWeights:u}=Gn(r),p=[],{extractConvLayerParams:g,extractResidualLayerParams:f}=xZ(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: ${u().length}`);const rt={conv32_down:I,conv32_1:S,conv32_2:x,conv32_3:v,conv64_down:O,conv64_1:C,conv64_2:U,conv64_3:G,conv128_down:ne,conv128_1:te,conv128_2:oe,conv256_down:ge,conv256_1:fe,conv256_2:Ae,conv256_down_out:Te,fc:Ve};return{params:rt,paramMappings:p}}function TZ(r,l){const u=ms(r,l);function p(I){const S=u(`${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 AD(r){const l=[],{extractConvLayerParams:u,extractResidualLayerParams:p}=TZ(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"}),!VS(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 zn(r,l),{params:Te,paramMappings:l}}const Vn=Xe(Je());function bi(r,l){let u=xD(r,l.conv1);return u=ix(u,l.conv2),u=Vn.add(u,r),u=Vn.relu(u),u}function Bu(r,l){let u=kg(r,l.conv1);u=ix(u,l.conv2);let p=Vn.avgPool(r,2,2,"valid");const g=Vn.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=Vn.zeros(S);u=Vn.concat([u,x],1);const v=[...u.shape];v[2]=1;const O=Vn.zeros(v);u=Vn.concat([u,O],2)}return p=f?Vn.concat([p,g],3):p,u=Vn.add(p,u),u=Vn.relu(u),u}const ks=Xe(Je());class Tc extends In{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return ks.tidy(()=>{const u=ks.cast(r.toBatchTensor(150,!0),"float32"),p=[122.782,117.001,104.298],g=Ys(u,p).div(ks.scalar(256));let f=kg(g,l.conv32_down);f=ks.maxPool(f,3,2,"valid"),f=bi(f,l.conv32_1),f=bi(f,l.conv32_2),f=bi(f,l.conv32_3),f=Bu(f,l.conv64_down),f=bi(f,l.conv64_1),f=bi(f,l.conv64_2),f=bi(f,l.conv64_3),f=Bu(f,l.conv128_down),f=bi(f,l.conv128_1),f=bi(f,l.conv128_2),f=Bu(f,l.conv256_down),f=bi(f,l.conv256_1),f=bi(f,l.conv256_2),f=Bu(f,l.conv256_down_out);const I=f.mean([1,2]),S=ks.matMul(I,l.fc);return S})}async forward(r){return this.forwardInput(await Tt(r))}async computeFaceDescriptor(r){const l=await Tt(r),u=ks.tidy(()=>ks.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 AD(r)}extractParams(r){return TD(r)}}function l2(r){const l=new Tc;return l.extractWeights(r),l}function gu(r,l){const u={descriptor:l};return Object.assign({},r,u)}function c2(r){return typeof r.age=="number"}function yu(r,l){const u={age:l};return Object.assign({},r,u)}function a2(r){return(r.gender===Ui.MALE||r.gender===Ui.FEMALE)&&Ec(r.genderProbability)}function mu(r,l,u){const p={gender:l,genderProbability:u};return Object.assign({},r,p)}const wi=Xe(Je());function AZ(r,l){function u(x,v){const O=wi.tensor4d(r(3*3*x),[3,3,x,1]),C=wi.tensor1d(r(x)),U=wi.tensor1d(r(x)),G=wi.tensor1d(r(x)),ne=wi.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=wi.tensor4d(r(x*v*O*O),[O,O,x,v]),ne=wi.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"),Ct=p(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),Ut=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"),An=p(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),vn={box_encoding_predictor:oe,class_predictor:ge},Zs={box_encoding_predictor:fe,class_predictor:Ae},Si={box_encoding_predictor:Te,class_predictor:Ve},Qs={box_encoding_predictor:rt,class_predictor:Ct},ya={box_encoding_predictor:Ut,class_predictor:Kt},ol={box_encoding_predictor:Dn,class_predictor:An};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:vn,box_predictor_1:Zs,box_predictor_2:Si,box_predictor_3:Qs,box_predictor_4:ya,box_predictor_5:ol}}return{extractMobilenetV1Params:I,extractPredictionLayerParams:S}}function 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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 ND(r){const l=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:p}=vZ(r,l),g=r["Output/extra_dim"];if(l.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!dr(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 zn(r,l),{params:f,paramMappings:l}}const fo=Xe(Je());function Ks(r,l,u){return fo.tidy(()=>{let p=fo.conv2d(r,l.filters,u,"same");return p=fo.add(p,l.batch_norm_offset),fo.clipByValue(p,0,6)})}const wr=Xe(Je()),NZ=.0010000000474974513;function CZ(r,l,u){return wr.tidy(()=>{let p=wr.depthwiseConv2d(r,l.filters,u,"same");return p=wr.batchNorm(p,l.batch_norm_mean,l.batch_norm_variance,l.batch_norm_offset,l.batch_norm_scale,NZ),wr.clipByValue(p,0,6)})}function RZ(r){return[2,4,6,12].some(l=>l===r)?