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new Error(`dtype of the new value (${t.dtype}) and previous value (${this.dtype}) must match`);if(!Dn(t.shape,this.shape))throw new Error(`shape of the new value (${t.shape}) and previous value (${this.shape}) must match`);Ms().disposeTensor(this),this.dataId=t.dataId,Ms().incRef(this,null)}dispose(){Ms().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Ka,Symbol.hasInstance,{value:r=>r instanceof Ft&&r.assign!=null&&r.assign instanceof Function});var go={};Wt(go,{assertTypesMatch:()=>I0,getTensorsInContainer:()=>nh,isTensorInList:()=>x4,makeTypesMatch:()=>Ut});var x0;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(x0||(x0={}));var y0;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(y0||(y0={}));var b0;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(b0||(b0={}));var w0;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(w0||(w0={}));var C0;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(C0||(C0={}));var g4={float32:w0,int32:y0,bool:b0,complex64:C0};function sr(r,t){if(r==="string"||t==="string"){if(r==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${r} with ${t}`)}return g4[r][t]}function Wu(r){return sr(r,"int32")}function Ut(r,t){if(r.dtype===t.dtype)return[r,t];let e=sr(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function I0(r,t){E(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function x4(r,t){return t.some(e=>e.id===r.id)}function nh(r){let t=[];return M1(r,t,new Set),t}function M1(r,t,e){if(r==null)return;if(r instanceof Ft){t.push(r);return}if(!y4(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),M1(s,t,e))}}function y4(r){return Array.isArray(r)||typeof r=="object"}function S0(r){return r.kernelName!=null}var Ug=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},ql=class{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Ug}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){zg(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof zo)&&typeof n.then=="function"){let o=++this.pendingBackendInitId,s=n.then(i=>o(othis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return ql.nextTensorId++}nextVariableId(){return ql.nextVariableId++}clone(t){let e=k.runKernel(co,{x:t}),n={x:t},o=i=>({x:()=>{let a="float32",u={x:i},l={dtype:a};return k.runKernel(lo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(Jd(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,n){let o=this.backend.numDataIds(),s=0;n.forEach(u=>{s+=u.dtype==="complex64"?3:1});let i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=o-e-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${t}'`)}runKernelFunc(t){let e,n=[],o=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let u,l=S0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(S0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=Jd(d,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let C=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,C);n=this.saveTensorsForBackwardMode(N)}return C}}else{let{forwardFunc:d}=t,h=g=>{!o||(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=S0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=u0(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(E(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let u=n.filter((l,c)=>i[c]);return a.concat(u)}return[]}makeTensor(t,e,n,o){if(t==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",o=o||this.backend;let s=t;n==="string"&&Vo(t[0])&&(s=t.map(u=>Hl(u)));let i=o.write(s,e,n),a=new Ft(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n==="string"){let u=this.state.tensorInfo.get(i),l=s0(s);this.state.numBytes+=l-u.bytes,u.bytes=l}return a}makeTensorFromDataId(t,e,n,o){n=n||"float32";let s={dataId:t,shape:e,dtype:n};return this.makeTensorFromTensorInfo(s,o)}makeTensorFromTensorInfo(t,e){let{dataId:n,shape:o,dtype:s}=t,i=new Ft(o,s,n,this.nextTensorId());return this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new Ka(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++;let n=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(n=t.size*Mg(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof Ka||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!=="complex64"&&t.dtype!=="string"){let n=t.size*Mg(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,n,o,s,i){let a={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:s},u=u0(t);u!=null&&(o=u.gradFunc),o!=null&&(a.gradient=l=>(l=l.map((c,p)=>{if(c==null){let m=n[p],f=ip(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(l.length>1?l:l[0],s,i))),this.state.activeTape.push(a)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=nh(t),n=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(E(e.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}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));E(s instanceof Ft,()=>"The result y returned by f() must be a tensor.");let i=A1(this.state.activeTape,e,s);if(!o&&i.length===0&&e.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",()=>{let a={};a[s.id]=n==null?b4(s.shape):n,$1(a,i,l=>this.tidy(l),w4);let u=e.map(l=>a[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:u}})}customGrad(t){return E(oi(t),()=>"The f passed in customGrad(f) must be a function."),(...e)=>{E(e.every(a=>a instanceof Ft),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,o={};e.forEach((a,u)=>{o[u]=a});let s=(a,u)=>(n=t(...e,u),E(n.value instanceof Ft,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),E(oi(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),i=(a,u)=>{let l=n.gradFunc(a,u),c=Array.isArray(l)?l:[l];E(c.length===e.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),E(c.every(m=>m instanceof Ft),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:i,inputs:o})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=Gu(),n=await this.backend.time(t);return n.wallMs=Gu()-e,n}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new Ug;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};ql.nextTensorId=0;ql.nextVariableId=0;function b4(r){let t=Wd(Jt(r),"float32");return k.makeTensor(t,r,"float32")}function v0(){let r=l0();if(r._tfengine==null){let t=new qd(r);r._tfengine=new ql(t)}return c1(r._tfengine.ENV),O1(()=>r._tfengine),r._tfengine}var k=v0();function w4(r,t){let e={a:r,b:t};return k.runKernel(Zn,e)}var Kl={};Wt(Kl,{isBrowser:()=>T0,isMobile:()=>S4,mockIsMobile:()=>I4});function C4(){return typeof navigator!="undefined"&&navigator!=null}var N0;function I4(r){N0=r}function S4(r){if(N0!==void 0)return N0;if(r||C4()){if(r||(r=navigator),r.product==="ReactNative")return!0;let t=r.userAgent||r.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let e=r;return 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Got strides ${e} and dilations '${i}'`);let a=s,u=!1;s.rank===3&&(u=!0,a=R(s,[1,s.shape[0],s.shape[1],s.shape[2]])),E(a.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${a.rank}.`),Ie("avgPool",n,o);let l={x:a},c={filterSize:t,strides:e,pad:n,dimRoundingMode:o},p=k.runKernel(Uo,l,c);return p=J(p,s.dtype),u?R(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Yl=T({avgPool_:QH});function tq(r,t,e,n,o,s="NDHWC"){let i=I(r,"x","avgPool3d","float32"),a=i,u=!1;i.rank===4&&(u=!0,a=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),E(a.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${a.rank}.`),E(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),Ie("avgPool3d",n,o);let l={x:a},c={filterSize:t,strides:e,pad:n,dimRoundingMode:o,dataFormat:s},p=k.runKernel(El,l,c);return p=J(p,a.dtype),u?R(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var gx=T({avgPool3d_:tq});function eq(r,t=0){E(r.length>=1,()=>"Pass at least one tensor to concat");let e=ja(r,"tensors","concat","string_or_numeric");if(e[0].dtype==="complex64"&&e.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor with dtype ${s.dtype}. `)}),e.length===1)return sn(e[0]);let n=e,o={axis:t};return k.runKernel(li,n,o)}var ne=T({concat_:eq});function rq(r){let e={x:I(r,"x","sigmoid","float32")};return k.runKernel(_s,e)}var Yr=T({sigmoid_:rq});function nq(r,t,e){let n=I(r,"x","slice","string_or_numeric");if(n.rank===0)throw new Error("Slicing scalar is not possible");let o={x:n},s={begin:t,size:e};return k.runKernel(gi,o,s)}var Rt=T({slice_:nq});function oq(r){let e={x:I(r,"x","tanh","float32")};return k.runKernel(Ps,e)}var $i=T({tanh_:oq});function sq(r,t,e,n,o,s){let i=I(r,"forgetBias","basicLSTMCell"),a=I(t,"lstmKernel","basicLSTMCell"),u=I(e,"lstmBias","basicLSTMCell"),l=I(n,"data","basicLSTMCell"),c=I(o,"c","basicLSTMCell"),p=I(s,"h","basicLSTMCell"),m=ne([l,p],1),f=Lt(m,a),d=X(f,u),h=d.shape[0],g=d.shape[1]/4,x=[h,g],b=Rt(d,[0,0],x),w=Rt(d,[0,g],x),C=Rt(d,[0,g*2],x),N=Rt(d,[0,g*3],x),_=X(D(Yr(b),$i(w)),D(c,Yr(X(i,C)))),A=D($i(_),Yr(N));return[_,A]}var BE=T({basicLSTMCell_:sq});function iq(r,t,e){let n=I(r,"x","batchToSpaceND"),o=t.reduce((a,u)=>a*u);E(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),E(e.length===t.length,()=>`crops.length is ${e.length} but should be equal to blockShape.length ${t.length}`),E(n.shape[0]%o===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${o}`);let s={x:n},i={blockShape:t,crops:e};return k.runKernel(ai,s,i)}var Zl=T({batchToSpaceND_:iq});function VE(r){let t;return r.rank===0||r.rank===1?t=R(r,[1,1,1,r.size]):r.rank===2?t=R(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?t=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]):t=r,t}function aq(r,t,e,n,o,s){s==null&&(s=.001);let i=I(r,"x","batchNorm"),a=I(t,"mean","batchNorm"),u=I(e,"variance","batchNorm"),l;o!=null&&(l=I(o,"scale","batchNorm"));let c;n!=null&&(c=I(n,"offset","batchNorm")),E(a.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),E(c==null||a.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),E(l==null||a.rank===l.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:VE(i),scale:l,offset:c,mean:a,variance:u},f={varianceEpsilon:s},d=k.runKernel(os,m,f);return R(d,i.shape)}var Di=T({batchNorm_:aq});function lq(r,t,e,n,o,s){let i=I(r,"x","batchNorm"),a=I(t,"mean","batchNorm"),u=I(e,"variance","batchNorm"),l;o!=null&&(l=I(o,"scale","batchNorm"));let c;return n!=null&&(c=I(n,"offset","batchNorm")),E(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),E(a.rank===2||a.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${a.rank}.`),E(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&E(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&E(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Di(i,a,u,c,l,s)}var xx=T({batchNorm2d_:lq});function uq(r,t,e,n,o,s){let i=I(r,"x","batchNorm"),a=I(t,"mean","batchNorm"),u=I(e,"variance","batchNorm"),l;o!=null&&(l=I(o,"scale","batchNorm"));let c;return n!=null&&(c=I(n,"offset","batchNorm")),E(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),E(a.rank===3||a.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${a.rank}.