[2,2]:[1,1]}function CD(r,l){return wr.tidy(()=>{let u,p=Ks(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=RZ(S);p=CZ(p,f.depthwise_conv,x),p=Ks(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 RD(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=OZ(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 OZ(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=Xe(Je());function EZ(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 DZ(r,l){const{sizes:u,centers:p}=EZ(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 OD(r,l,u){return De.tidy(()=>{const p=r.shape[0];let g=DZ(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 Mu=Xe(Je());function fa(r,l){return Mu.tidy(()=>{const u=r.shape[0],p=Mu.reshape(ma(r,l.box_encoding_predictor),[u,-1,1,4]),g=Mu.reshape(ma(r,l.class_predictor),[u,-1,3]);return{boxPredictionEncoding:p,classPrediction:g}})}const Pu=Xe(Je());function ED(r,l,u){return Pu.tidy(()=>{const p=Ks(r,u.conv_0,[1,1]),g=Ks(p,u.conv_1,[2,2]),f=Ks(g,u.conv_2,[1,1]),I=Ks(f,u.conv_3,[2,2]),S=Ks(I,u.conv_4,[1,1]),x=Ks(S,u.conv_5,[2,2]),v=Ks(x,u.conv_6,[1,1]),O=Ks(v,u.conv_7,[2,2]),C=fa(l,u.box_predictor_0),U=fa(r,u.box_predictor_1),G=fa(g,u.box_predictor_2),ne=fa(I,u.box_predictor_3),te=fa(x,u.box_predictor_4),oe=fa(O,u.box_predictor_5),ge=Pu.concat([C.boxPredictionEncoding,U.boxPredictionEncoding,G.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,oe.boxPredictionEncoding],1),fe=Pu.concat([C.classPrediction,U.classPrediction,G.classPrediction,ne.classPrediction,te.classPrediction,oe.classPrediction],1);return{boxPredictions:ge,classPredictions:fe}})}class Vs{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 Li=Xe(Je());class Xo extends In{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return Li.tidy(()=>{const u=Li.cast(r.toBatchTensor(512,!1),"float32"),p=Li.sub(Li.mul(u,Li.scalar(.007843137718737125)),Li.scalar(1)),g=CD(p,l.mobilenetv1),{boxPredictions:f,classPredictions:I}=ED(g.out,g.conv11,l.prediction_layer);return OD(f,I,l.output_layer)})}async forward(r){return this.forwardInput(await Tt(r))}async locateFaces(r,l={}){const{maxResults:u,minConfidence:p}=new Vs(l),g=await Tt(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=RD(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(Ct=>Ct*te),[Ve,rt]=[Math.max(0,oe[fe][1]),Math.min(1,oe[fe][3])].map(Ct=>Ct*ne);return new Wt(v[fe],new Cc(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 ND(r)}extractParams(r){return vD(r)}}function $S(r){const l=new Xo;return l.extractWeights(r),l}function UC(r){return $S(r)}class BC extends Xo{}const DD=.4,kD=[new qe(.738768,.874946),new qe(2.42204,2.65704),new qe(4.30971,7.04493),new qe(10.246,4.59428),new qe(12.6868,11.8741)],FD=[new qe(1.603231,2.094468),new qe(6.041143,7.080126),new qe(2.882459,3.518061),new qe(4.266906,5.178857),new qe(9.041765,10.66308)],_D=[117.001,114.697,97.404],WD="tiny_yolov2_model",$D="tiny_yolov2_separable_conv_model";const Fg=r=>typeof r=="number";function Tm(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(!Fg(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=>Fg(l.x)&&Fg(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(Fg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const Xs=Xe(Je());function Qc(r){return Xs.tidy(()=>{const l=Xs.mul(r,Xs.scalar(.10000000149011612));return Xs.add(Xs.relu(Xs.sub(r,l)),l)})}const Js=Xe(Je());function Lr(r,l){return Js.tidy(()=>{let u=Js.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=Js.conv2d(u,l.conv.filters,[1,1],"valid"),u=Js.sub(u,l.bn.sub),u=Js.mul(u,l.bn.truediv),u=Js.add(u,l.conv.bias),Qc(u)})}const go=Xe(Je());function Sr(r,l){return go.tidy(()=>{let u=go.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=go.separableConv2d(u,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),u=go.add(u,l.bias),Qc(u)})}const rx=Xe(Je());function kZ(r,l){const u=qc(r,l);function p(I,S){const x=rx.tensor1d(r(I)),v=rx.tensor1d(r(I));return l.push({paramPath:`${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=jc(r,l);return{extractConvParams:u,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}}function UD(r,l,u,p){const{extractWeights:g,getRemainingWeights:f}=Gn(r),I=[],{extractConvParams:S,extractConvWithBatchNormParams:x,extractSeparableConvParams:v}=kZ(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"),Ct=v(ne,te,"conv3"),Ut=v(te,oe,"conv4"),Kt=v(oe,ge,"conv5"),Dn=fe?v(ge,fe,"conv6"):void 0,An=Ae?v(fe,Ae,"conv7"):void 0,vn=S(Ae||fe||ge,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:Ct,conv4:Ut,conv5:Kt,conv6:Dn,conv7:An,conv8:vn}}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"),Ct=x(ne,te,"conv3"),Ut=x(te,oe,"conv4"),Kt=x(oe,ge,"conv5"),Dn=x(ge,fe,"conv6"),An=x(fe,Ae,"conv7"),vn=S(Ae,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:Ct,conv4:Ut,conv5:Kt,conv6:Dn,conv7:An,conv8:vn}}if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{params:O,paramMappings:I}}function FZ(r,l){const u=ms(r,l);function p(S){const x=u(`${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=Kc(u);return{extractConvParams:g,extractConvWithBatchNormParams:f,extractSeparableConvParams:I}}function BD(r,l){const u=[],{extractConvParams:p,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}=FZ(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 zn(r,u),{params:I,paramMappings:u}}var Am;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(Am||(Am={}));class $i{constructor({inputSize:r,scoreThreshold:l}={}){this._name="TinyYolov2Options";if(this._inputSize=r||416,this._scoreThreshold=l||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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=Xe(Je());class el extends In{constructor(r){super("TinyYolov2");Tm(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=Lr(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=Lr(u,l.conv6),u=Lr(u,l.conv7),ma(u,l.conv8,"valid",!1)}runMobilenet(r,l){let u=this.config.isFirstLayerConv2d?Qc(ma(r,l.conv0,"valid",!1)):Sr(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=l.conv6?Sr(u,l.conv6):u,u=l.conv7?Sr(u,l.conv7):u,ma(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?Ys(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 Tt(r),l)}async detect(r,l={}){const{inputSize:u,scoreThreshold:p}=new $i(l),g=await Tt(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=Cm(v.map(te=>te.rescale(u)),O,this.config.iouThreshold,!0),ne=G.map(te=>new ta(O[te],C[te],U[te],v[te],S));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return BD(r,this.config)}extractParams(r){const l=this.config.filterSizes||el.DEFAULT_FILTER_SIZES,u=l?l.length:void 0;if(u!==7&&u!==8&&u!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${u} filterSizes in config`);return UD(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,u){const{width:p,height:g}=l,f=Math.max(p,g),I=f/p,S=f/g,x=r.shape[1],v=this.config.anchors.length,[O,C,U]=kt.tidy(()=>{const oe=r.reshape([x,x,v,this.boxEncodingSize]),ge=oe.slice([0,0,0,0],[x,x,v,4]),fe=oe.slice([0,0,0,4],[x,x,v,1]),Ae=this.withClassScores?kt.softmax(oe.slice([0,0,0,5],[x,x,v,this.config.classes.length]),3):kt.scalar(0);return[ge,fe,Ae]}),G=[],ne=await C.array(),te=await O.array();for(let oe=0;oe<x;oe++)for(let ge=0;ge<x;ge++)for(let fe=0;fe<v;fe++){const Ae=xc(ne[oe][ge][fe][0]);if(!u||Ae>u){const Te=(ge+xc(te[oe][ge][fe][0]))/x*I,Ve=(oe+xc(te[oe][ge][fe][1]))/x*S,rt=Math.exp(te[oe][ge][fe][2])*this.config.anchors[fe].x/x*I,Ct=Math.exp(te[oe][ge][fe][3])*this.config.anchors[fe].y/x*S,Ut=Te-rt/2,Kt=Ve-Ct/2,Dn={row:oe,col:ge,anchor:fe},{classScore:An,label:vn}=this.withClassScores?await this.extractPredictedClass(U,Dn):{classScore:1,label:0};G.push({box:new Oc(Ut,Kt,Ut+rt,Kt+Ct),score:Ae,classScore:Ae*An,label:vn,...Dn})}}return O.dispose(),C.dispose(),U.dispose(),G}async extractPredictedClass(r,l){const{row:u,col:p,anchor:g}=l,f=await r.array();return Array(this.config.classes.length).fill(0).map((I,S)=>f[u][p][g][S]).map((I,S)=>({classScore:I,label:S})).reduce((I,S)=>I.classScore>S.classScore?I:S)}}el.