`),E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&E(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Di(i,a,u,c,l,s)}var yx=T({batchNorm3d_:uq});function cq(r,t,e,n,o,s){let i=I(r,"x","batchNorm"),a=I(t,"mean","batchNorm"),u=I(e,"variance","batchNorm"),l;o!=null&&(l=I(o,"scale","batchNorm"));let c;return n!=null&&(c=I(n,"offset","batchNorm")),E(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),E(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${a.rank}.`),E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&E(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Di(i,a,u,c,l,s)}var bx=T({batchNorm4d_:cq});function pq(r,t,e){let n=I(r,"x","bincount"),o=I(t,"weights","bincount");E(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),E(e>=0,()=>`size must be non-negative, but got ${e}.`),E(o.size===n.size||o.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${o.shape}.`);let s={x:n,weights:o},i={size:e};return k.runKernel(up,s,i)}var wx=T({bincount_:pq});function mq(r,t){let e=I(r,"s0","broadcastArgs","int32"),n=I(t,"s1","broadcastArgs","int32");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${e.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let o={s0:e,s1:n};return k.runKernel(cp,o)}var GE=T({broadcastArgs_:mq});function fq(r,t){let e=I(r,"broadcastTo","x"),n=e.shape;if(t.some(l=>!(l>0)||l%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.lengthe.rank){let l=e.shape.slice();for(;l.length=0;l--)if(o[l]===t[l])s[l]=1;else if(e.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return sn(e);let a={x:e},u={reps:s};return k.runKernel(Jn,a,u)}var Ri=T({broadcastTo_:fq});function dq(r){let e={x:I(r,"x","ceil","float32")};return k.runKernel(qo,e)}var Cx=T({ceil_:dq});function xo(r,t,e){let n={shape:r,value:t,dtype:e};return k.runKernel(Dl,{},n)}function hq(r,t,e){let n=I(r,"x","clipByValue");if(E(t<=e,()=>`Error in clip: min (${t}) must be less than or equal to max (${e}).`),t===e)return xo(n.shape,t,n.dtype);let o={x:n},s={clipValueMin:t,clipValueMax:e};return k.runKernel(uo,o,s)}var Cr=T({clipByValue_:hq});function gq(r){return ne(r,0)}var Ix=T({concat1d_:gq});function xq(r,t){return ne(r,t)}var Sx=T({concat2d_:xq});function yq(r,t){return ne(r,t)}var vx=T({concat3d_:yq});function bq(r,t){return ne(r,t)}var Nx=T({concat4d_:bq});function wq(r,t,e,n,o="NHWC",s=[1,1],i){let a=I(r,"x","conv2d","float32"),u=I(t,"filter","conv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),E(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),E(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),Ie("conv2d",n,i);let p=o==="NHWC"?l.shape[3]:l.shape[1];E(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),E(Ar(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`);let m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=k.runKernel(Ko,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var In=T({conv2d_:wq});function Cq(r,t,e,n,o="NWC",s=1,i){let a=I(r,"x","conv1d"),u=I(t,"filter","conv1d"),l=a,c=!1;a.rank===2&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1]])),E(l.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${l.rank}.`),E(u.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`),Ie("conv1d",n,i),E(l.shape[2]===u.shape[1],()=>`Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`),E(Ar(e,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${e} and dilation '${s}'`),E(o==="NWC",()=>`Error in conv1d: got dataFormat of ${o} but only NWC is currently supported.`);let p=R(u,[1,u.shape[0],u.shape[1],u.shape[2]]),m=R(l,[l.shape[0],1,l.shape[1],l.shape[2]]),g=In(m,p,[1,e],n,"NHWC",[1,s],i);return c?R(g,[g.shape[2],g.shape[3]]):R(g,[g.shape[0],g.shape[2],g.shape[3]])}var Qp=T({conv1d_:Cq});function Iq(r,t,e,n,o,s="NHWC",i){E(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let a=r,u=t,l=!1;t.rank===3&&(l=!0,u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]),a=[1,r[0],r[1],r[2]]),E(a.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${a.length}.`),E(u.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`),E(e.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${e.rank}`);let c=s==="NHWC"?a[3]:a[1],p=s==="NHWC"?u.shape[3]:u.shape[1];E(c===e.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${e.shape[2]}.`),E(p===e.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${e.shape[3]}.`),Ie("conv2dDerInput",o,i);let m={dy:u,filter:e},f={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,inputShape:a},d=k.runKernel(jo,m,f);return l?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var tm=T({conv2DBackpropInput_:Iq});function Sq(r,t,e,n,o,s){let i=I(r,"x","conv2dTranspose"),a=I(t,"filter","conv2dTranspose");return tm(e,i,a,n,o,"NHWC",s)}var em=T({conv2dTranspose_:Sq});function vq(r,t,e,n,o="NDHWC",s=[1,1,1]){let i=I(r,"x","conv3d"),a=I(t,"filter","conv3d"),u=i,l=!1;i.rank===4&&(l=!0,u=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),E(u.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`),E(a.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${a.rank}.`),E(u.shape[4]===a.shape[3],()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${a.shape[3]}.`),E(Ar(e,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),E(o==="NDHWC",()=>`Error in conv3d: got dataFormat of ${o} but only NDHWC is currently supported.`);let c={x:u,filter:a},p={strides:e,pad:n,dataFormat:o,dilations:s},m=k.runKernel(Al,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Tx=T({conv3d_:vq});function Nq(r,t,e,n,o){E(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let s=r,i=t,a=!1;t.rank===4&&(a=!0,i=R(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let u=s[4],l=i.shape[4];E(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),E(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),E(e.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${e.rank}`),E(u===e.shape[3],()=>`Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${e.shape[3]}.`),E(l===e.shape[4],()=>`Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${e.shape[4]}.`);let c={dy:i,filter:e},p={pad:o,strides:n,inputShape:s},m=k.runKernel(dp,c,p);return a?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var kx=T({conv3DBackpropInput_:Nq});function Tq(r,t,e,n,o){let s=I(r,"x","conv3dTranspose"),i=I(t,"filter","conv3dTranspose");return kx(e,s,i,n,o)}var Ex=T({conv3dTranspose_:Tq});function kq(r){let e={x:I(r,"x","cos","float32")};return k.runKernel(Xo,e)}var Jl=T({cos_:kq});function Eq(r){let e={x:I(r,"x","cosh","float32")};return k.runKernel(Yo,e)}var rm=T({cosh_:Eq});function _q(r,t=0,e=!1,n=!1){let s={x:I(r,"x","cumprod")},i={axis:t,exclusive:e,reverse:n};return k.runKernel(fa,s,i)}var Xu=T({cumprod_:_q});function Aq(r,t=0,e=!1,n=!1){let s={x:I(r,"x","cumsum")},i={axis:t,exclusive:e,reverse:n};return k.runKernel(Zo,s,i)}var nm=T({cumsum_:Aq});function $q(r,t,e,n=!1){let o=I(r,"x","denseBincount"),s=I(t,"weights","denseBincount");E(o.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),E(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),E(e>=0,()=>`size must be non-negative, but got ${e}.`),E(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return k.runKernel(hp,i,a)}var ch=T({denseBincount_:$q});function Dq(r,t,e="NHWC"){let n=I(r,"x","depthToSpace","float32"),o=e==="NHWC"?n.shape[1]:n.shape[2],s=e==="NHWC"?n.shape[2]:n.shape[3],i=e==="NHWC"?n.shape[3]:n.shape[1];E(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),E(o*t>=0,()=>`Negative dimension size caused by overflow when multiplying ${o} and ${t} for depthToSpace with input shape ${n.shape}`),E(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying ${s} and ${t} for depthToSpace with input shape ${n.shape}`),E(i%(t*t)===0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${n.shape}`);let a={x:n},u={blockSize:t,dataFormat:e};return k.runKernel(ha,a,u)}var _x=T({depthToSpace_:Dq});function Rq(r,t,e,n,o="NHWC",s=[1,1],i){let a=I(r,"x","depthwiseConv2d","float32"),u=I(t,"filter","depthwiseConv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),E(l.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`),E(u.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`);let p=o==="NHWC"?l.shape[3]:l.shape[1];E(p===u.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${p}) must match the inChannels dimension in filter ${u.shape[2]}.`),Ie("depthwiseConv2d",n,i);let m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=k.runKernel(Jo,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Fi=T({depthwiseConv2d_:Rq});function Fq(r){let e={x:I(r,"x","diag")};return k.runKernel(yp,e)}var WE=T({diag_:Fq});function Oq(r,t,e,n,o=[1,1],s="NHWC"){let i=I(r,"x","dilation2d"),a=I(t,"filter","dilation2d");E(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),E(a.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${a.rank}.`),E(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let u=i,l=!1;i.rank===3&&(u=R(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=!0);let c={x:u,filter:a},p={strides:e,pad:n,dilations:o},m=k.runKernel($l,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Ax=T({dilation2d_:Oq});function Pq(r,t){let e=I(r,"a","equal","string_or_numeric"),n=I(t,"b","equal","string_or_numeric");[e,n]=Ut(e,n),Pt(e.shape,n.shape);let o={a:e,b:n};return k.runKernel(xa,o)}var $r=T({equal_:Pq});function Lq(r,t,e){let n=I(t,"a","where"),o=I(e,"b","where"),s=I(r,"condition","where","bool"),i=Pt(Pt(s.shape,n.shape),o.shape),a=Ri(s,i),u=Ri(n,i),l=Ri(o,i),c={condition:a,t:u,e:l};return k.runKernel(hi,c)}var _e=T({where_:Lq});function Mq(r){let e={x:I(r,"x","zerosLike")};return k.runKernel(wi,e)}var It=T({zerosLike_:Mq});function zq(r,t){let e=I(r,"a","div"),n=I(t,"b","div");[e,n]=Ut(e,n);let o=pt(e,n),s=It(o),i=$r(n,s);return _e(i,s,o)}var $x=T({divNoNan_:zq});function Bq(r,t){let e=I(r,"t1","dot"),n=I(t,"t2","dot");E((e.rank===1||e.rank===2)&&(n.rank===1||n.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${e.rank} and ${n.rank}.`);let o=e.rank===1?e.size:e.shape[1],s=n.rank===1?n.size:n.shape[0];if(E(o===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${o} and 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e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};pu.className="Adagrad";Cn(pu);var mu=class extends Wr{constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=mt(e).variable(),this.accBeta2=mt(n).variable()}),o==null&&(this.epsilon=k.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=ct(1,this.accBeta1),o=ct(1,this.accBeta2);e.forEach((s,i)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){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(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};fu.className="Adamax";Cn(fu);var Bi=class extends Wr{constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=k.registeredVariables[n];B(()=>{let a=X(D(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=De(mt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};du.className="Momentum";Cn(du);var hu=class extends Wr{constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=k.