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class Sc extends el{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:DD,classes:["face"]},r?{anchors:FD,meanRgb:_D}:{anchors:kD,withClassScores:!0});super(l)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Wt(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?$D:WD}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function WC(r,l=!0){const u=new Sc(l);return u.extractWeights(r),u}class vm extends $i{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class Hs{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const ox=Xe(Je());async function ga(r,l,u,p,g=({alignedRect:f})=>f){const f=r.map(x=>Jr(x)?g(x):x.detection),I=p||(l instanceof ox.Tensor?await Qo(l,f):await Zo(l,f)),S=await u(I);return I.forEach(x=>x instanceof ox.Tensor&&x.dispose()),S}async function tl(r,l,u,p,g){return ga([r],l,async f=>u(f[0]),p,g)}const MD=.4,PD=[new qe(1.603231,2.094468),new qe(6.041143,7.080126),new qe(2.882459,3.518061),new qe(4.266906,5.178857),new qe(9.041765,10.66308)],zD=[117.001,114.697,97.404];class Ic extends el{constructor(){const r={withSeparableConvs:!0,iouThreshold:MD,classes:["face"],anchors:PD,meanRgb:zD,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 Wt(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const ht={ssdMobilenetv1:new Xo,tinyFaceDetector:new Ic,tinyYolov2:new Sc,faceLandmark68Net:new Ac,faceLandmark68TinyNet:new Um,faceRecognitionNet:new Tc,faceExpressionNet:new Mm,ageGenderNet:new jm},BS=(r,l)=>ht.ssdMobilenetv1.locateFaces(r,l),zC=(r,l)=>ht.tinyFaceDetector.locateFaces(r,l),GC=(r,l)=>ht.tinyYolov2.locateFaces(r,l),MS=r=>ht.faceLandmark68Net.detectLandmarks(r),VC=r=>ht.faceLandmark68TinyNet.detectLandmarks(r),YC=r=>ht.faceRecognitionNet.computeFaceDescriptor(r),HC=r=>ht.faceExpressionNet.predictExpressions(r),qC=r=>ht.ageGenderNet.predictAgeAndGender(r),PS=r=>ht.ssdMobilenetv1.load(r),jC=r=>ht.tinyFaceDetector.load(r),KC=r=>ht.tinyYolov2.load(r),XC=r=>ht.faceLandmark68Net.load(r),JC=r=>ht.faceLandmark68TinyNet.load(r),ZC=r=>ht.faceRecognitionNet.load(r),QC=r=>ht.faceExpressionNet.load(r),e2=r=>ht.ageGenderNet.load(r),t2=PS,n2=BS,s2=MS;class GD extends Hs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class Vu extends GD{async run(){const r=await this.parentTask,l=await ga(r,this.input,async u=>await Promise.all(u.map(p=>ht.faceExpressionNet.predictExpressions(p))),this.extractedFaces);return r.map((u,p)=>fu(u,l[p]))}withAgeAndGender(){return new zu(this,this.input)}}class Yu extends GD{async run(){const r=await this.parentTask;if(!r)return;const l=await tl(r,this.input,u=>ht.faceExpressionNet.predictExpressions(u),this.extractedFaces);return fu(r,l)}withAgeAndGender(){return new Gu(this,this.input)}}class il extends Vu{withAgeAndGender(){return new nl(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}}class rl extends Yu{withAgeAndGender(){return new sl(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}}class VD extends Hs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class zu extends VD{async run(){const r=await this.parentTask,l=await ga(r,this.input,async u=>await Promise.all(u.map(p=>ht.ageGenderNet.predictAgeAndGender(p))),this.extractedFaces);return r.map((u,p)=>{const{age:g,gender:f,genderProbability:I}=l[p];return yu(mu(u,f,I),g)})}withFaceExpressions(){return new Vu(this,this.input)}}class Gu extends VD{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:u,genderProbability:p}=await tl(r,this.input,g=>ht.ageGenderNet.predictAgeAndGender(g),this.extractedFaces);return yu(mu(r,u,p),l)}withFaceExpressions(){return new Yu(this,this.input)}}class nl extends zu{withFaceExpressions(){return new il(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}}class sl extends Gu{withFaceExpressions(){return new rl(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}}class Wm extends Hs{constructor(r,l){super();this.parentTask=r;this.input=l}}class Kr extends Wm{async run(){const r=await this.parentTask,l=await ga(r,this.input,u=>Promise.all(u.map(p=>ht.faceRecognitionNet.computeFaceDescriptor(p))),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return