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let 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e=this.centered?t.length/3:t.length/2,n=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};hu.className="RMSProp";Cn(hu);var Ws=class{static sgd(t){return new Bi(t)}static momentum(t,e,n=!1){return new du(t,e,n)}static rmsprop(t,e=.9,n=0,o=null,s=!1){return new hu(t,e,n,o,s)}static adam(t=.001,e=.9,n=.999,o=null){return new mu(t,e,n,o)}static adadelta(t=.001,e=.95,n=null){return new cu(t,e,n)}static adamax(t=.002,e=.9,n=.999,o=null,s=0){return new fu(t,e,n,o,s)}static 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$t{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",bc.verifyArgs(e),this.rank=t,Ze(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new St(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Cu(e.kernelSize,t,"kernelSize"),this.strides=Cu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,pn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Fe(this.dataFormat),this.activation=Xs(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=de(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Be(e.biasConstraint),this.biasRegularizer=be(e.biasRegularizer),this.activityRegularizer=be(e.activityRegularizer),this.dilationRate=Cu(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new M(`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 M(`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 M(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(ro("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Cy(t.kernelSize,"number",1,3))throw new M(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:js(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:ze(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},Iu=class extends bc{constructor(t,e){super(t,e),this.kernel=null,Iu.verifyArgs(e),this.filters=e.filters,Ze(this.filters,"filters"),this.kernelInitializer=de(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Be(e.kernelConstraint),this.kernelRegularizer=be(e.kernelRegularizer)}build(t){t=Bt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new M(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,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:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=Nt(t);let n,o=this.bias==null?null:this.bias.read(),s=Iy(this.activation.getClassName());if(s!=null&&this.rank===2)n=wD(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=X8(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=wD(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Y8(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new St("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Bt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},il=class extends Iu{constructor(t){super(2,t),il.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Cy(t.kernelSize,"number",1,2))throw new M(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};il.className="Conv2D";Q.registerClass(il);var al=class extends Iu{constructor(t){super(3,t),al.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new M(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};al.className="Conv3D";Q.registerClass(al);var Qm=class extends il{constructor(t){if(super(t),this.inputSpec=[new ye({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new M(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Bt(t),t.length!==4)throw new M("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new M("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ye({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Nt(t);if(n.shape.length!==4)throw new M(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Ys(u,m,c,this.padding),h=Ys(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Ot(n,[0,2,3,1]));let x=em(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Ot(x,[0,3,1,2])),this.bias!=null&&(x=fn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Bt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=Ys(e[o],u,i,this.padding),e[s]=Ys(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};Qm.className="Conv2DTranspose";Q.registerClass(Qm);var tf=class extends al{constructor(t){if(super(t),this.inputSpec=[new ye({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new M(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Bt(t),t.length!==5)throw new M("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new M("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ye({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Nt(t);if(n.shape.length!==5)throw new M(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=Ys(l,h,m,this.padding),w=Ys(c,g,f,this.padding),C=Ys(p,x,d,this.padding),N=[s,b,w,C,this.filters];this.dataFormat!=="channelsLast"&&(n=Ot(n,[0,2,3,4,1]));let _=Ex(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(_=Ot(_,[0,4,1,2,3])),this.bias!==null&&(_=fn(_,this.bias.read(),this.dataFormat)),this.activation!==null&&(_=this.activation.apply(_)),_})}computeOutputShape(t){t=Bt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=Ys(e[o],c,a,this.padding),e[s]=Ys(e[s],p,u,this.padding),e[i]=Ys(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};tf.className="Conv3DTranspose";Q.registerClass(tf);var fb=class extends Iu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new M("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new M("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new M(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=be(e.depthwiseRegularizer),this.depthwiseConstraint=Be(e.depthwiseConstraint),this.pointwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=be(e.pointwiseRegularizer),this.pointwiseConstraint=Be(e.pointwiseConstraint)}build(t){if(t=Bt(t),t.length{t=Nt(t);let n;if(this.rank===1)throw new St("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Ot(t,[0,2,3,1])),n=mm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=fn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ot(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=ze(this.depthwiseConstraint),t.pointwiseConstraint=ze(this.pointwiseConstraint),t}};fb.className="SeparableConv";var ef=class extends fb{constructor(t){super(2,t)}};ef.className="SeparableConv2D";Q.registerClass(ef);var Su=class extends Iu{constructor(t){super(1,t),Su.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Cy(t.kernelSize,"number",1,1))throw new M(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};Su.className="Conv1D";Q.registerClass(Su);var rf=class extends $t{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=Nt(t),this.dataFormat==="channelsLast"){let n=wh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return wh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=wh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return wh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};rf.className="Cropping2D";Q.registerClass(rf);var nf=class extends $t{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Fe(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,E$(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=Nt(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Ot(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?Gs.resizeNearestNeighbor(n,[s,i]):Gs.resizeBilinear(n,[s,i]);return Ot(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?Gs.resizeNearestNeighbor(n,[s,i]):Gs.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};nf.className="UpSampling2D";Q.registerClass(nf);function Z8(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=mn()),Fe(o);let i=Ah(r,o);if(r.rank!==4)throw new M(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new M(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Fi(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Ot(i,[0,3,1,2])),i})}var of=class extends bc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=de(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Be(t.depthwiseConstraint),this.depthwiseRegularizer=be(t.depthwiseRegularizer)}build(t){if(t=Bt(t),t.length<4)throw new M(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new M(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,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(t,e){return B(()=>{t=Nt(t);let n=Z8(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=fn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Bt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Nn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Nn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=ze(this.depthwiseRegularizer),t}};of.className="DepthwiseConv2D";Q.registerClass(of);function Bv(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new M("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function Vv(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new M(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(Zr(2,u));if(t=Ot(t,l),s!=null)throw new St("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===u-1&&(o=rr(o,-1)),o=Ot(o,l)),n&&(t=pr(t,0),o!=null&&(o=pr(o,0)));let c=[],p,m=e,f=t.shape[0],d=vr(t),h;o!=null&&(h=vr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let C=B(()=>{let N=h[x],_=ct(yr(N),N),A=X(D(w[0],N),D(m[0],_)),$=m.map((F,P)=>X(D(w[1][P],N),D(F,_)));return{output:A,newStates:$}});p=C.output,m=C.newStates}a&&c.push(p)}let g;return a&&(g=nr(c,1)),[p,g,m]})}var Tn=class extends $t{constructor(t){super(t);let e;if(t.cell==null)throw new M("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Ic({cells:t.cell}):e=t.cell,e.stateSize==null)throw new M("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new ye({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Zr(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){$y(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new M(`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=i.map(a=>new ye({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new vn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new M("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(o=>Ne([n,o])):this.states_=[Ne([n,this.cell.stateSize])];else if(t==null)vt(this.states_),this.keptStates!=null&&(vt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Ne([n,o])):this.states_[0]=Ne([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new M(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):vt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=Bv(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new ye({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof Jr){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=Nt(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new M(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=Vv((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Ne(t.shape);return e=ft(e,[1,2]),e=nl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Ey(e,[1,n]):e):this.cell.stateSize>1?[Ey(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Tn.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=gn(o,n);return new t(Object.assign(e,{cell:s}))}};Tn.className="RNN";Q.registerClass(Tn);var ll=class extends $t{},wc=class extends ll{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Ze(this.units,"units"),this.activation=Xs(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=be(t.kernelRegularizer),this.recurrentRegularizer=be(t.recurrentRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.kernelConstraint=Be(t.kernelConstraint),this.recurrentConstraint=Be(t.recurrentConstraint),this.biasConstraint=Be(t.biasConstraint),this.dropout=ac([1,qs([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=ac([1,qs([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Bt(t),this.kernel=this.addWeight("kernel",[t[t.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(t,e){return B(()=>{if(t=t,t.length!==2)throw new M(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0yr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0yr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=To(D(t,i),this.kernel.read()):s=To(t,this.kernel.read()),this.bias!=null&&(s=fn(s,this.bias.read())),a!=null&&(n=D(n,a));let u=X(s,To(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:js(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:ze(this.kernelConstraint),recurrentConstraint:ze(this.recurrentConstraint),biasConstraint:ze(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};wc.className="SimpleRNNCell";Q.registerClass(wc);var sf=class extends Tn{constructor(t){t.cell=new wc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(vt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(vt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};sf.className="SimpleRNN";Q.registerClass(sf);var Cc=class extends ll{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new M("GRUCell does not support reset_after parameter set to true.");this.units=t.units,Ze(this.units,"units"),this.activation=Xs(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Xs(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=be(t.kernelRegularizer),this.recurrentRegularizer=be(t.recurrentRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.kernelConstraint=Be(t.kernelConstraint),this.recurrentConstraint=Be(t.recurrentConstraint),this.biasConstraint=Be(t.biasConstraint),this.dropout=ac([1,qs([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=ac([1,qs([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Bt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,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(t,e){return B(()=>{if(t=t,t.length!==2)throw new M(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0yr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0yr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(vt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(vt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};af.className="GRU";Q.registerClass(af);var ul=class extends ll{constructor(t){super(t),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=t.units,Ze(this.units,"units"),this.activation=Xs(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Xs(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=be(t.kernelRegularizer),this.recurrentRegularizer=be(t.recurrentRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.kernelConstraint=Be(t.kernelConstraint),this.recurrentConstraint=Be(t.recurrentConstraint),this.biasConstraint=Be(t.biasConstraint),this.dropout=ac([1,qs([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=ac([1,qs([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Bt(t);let n=t[t.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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends dn{apply(u,l){let c=s.apply([i]),p=new yu().apply([i]),m=s.apply([i*2]);return Tv(Tv(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new M(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0yr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0yr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(vt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(vt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};lf.className="LSTM";Q.registerClass(lf);var Ic=class extends ll{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{Hs(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(gn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Ih(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Ay(t(),e),a=()=>xu(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var J8=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(vt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(vt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new M("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Ne(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new vn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new M("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(()=>Ne(s)):this.states_=[Ne(s)];else if(t==null)vt(this.states_),this.keptStates!=null&&(vt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ne(s)):this.states_[0]=Ne(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new M(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):vt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=Nn(l,o[0],s,i[0],a[0]),m=Nn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};db.className="ConvRNN2D";var Sc=class extends ul{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Ze(this.filters,"filters"),this.kernelSize=Cu(n,2,"kernelSize"),this.kernelSize.forEach(u=>Ze(u,"kernelSize")),this.strides=Cu(o||1,2,"strides"),this.strides.forEach(u=>Ze(u,"strides")),this.padding=s||"valid",pn(this.padding),this.dataFormat=i||"channelsLast",Fe(this.dataFormat),this.dilationRate=Cu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>Ze(u,"dilationRate"))}build(t){var e;t=Bt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new M(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends dn{apply(m,f){let d=l.apply([c]),h=cr([c]),g=l.apply([c*2]);return Nm([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new M(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0yr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(rt,ot,at)=>!ot||!ot[at]?rt:D(ot[at],rt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0yr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[C,N,_,A]=mr(this.kernel.read(),a,w),[$,F,P,V]=this.useBias?mr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,C,$,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,_,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=mr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let j=this.recurrentActivation.apply(X(c,h)),Y=this.recurrentActivation.apply(X(p,g)),Z=X(D(Y,i),D(j,this.activation.apply(X(m,x)))),et=D(this.recurrentActivation.apply(X(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=J8(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=In(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?fn(s,n,this.dataFormat):s}recurrentConv(t,e){return In(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Sc.className="ConvLSTM2DCell";Q.registerClass(Sc);var uf=class extends db{constructor(t){let e=new Sc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};uf.className="ConvLSTM2D";Q.registerClass(uf);var vc=class extends $t{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=Nt(t);if(0Ay(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};vc.className="Dropout";Q.registerClass(vc);var cf=class extends vc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};cf.className="SpatialDropout1D";Q.registerClass(cf);var pf=class extends $t{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let 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t={units:this.units,activation:js(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:ze(this.kernelConstraint),biasConstraint:ze(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};pf.className="Dense";Q.registerClass(pf);var mf=class extends $t{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Bt(t);for(let e of t.slice(1))if(e==null)throw new M(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). 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$t{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t);return xu(()=>X(Tm(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Tf.className="GaussianNoise";Q.registerClass(Tf);var kf=class extends $t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t);return this.rate>0&&this.rate<1?xu(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return D(n,Tm(n.shape,1,s))},()=>n,e.training||!1):n})}};kf.className="GaussianDropout";Q.registerClass(kf);var Ef=class extends $t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||Nt(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return xu(()=>{let s=Nt(t),i=1.6732632423543772,a=1.0507009873554805,u=-i*a,l=ln(zi(n),this.rate);l=no(l,"float32");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=X(D(s,l),D(X(l,-1),u));return X(D(m,c),p)},()=>Nt(t),e.training||!1)}return t})}};Ef.className="AlphaDropout";Q.registerClass(Ef);function Dh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=xx(r,t,e,n,o,s);else if(r.rank===3)i=yx(r,t,e,n,o,s);else if(r.rank===4)i=bx(r,t,e,n,o,s);else throw new St(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function tY(r,t,e,n,o=.001){return B(()=>{let s=Zu(r,n),i=s.mean,a=s.variance;return[Dh(r,i,a,e,t,o),i,a]})}function eY(r,t,e,n,o=.001){return B(()=>{let s=Zu(r,n),i=s.mean,a=s.variance,u=[];for(let d of 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new M(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new ye({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=Nt(t),s=o.shape,i=s.length,a=Zr(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=Io(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,Zr(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:ze(this.betaConstraint),gammaConstraint:ze(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};_f.className="BatchNormalization";Q.registerClass(_f);var Af=class extends $t{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||"zeros"),this.gammaInitializer=de(t.gammaInitializer||"ones"),this.betaRegularizer=be(t.betaRegularizer),this.gammaRegularizer=be(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Bt(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==vo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=Nt(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=Zu(n,this.axis,!0),l=Io(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new M(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new M("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=mn()),e!