l.map((u,p)=>gu(r[p],u))}withFaceExpressions(){return new il(this,this.input)}withAgeAndGender(){return new nl(this,this.input)}}class Xr extends Wm{async run(){const r=await this.parentTask;if(!r)return;const l=await tl(r,this.input,u=>ht.faceRecognitionNet.computeFaceDescriptor(u),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return gu(r,l)}withFaceExpressions(){return new rl(this,this.input)}withAgeAndGender(){return new sl(this,this.input)}}const Hu=Xe(Je());class km extends Hs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=u}get landmarkNet(){return this.useTinyLandmarkNet?ht.faceLandmark68TinyNet:ht.faceLandmark68Net}}class Fm extends km{async run(){const r=await this.parentTask,l=r.map(g=>g.detection),u=this.input instanceof Hu.Tensor?await Qo(this.input,l):await Zo(this.input,l),p=await Promise.all(u.map(g=>this.landmarkNet.detectLandmarks(g)));return u.forEach(g=>g instanceof Hu.Tensor&&g.dispose()),r.map((g,f)=>Jo(g,p[f]))}withFaceExpressions(){return new il(this,this.input)}withAgeAndGender(){return new nl(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}}class _m extends km{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,u=this.input instanceof Hu.Tensor?await Qo(this.input,[l]):await Zo(this.input,[l]),p=await this.landmarkNet.detectLandmarks(u[0]);return u.forEach(g=>g instanceof Hu.Tensor&&g.dispose()),Jo(r,p)}withFaceExpressions(){return new rl(this,this.input)}withAgeAndGender(){return new sl(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}}class Em extends Hs{constructor(r,l=new Vs){super();this.input=r;this.options=l}}class pu extends Em{async run(){const{input:r,options:l}=this,u=l instanceof vm?p=>ht.tinyFaceDetector.locateFaces(p,l):l instanceof Vs?p=>ht.ssdMobilenetv1.locateFaces(p,l):l instanceof $i?p=>ht.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=>Zr({},u)))})}withFaceLandmarks(r=!1){return new Fm(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new Vu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new zu(this.runAndExtendWithFaceDetections(),this.input)}}class Dm extends Em{async run(){const r=await new pu(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?Zr({},l):void 0)})}withFaceLandmarks(r=!1){return new _m(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new Yu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Gu(this.runAndExtendWithFaceDetection(),this.input)}}function i2(r,l=new Vs){return new Dm(r,l)}function du(r,l=new Vs){return new pu(r,l)}async function zS(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await du(r,new Vs(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function r2(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await du(r,new $i(l)).withFaceLandmarks().withFaceDescriptors()}const o2=zS;function Pm(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 GS{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 so)return f;if(f instanceof Float32Array)return new so(g(),[f]);if(f.descriptor&&f.descriptor instanceof Float32Array)return new so(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=>Pm(u,r)).reduce((u,p)=>u+p,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:u})=>new Lu(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 Lu("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(u=>so.fromJSON(u));return new GS(l,r.distanceThreshold)}}function $C(r){const l=new Ic;return l.extractWeights(r),l}function US(r,l){const{width:u,height:p}=new Zn(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=>US(g,{width:u,height:p}));if(Jr(r)){const g=r.detection.forSize(u,p),f=r.unshiftedLandmarks.forSize(g.box.width,g.box.height);return Jo(Zr(r,g),f)}return mi(r)?Zr(r,r.detection.forSize(u,p)):r instanceof Ns||r instanceof Wt?r.forSize(u,p):r}var FC="0.8.3";return g2();})();
/**
 * @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
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 * =============================================================================
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