=="channelsLast"&&e!=="channelsFirst")throw new M(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new ye({ndim:4})]}computeOutputShape(t){t=Bt(t);let e,n;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>nY(Nt(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};$f.className="ZeroPadding2D";Q.registerClass($f);function wb(r,t,e,n,o,s){return B(()=>{Fe(o),Iv(s),pn(n),e==null&&(e=[1,1]),n==null&&(n="valid"),o==null&&(o=mn()),s==null&&(s="max"),r=Ah(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=ru(r,t,e,a):i=Yl(r,t,e,a),o==="channelsFirst"&&(i=Ot(i,[0,3,1,2])),i})}function CD(r,t,e,n,o,s){return B(()=>{Fe(o),Iv(s),pn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=mn()),s==null&&(s="max"),r=zv(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Hx(r,t,e,a):i=gx(r,t,e,a),o==="channelsFirst"&&(i=Ot(i,[0,4,1,2,3])),i})}var hb=class extends $t{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new M(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(Ze(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new M(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);Ze(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,pn(this.padding),this.inputSpec=[new ye({ndim:3})]}computeOutputShape(t){t=Bt(t);let e=Nn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=nl(Nt(t),2);let n=this.poolingFunction(Nt(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Mn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Df=class extends hb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),pn(o),wb(t,e,n,o,s,"max")}};Df.className="MaxPooling1D";Q.registerClass(Df);var Rf=class extends hb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),pn(o),wb(t,e,n,o,s,"avg")}};Rf.className="AveragePooling1D";Q.registerClass(Rf);var gb=class extends $t{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new M(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];Ze(this.poolSize,"poolSize"),Ze(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Fe(this.dataFormat),pn(this.padding),this.inputSpec=[new ye({ndim:4})]}computeOutputShape(t){t=Bt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=Nn(e,this.poolSize[0],this.padding,this.strides[0]),n=Nn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(Nt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Ff=class extends gb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),pn(o),wb(t,e,n,o,s,"max")}};Ff.className="MaxPooling2D";Q.registerClass(Ff);var Of=class extends gb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),pn(o),wb(t,e,n,o,s,"avg")}};Of.className="AveragePooling2D";Q.registerClass(Of);var xb=class extends $t{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new M(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Ze(this.poolSize,"poolSize"),Ze(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Fe(this.dataFormat),pn(this.padding),this.inputSpec=[new ye({ndim:5})]}computeOutputShape(t){t=Bt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=Nn(e,this.poolSize[0],this.padding,this.strides[0]),n=Nn(n,this.poolSize[1],this.padding,this.strides[1]),o=Nn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(Nt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Pf=class extends xb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),pn(o),CD(t,e,n,o,s,"max")}};Pf.className="MaxPooling3D";Q.registerClass(Pf);var Lf=class extends xb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),pn(o),CD(t,e,n,o,s,"avg")}};Lf.className="AveragePooling3D";Q.registerClass(Lf);var yb=class extends $t{constructor(t){super(t),this.inputSpec=[new ye({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new St}},Mf=class extends yb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=Nt(t);return ve(n,1)})}};Mf.className="GlobalAveragePooling1D";Q.registerClass(Mf);var zf=class extends yb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=Nt(t);return Ir(n,1)})}};zf.className="GlobalMaxPooling1D";Q.registerClass(zf);var bb=class extends $t{constructor(t){super(t),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Fe(this.dataFormat),this.inputSpec=[new ye({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new St}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Bf=class extends bb{call(t,e){return B(()=>{let n=Nt(t);return this.dataFormat==="channelsLast"?ve(n,[1,2]):ve(n,[2,3])})}};Bf.className="GlobalAveragePooling2D";Q.registerClass(Bf);var Vf=class extends bb{call(t,e){return B(()=>{let n=Nt(t);return this.dataFormat==="channelsLast"?Ir(n,[1,2]):Ir(n,[2,3])})}};Vf.className="GlobalMaxPooling2D";Q.registerClass(Vf);var Cb=class extends $t{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}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(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=gn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Gf=class extends Cb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Bt(t),t.length<3)throw new M(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Bt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=Nt(t),Vv((i,a)=>[Nt(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Gf.className="TimeDistributed";Q.registerClass(Gf);function oY(r){Wi(T$,"BidirectionalMergeMode",r)}var sY="concat",Wf=class extends Cb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=gn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=gn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?sY:t.mergeMode,oY(this.mergeMode),t.weights)throw new St("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Nr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=Bv(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new M("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=n,i.push(...n);let c=n.map(p=>new ye({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new St("Support for constants in Bidirectional layers is not implemented yet.");let u=i[0]instanceof Jr;for(let l of i)if(l instanceof Jr!==u)throw new M("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=pr(s,1));let a;return this.mergeMode==="concat"?a=Nm([o,s]):this.mergeMode==="sum"?a=X(o,s):this.mergeMode==="ave"?a=D(.5,X(o,s)):this.mergeMode==="mul"?a=D(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){Hs(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),Hs(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=gn(e.layer);if(delete e.layer,e.numConstants!=null)throw new St("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=n,new t(o)}};Wf.className="Bidirectional";Q.registerClass(Wf);var Uf=class extends $t{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=Nt(t),t.dtype!=="float32"&&(t=no(t,"float32")),X(D(t,this.scale),this.offset)))}};Uf.className="Rescaling";Q.registerClass(Uf);var iY=["bilinear","nearest"],ID=new Set(iY),Hf=class extends $t{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(ID.has(t.interpolation))this.interpolation=t.interpolation;else throw new M(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation="bilinear";this.cropToAspectRatio=Boolean(t.cropToAspectRatio)}computeOutputShape(t){t=Bt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation==="bilinear")return Gs.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation==="nearest")return 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this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function dN(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=Object.keys(r).map(m=>xn(m)[0]),c=[];n!=null&&(c=n.map(m=>xn(m.name)[0]));let p=[...t];for(;p.length>0;){let m=p.pop();if((hN(m)||R7(m)||F7(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&l.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function uR(r,t,e){let{usedNodes:n,inputs:o}=e,s=[],i=Object.keys(o).map(c=>xn(c)[0]).map(c=>r.nodes[c]),a=r.initNodes;i.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),a!=null&&a.forEach(c=>{n.has(c.name)&&s.push(c)});let u=new Set,l=[];for(;s.length>0;){let c=s.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>u.has(m.name))&&s.push(p)})}return l}var A7=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],$7=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],D7=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function hN(r){return A7.indexOf(r.op)>=0}function R7(r){return $7.indexOf(r.op)>=0}function F7(r){return D7.indexOf(r.op)>=0}var Nc=class{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new Nc(t.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(t,e){let n=dN(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let a=e.map(l=>l.name),u=Object.keys(t);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${u}]. Missing the following inputs: [${o}]`)}return uR(this.graph,this.weightMap,n)}execute(t,e){t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(p=>this.graph.nodes[xn(p)[0]]),s=e.map(p=>xn(p)[0]),i=s.map(p=>this.graph.nodes[p]);this.resetIntermediateTensors(),i.length===0&&(i=this._outputs);let a=this.getCompilationKey(o,i),u=this.compiledMap.get(a);u==null&&(u=this.compile(t,i),this.compiledMap.set(a,u));let l={},c={};return B(()=>{let p=new Oh(this.weightMap,l,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);Object.keys(t).forEach(h=>{let[g,x]=xn(h),b=[];b[x]=t[h],m[g]=b});let f=this.getFrozenTensorIds(m),d={};for(let h=0;hbr(h,m,p))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){e.category==="control"||i.indexOf(t)!==-1||(n[t].forEach(u=>{u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length)}),e.inputs.forEach(u=>{if(u.category!=="control"){let l=FD(u.name,n,o);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let p=a[c.id];if(p===1){if(!this.keepTensorForDebug)c.dispose();else{let[m,f]=_o(e.name,o);this.intermediateTensors[m]?this.intermediateTensors[m][f]=c:(this.intermediateTensors[m]=[],this.intermediateTensors[m][f]=c)}delete a[c.id]}else p!=null&&a[c.id]--}})}}))}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(t=>this.intermediateTensors[t].forEach(e=>e.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(t=>{this.tensorsMap[t].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let t in this.intermediateTensors)this.intermediateTensors[t].forEach(e=>e.dispose()),delete this.intermediateTensors[t]}async _executeAsync(t,e,n=!1,o={},s={}){n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepTensorForDebug=z().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){console.warn(c.message)}this.resetIntermediateTensors();let i=new Oh(this.weightMap,o,s,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(t,i,e,n);let a=e.map(c=>br(c,this.tensorsMap,i)),u=a.map(c=>c.id),l=Object.keys(t).map(c=>t[c].id);return this.keepIds=new Set([...u,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&i.dispose(this.keepIds),a}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(w=>this.graph.nodes[xn(w)[0]]),a=n.map(w=>xn(w)[0]),u=a.map(w=>this.graph.nodes[w]);u.length===0&&(u=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:p,syncInputs:m}=dN(t,u,this.weightMap,this._initNodes),f=[...i,...this.graph.weights,...this._initNodes||[]].map(w=>({node:w,contexts:e.currentContext})),d=Object.assign({},this.weightMap);Object.keys(t).forEach(w=>{let[C,N]=xn(w),_=[];_[N]=t[w],d[C]=_});let h={},g=this.getFrozenTensorIds(d),x={};for(;f.length>0;){let w=this.processStack(i,f,e,d,x,g,a,h,l);await Promise.all(w)}p==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=u.filter(w=>!hN(w)&&!br(w.name,d,e)).map(w=>w.name);if(b.length>0){let w="";throw p!=null&&(w=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${w}`)}return d}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&S("isConstant",p.node,o,n)&&([m]=_o(p.node.name,n)),o[p.node.name]==null){let f=fN(p.node,o,n,this._resourceManager);m||([m]=_o(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=_o(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!br(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!br(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=xn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){let e={};for(let n in t)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let o=this._signature.inputs[n];e[o.name]=t[n]}else e[n]=t[n];return e}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=xn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[e]!=null?this._signature.outputs[e].name:e,{})}checkOutputs(t){t.forEach(e=>{let[n]=xn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var Vb=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in 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if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Nc(Fh.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Fh.Instance.transformGraph(t.modelInitializer);this.initializer=new Nc(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}predict(t,e){let n=this.execute(t,this.outputNodes);if(this.structuredOutputKeys){let o=n instanceof Ft?[n]:n,s={};return o.forEach((i,a)=>s[this.structuredOutputKeys[a]]=i),s}return n}normalizeInputs(t){if(!(t instanceof Ft)&&!Array.isArray(t)){if(this.signature!=null&&this.signature.inputs!=null)for(let o in this.signature.inputs){let s=this.signature.inputs[o];s.resourceId!=null&&(t[o]=this.resourceIdToCapturedInput[s.resourceId])}return t}t=Array.isArray(t)?t:[t];let e=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+e!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-e} non-resource placeholders, while there are ${t.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((o,s)=>{let i=this.signature?this.signature.inputs[s]:null;return i!=null&&i.resourceId!=null?o[s]=this.resourceIdToCapturedInput[i.resourceId]:o[s]=t[n++],o},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=Object.keys(this.initializerSignature.outputs);for(let n=0;n1?n:n[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=await this.executor.executeAsync(t,e);return n.length>1?n:n[0]}getIntermediateTensors(){return 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jf{constructor(){super(Tc.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;oe===!0)}rowMajorBatch(t,e=!0){return new SN(this,t,e)}columnMajorBatch(t,e=!0,n=xN){return this.rowMajorBatch(t,e).map(s=>mR(s,n))}concatenate(t,e){return new Hb(AN([this,t]),e)}take(t){return t<0||t==null?this:new IN(this,t)}skip(t){return t<0||t==null?this:new CN(this,t)}prefetch(t){return new qb(this,t)}shuffle(t,e){return new _N(this,t,e)}serial(){return new wN(this)}},yN=class extends Je{constructor(t){super(),this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:hR(t),done:!1}}},bN=class extends Je{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return 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Je{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},vN=class extends Je{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;vt(t.value)}}},NN=class extends Je{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=go.getTensorsInContainer(t.value),n=this.transform(t.value),o=go.getTensorsInContainer(n);for(let s of e)go.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},TN=class extends Je{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},Ub=class extends Je{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=go.getTensorsInContainer(t.value),n=await this.transform(t.value),o=go.getTensorsInContainer(n);for(let s of e)go.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},kc=class extends Je{constructor(){super(),this.outputQueue=new Tc,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}}},kN=class extends kc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=go.getTensorsInContainer(t.value),n=this.transform(t.value),o=go.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)go.isTensorInList(s,o)||s.dispose();return!0}},Hb=class extends Je{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries 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Wb(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case fl.FAIL:throw new Error(`Zipped streams should have the same length. 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!z().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new Zf(t);return await e.start(),e}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.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),ur(n,e)}};var Jf=class extends Je{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Me([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=Vs([i,s,u,a],[1,4])}else this.cropBox=Vs([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!z().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new Jf(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=nx.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=rr(J(t,"float32"),0),n;n=Gs.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var Qf=class{};var zh=class extends Je{split(t){return new DN(this,t)}},DN=class extends 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c=v.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,C=c.effectiveFilterDepth,N=c.effectiveFilterHeight,_=c.effectiveFilterWidth,A=C-1-c.padInfo.front,$=_-1-c.padInfo.left,F=N-1-c.padInfo.top,P=wt(s.shape,"float32"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W=c.outDepth||Math.floor(nt)!==nt))for(let it=0;it=c.outHeight||Math.floor(dt)!==dt))for(let ht=0;ht<_;ht+=w){let bt=(rt+ht)/f;if(bt<0||bt>=c.outWidth||Math.floor(bt)!==bt)continue;ot+=G.get(W,nt,dt,bt,q)}}}P.set(ot*V,W,H,j,Y,q)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var PF={kernelName:lp,backendName:"cpu",kernelFunc:LJ};function MJ(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=v.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,C=x-1-c.padInfo.top,N=wt(i.shape,"float32"),_=1/(f*d),A=e.data.get(o.dataId).values,$=wt(o.shape,"float32",A);for(let F=0;F=c.outHeight||Math.floor(Y)!==Y))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;H+=$.get(F,Y,et,P)}}N.set(H*_,F,V,G,P)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var LF={kernelName:ap,backendName:"cpu",kernelFunc:MJ};function zJ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),tt([o,a,u,s,i],"batchNorm");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=e.data.get(o.dataId).values,p=e.data.get(a.dataId).values,m=e.data.get(u.dataId).values,f=s?e.data.get(s.dataId).values:new Float32Array([1]),d=i?e.data.get(i.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,w=p.length,C=0,N=0,_=0,A=0;for(let $=0;$=g&&(C=0),N>=w&&(N=0),_>=x&&(_=0),A>=b&&(A=0);return e.makeTensorInfo(o.shape,o.dtype,h)}var MF={kernelName:os,backendName:"cpu",kernelFunc:zJ};function BJ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;tt([o],"batchToSpaceND");let a=s.reduce((x,b)=>x*b),u=v.getReshaped(o.shape,s,a),l=v.getPermuted(u.length,s.length),c=v.getReshapedPermuted(o.shape,s,a),p=v.getSliceBeginCoords(i,s.length),m=v.getSliceSize(c,i,s.length),f=Yt({inputs:{x:o},backend:e,attrs:{shape:u}}),d=Ve({inputs:{x:f},backend:e,attrs:{perm:l}}),h=Yt({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Fo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var zF={kernelName:ai,backendName:"cpu",kernelFunc:BJ};function VJ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=sd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var BF={kernelName:up,backendName:"cpu",kernelFunc:VJ};function GJ(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=v.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var VF={kernelName:cp,backendName:"cpu",kernelFunc:GJ};var WJ=kt(uo,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);v.assertParamsConsistent(i,s);let a=v.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Kr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(C=>Ao({inputs:{input:C},backend:e})),g=u.map(C=>ji({inputs:{input:C},backend:e})),x=Tu({inputs:h,backend:e,attrs:{axis:s}}),b=Tu({inputs:g,backend:e,attrs:{axis:s}}),w=wr({inputs:{real:x,imag:b},backend:e});return h.forEach(C=>e.disposeIntermediateTensorInfo(C)),g.forEach(C=>e.disposeIntermediateTensorInfo(C)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let g=y.sizeFromShape(h.shape.slice(s));return Yt({inputs:{x:h},backend:e,attrs:{shape:[-1,g]}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=v.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Ec(c,a,t[0].dtype,p),f=v.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var HF={kernelName:li,backendName:"cpu",kernelFunc:Tu};function dT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=v.convertConv2DDataFormat(u),m=v.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",C=new pe(m.outShape,o.dtype),N=y.computeStrides(o.shape),_=y.computeStrides(s.shape),A=N[0],$=w?N[1]:N[2],F=w?N[2]:1,P=w?1:N[1],V=C.strides[0],G=w?C.strides[1]:C.strides[2],W=w?C.strides[2]:1,q=w?1:C.strides[1],H=e.data.get(o.dataId).values,j=e.data.get(s.dataId).values,Y=C.values;for(let Z=0;Z=m.inHeight)continue;let ht=it*_[0],bt=et+dt*$;for(let Et=0;Et=m.inWidth)continue;let he=ht+Zt*_[1],jt=bt+ce*F,ke=he;for(let fe=0;fe=l.inDepth)continue;let Z=j*F[0],et=V+Y*$[1];for(let rt=0;rt=l.inHeight)continue;let dt=Z+nt*F[1],ht=et+it*$[2];for(let bt=0;bt=l.inWidth)continue;let ce=dt+Vt*F[2],he=ht+Zt*l.inChannels,jt=ce;for(let ke=0;keMath.cos(r)),JF={kernelName:Xo,backendName:"cpu",kernelFunc:YJ};var ZJ=kt(Yo,r=>Math.cosh(r)),QF={kernelName:Yo,backendName:"cpu",kernelFunc:ZJ};function JJ(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,C=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),_=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let j=0;j1?F*(p-1)+j*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?P*(m-1)+ot*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let ht=0;ht1?P*(m-1)+Z*H:.5*(P+G)*(m-1);if(et<0||et>m-1){for(let at=0;atx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=v.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,C=b.top,N=f.outChannels/f.inChannels,_=new pe(f.outShape,o.dtype),A=e.data.get(o.dataId).values,$=e.data.get(s.dataId).values,F=_.values;for(let P=0;P=f.inHeight)continue;let Z=j*p[0],et=V+Y*c[1];for(let rt=0;rt=f.inWidth)continue;let dt=Z+nt*p[1],ht=et+it*f.inChannels,bt=ot,Et=dt;for(let At=0;At{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:C,strideWidth:N,filterHeight:_,filterWidth:A,dilationHeight:$,dilationWidth:F,outShape:P}=v.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(P),G=P.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&it=0&&htot&&(ot=At)}}}let at=y.locToIndex([H,j,Z,rt],G,y.computeStrides(P));W[at]=ot}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}};var cO={kernelName:Xd,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:C,filterHeight:N,filterWidth:_,dilationHeight:A,dilationWidth:$,outShape:F}=v.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Xd}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&nt=0&&dtet&&(et=ht,rt=at,ot=it)}}}V[rt][ot][Z]+=P[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var pO={kernelName:jd,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:C,filterHeight:N,filterWidth:_,dilationHeight:A,dilationWidth:$,outShape:F}=v.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${jd}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&nt=0&&dtet&&(et=ht,rt=nt,ot=dt)}}}V[W][rt][ot][Z]+=P[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function hl(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"sum");let a;o.dtype==="bool"?a=$o({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=Kr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=v.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=Ve({inputs:{x:a},backend:e,attrs:{perm:c}}),p=v.getInnerMostAxes(p.length,u)),v.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=v.computeOutAndReduceShapes(m.shape,p),h=v.upcastType(m.dtype,"int32"),g=nd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let C=0;C=0&&(m=hl({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var fO={kernelName:bp,backendName:"cpu",kernelFunc:iQ};function aQ(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],"eluGrad");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=1?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,"float32",s)}var dO={kernelName:wp,backendName:"cpu",kernelFunc:aQ};var lQ=v.ERF_P,uQ=v.ERF_A1,cQ=v.ERF_A2,pQ=v.ERF_A3,mQ=v.ERF_A4,fQ=v.ERF_A5,dQ=kt(ga,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+lQ*e);return t*(1-((((fQ*n+mQ)*n+pQ)*n+cQ)*n+uQ)*n*Math.exp(-e*e))}),hO={kernelName:ga,backendName:"cpu",kernelFunc:dQ};function cd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Yt({inputs:{x:o},backend:e,attrs:{shape:a}})}var gO={kernelName:ui,backendName:"cpu",kernelFunc:cd};var hQ=Qt((r,t)=>r/t),Wh=oe(Qo,hQ),Uh={kernelName:Qo,backendName:"cpu",kernelFunc:Wh};function hw(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&wMath.floor(r/t)),IQ=oe(ns,CQ,null,"int32"),wO={kernelName:ns,backendName:"cpu",kernelFunc:IQ};function SQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=dT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;if(c==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let x=Yt({inputs:{x:i},backend:e,attrs:{shape:[i.shape[0],1,1]}});h=Ki({inputs:{a:h,b:x},backend:e}),e.disposeIntermediateTensorInfo(x)}else h=Ki({inputs:{a:h,b:i},backend:e});e.disposeIntermediateTensorInfo(g)}if(f){let g=h;if(c==="NCHW"&&f==="prelu"&&a.shape.length===1&&a.shape[0]!==1){let x=Yt({inputs:{x:a},backend:e,attrs:{shape:[a.shape[0],1,1]}});h=Oc(e,h,f,x,d),e.disposeIntermediateTensorInfo(x)}else h=Oc(e,h,f,a,d);e.disposeIntermediateTensorInfo(g)}return h}var CO={kernelName:Ii,backendName:"cpu",kernelFunc:SQ};function vQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=hT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;h=Ki({inputs:{a:h,b:i},backend:e}),e.disposeIntermediateTensorInfo(g)}if(f){let g=h;h=Oc(e,h,f,a,d),e.disposeIntermediateTensorInfo(g)}return h}var IO={kernelName:Si,backendName:"cpu",kernelFunc:vQ};function NQ(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=y.sizeFromShape(n.shape),i=o.shape,a=i[i.length-1],[u,l,c,p]=v.prepareAndValidate(n,o);if(l===0)return e.makeTensorInfo(u,n.dtype,[]);let m=e.data.get(o.dataId).values,f=e.bufferSync(n),d=Qb(m,f,n.dtype,l,a,c,p,n.shape,s);return e.makeTensorInfo(u,n.dtype,d.values)}var SO={kernelName:wa,backendName:"cpu",kernelFunc:NQ};function TQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n;tt([o,s],"gatherV2");let u=y.parseAxisParam(i,o.shape)[0],l=e.data.get(s.dataId).values,c=o.shape[u];for(let C=0;C=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=v.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=Yt({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=Yt({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=tw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var vO={kernelName:ci,backendName:"cpu",kernelFunc:TQ};function kQ(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=Yt({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=hw(a,!0,e),l=Yt({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var NO={kernelName:Ip,backendName:"cpu",kernelFunc:kQ};var EQ=kt(Ia,r=>Number.isFinite(r)?1:0,"bool"),TO={kernelName:Ia,backendName:"cpu",kernelFunc:EQ};var 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0); } `}function Ctt(r){return` void setOutput(vec4 val) { ${r.output} = val; } `}function Itt(r){return`${r.version} precision highp float; precision highp int; precision highp sampler2D; ${r.varyingFs} vec2 resultUV; ${r.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; ${r.defineSpecialNaN} ${r.defineSpecialInf} ${r.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); } ${Stt} ${vtt} ${Ntt} `}var Stt=` vec2 uvFromFlat(int texNumR, int texNumC, int 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getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? 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yt(e,()=>e.attachShader(n,this.vertexShader)),yt(e,()=>e.attachShader(n,t)),kT(e,n),this.debug&&Yh(e,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=JT(e,this.program,this.vertexBuffer)),n}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&yt(this.gl,()=>this.gl.deleteProgram(t))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&Yh(this.gl,this.program),yt(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?RT(this.gl,t,e):FT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),yt(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),OT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=Xi(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Yh(this.gl,this.program),md(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;this.debug&&this.debugValidate(),yt(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),yt(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return 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y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=tet(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in z().platform&&(n=z().platform.setTimeoutCustom.bind(z().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),Zh(this.gl,t,this.framebuffer),this.debug&&md(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Zh(this.gl,this.outputTexture,this.framebuffer),this.debug&&md(this.gl)):Sw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;Zh(o,t,this.framebuffer),this.debug&&md(o),this.outputTexture=t,yt(o,()=>o.viewport(0,0,e,n)),yt(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),yt(this.gl,()=>this.gl.scissor(t,e,n,o))}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 tet(r){let t=0;for(;t`${r}.${e}`)}function Qe(r,t){return t===1?[r]:ak(r,t)}function QL(r,t){if(r===1)return"rc";let e="";for(let n=0;n ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let e="";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n}; bool rEdge = rp1 >= ${o}; `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}), cEdge ? 0. : getA(${e[1]}), rEdge ? 0. 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o===Pr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Pr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Pr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Pr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Pr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=eM(n,o),i=rM(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=tM(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=z().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l.indexOf(t);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function ret(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function tM(r,t,e,n,o){let s=net(t,n),i;if(o){let[u,l]=Xi(r[0],r[1]);i=u*l}else{let[u,l]=Lc(r[0],r[1]);i=u*l}let a=ret(e,s);return i*a}function net(r,t){switch(r){case Pr.PACKED_2X2_FLOAT32:return Ow(t);case Pr.PACKED_2X2_FLOAT16:return Pw(t);case Pr.UNPACKED_FLOAT32:return Dw(t);case Pr.UNPACKED_FLOAT16:return Rw(t);case Pr.PACKED_4X1_UNSIGNED_BYTE:return Fw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function oet(r){return z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Pr.PACKED_2X2_FLOAT32:Pr.UNPACKED_FLOAT32:r?Pr.PACKED_2X2_FLOAT16:Pr.UNPACKED_FLOAT16}function eM(r,t){if(r===jr.UPLOAD)return Pr.PACKED_2X2_FLOAT32;if(r===jr.RENDER||r==null)return oet(t);if(r===jr.DOWNLOAD||r===jr.PIXELS)return Pr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function rM(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var tn=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=we(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${e} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},fr="if (isnan(x)) return x;",nM="return x;",lk="return abs(x);";var oM="return (x >= 0.0) ? x : (exp(x) - 1.0);",sM=fr+` return (x < 0.0) ? 0.0 : x; `,iM=fr+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Gc="return x;",aM="return 1.0 / (1.0 + exp(-1.0 * x));";var uM="return x;",cM=` vec4 result; result.r = (x.r >= 0.0) 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WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!z().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof Bc)e=t;else{let n=Gn(z().getNumber("WEBGL_VERSION"),t);e=new Bc(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Gn(z().getNumber("WEBGL_VERSION"));e=new Bc(n),this.binaryCache=cet(z().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Vw(this.gpgpu),this.numMBBeforeWarning=fet(),this.texData=new ra(this,Pn())}nextDataId(){return _u.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=fd(e),c=new 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Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:jr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(z().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:jr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new so(a,Gc):m=new tn(a,Gc);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=v.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new so(o,Gc):d=new tn(o,Gc);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(z().getBool("DEBUG")&&!z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&z().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(i!=="complex64"&&z().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...jh(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=v.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;yt(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Pn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new so(s,Gc):f=new tn(s,Gc);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Pn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.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 z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=pet){return z().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Pn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new Gw(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Bw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[xl(t.shape),...yl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[xl(e),...yl(e)],i=new Id(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=fd(s),u;o?u=new Ew(a):u=new kw(a);let l=!0,c=[e!=null?e:jh(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===ku.DENSE){let x=i!=null?i:jh(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=z().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Eu(b.shape,x.shape)){let w=x,C=x.shape;x.shape=b.shape,x=this.packedReshape(x,C),l.push(x),b=this.texData.get(x.dataId),w.shape=C}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=uL(t,c,p),f=this.getAndSaveBinary(m,()=>aL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),z().get("ENGINE_COMPILE_ONLY")||lL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=z().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!z().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(z().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),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=B(()=>{if(!z().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=z().getBool("DEBUG");z().set("DEBUG",!1);let e=this.abs(mt(1e-8)).dataSync()[0];if(z().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?aet:uet}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=PT(n,u),e.texShape=p),s!=null){let m=fd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=Xi(p[0],p[1])),u?f=new $w(m,g):f=new Jh(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=jr.PIXELS:w.usage=jr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let C=[[h,d]],N=!0,_=this.runWebGLProgram(f,[b],o,C,N),A=this.texData.get(_.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,z().get("ENGINE_COMPILE_ONLY")?this.disposeData(_.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return this.releaseGPUData(t),e!=null&&(n.values=det(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await gh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Cw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,t]of Object.entries(this.binaryCache)){let{uniformLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,inShapesLocations:i,inTexShapesLocations:a,outShapeLocation:u,outShapeStridesLocation:l,outTexShapeLocation:c}=WT(this.gpgpu,t.program,t.webGLProgram);t.uniformLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.inShapesLocations=i,t.inTexShapesLocations=a,t.outShapeLocation=u,t.outShapeStridesLocation=l,t.outTexShapeLocation=c}}createTensorFromTexture(t,e,n){let{texture:o,height:s,width:i,channels:a}=t,u=Pn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Pn().makeTensorFromDataId(l,e,n,u)}};_u.nextDataId=0;function det(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew _u,2);var t1e={forceHalfFloat:hM};var Sd=` if (isnan(a)) return a; if (isnan(b)) return b; `;var io=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=v.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=we(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${t} } void main() { float a = getAAtOutCoords(); float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } `}};var Yi=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; `;var Oo=class{constructor(t,e,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=v.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=we(s);let i="";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(i=` ${zt(s)} coords = getOutputCoords(); `,s===1)this.enableShapeUniforms?i+=` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; result.w = 0.; `:i+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let u=Qe("coords",s);this.enableShapeUniforms?i+=` bool nextRowOutOfBounds = (${u[s-2]} + 1) >= outShape[${s} - 2]; bool nextColOutOfBounds = (${u[s-1]} + 1) >= outShape[${s} - 1]; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; `:i+=` bool nextRowOutOfBounds = (${u[s-2]} + 1) >= ${this.outputShape[s-2]}; bool nextColOutOfBounds = (${u[s-1]} + 1) >= ${this.outputShape[s-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) { ${t} } void main() { vec4 a = getAAtOutCoords(); vec4 b = getBAtOutCoords(); vec4 result = binaryOperation(a, b); ${i} setOutput(result); } `}};function tr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var gM={kernelName:co,backendName:"webgl",kernelFunc:tr};function En(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=tr({inputs:{x:n},backend:e}),u=tr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var xM={kernelName:pp,backendName:"webgl",kernelFunc:En};var uk="return (a < 0.) ? b * a : a;",ck=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function het(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),a=z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Oo(ck,o.shape,i.shape):new io(uk,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var yM={kernelName:is,backendName:"webgl",kernelFunc:het};var pk="return (a < 0.) ? b * a : a;",mk=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function get(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Oo(mk,n.shape,o.shape):new io(pk,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var bM={kernelName:bs,backendName:"webgl",kernelFunc:get};var Po="if (isnan(x)) return x;";function Ct({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=z().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new so(i.shape,t):c=new tn(i.shape,r),a.runWebGLProgram(c,[i],u)}}function le({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[C,N]=w,_={dataId:C.dataId,dtype:C.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},$=new io(r,u.shape,l.shape);return c.runWebGLProgram($,[_,A],sr(C.dtype,N.dtype))}),b=En({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||sr(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?v.fromUint8ToStringArray(d):d,x=u.dtype==="string"?v.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),C=c.makeTensorInfo(w,p),N=c.texData.get(C.dataId);return N.values=b,C}let m=z().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new Oo(t,u.shape,l.shape,e):f=new io(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function bl(r,t=!1){if(r==="linear")return t?uM:nM;if(r==="relu")return t?pM:sM;if(r==="elu")return t?cM:oM;if(r==="relu6")return t?mM:iM;if(r==="prelu")return t?mk:pk;if(r==="leakyrelu")return t?ck:uk;if(r==="sigmoid")return t?fM:aM;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var vd=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=we(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";a&&(u?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${a} }`:l?g=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${a} }`:g=`vec4 activation(vec4 x) { ${a} }`,x="result = activation(result);");let b=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let w="rc.x",C="rc.x";t[0]`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. 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} else { minMaxValue = ${u}(values, minMaxValue); if (${e==="min"} || ${e==="max"}) { minMaxValue = ${u}(values, minMaxValue); bvec4 isNaN = isnan(values); if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) { minMaxValue = vec4(NAN); } } } `,f="vec4";e==="all"?(a="1.0",m=` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); `,f="bvec4"):e==="any"&&(a="0.0",m=` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); `,f="bvec4");let d="";s%n>0&&(d=` if (inIdx < 0 || inIdx >= ${s}) { 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) { ${d} 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 < ${c}; i += 4) { int inIdx = inOffset + i; ${f} values = ${f}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); ${m} } int inIdx = inOffset + ${c}; if (${p===1}) { ${f} values = ${f}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); ${m} } else if (${p===2}) { ${f} values = ${f}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); ${m} } else if (${p===3}) { ${f} values = ${f}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); ${m} } setOutput(${l}); } `}};function yet(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=v.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Un(r,t,e,n){let o=yet(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=ak("rc",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],_=n?[b,d,m]:[b,m,d],A=st({inputs:{x:r},backend:o,attrs:{shape:N}}),$=st({inputs:{x:t},backend:o,attrs:{shape:_}}),F=[A,$],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?bl(u,!0):null,j=G||W||q||H!=null,Y;if((f===1||d===1)&&V>dk&&j===!1){let et=A,rt=$;e&&(et=Oe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(rt=Oe({inputs:{x:$},backend:o,attrs:{perm:[0,2,1]}}),F.push(rt));let ot=d!==1,at=d===1,nt=et;ot&&(nt=st({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(nt));let it=d===1?2:1,dt=rt;at&&(dt=st({inputs:{x:rt},backend:o,attrs:{shape:[P,1,V]}}),F.push(dt));let ht=eg({inputs:{a:nt,b:dt},backend:o});Y=Wc({inputs:{x:ht},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(ht)}else{let et=sr(r.dtype,t.dtype),rt=new vd(N,_,[P,f,d],e,n,G,H,W,q),ot=[A,$];if(s!=null&&ot.push(s),W&&ot.push(i),q){let at=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));ot.push(at),F.push(at)}Y=o.runWebGLProgram(rt,ot,et)}let Z=st({inputs:{x:Y},backend:o,attrs:{shape:C}});F.push(Y);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function wet(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return Uc({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var kM={kernelName:Ci,backendName:"webgl",kernelFunc:wet};var EM="return abs(x);";function Cet(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=Mw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new so(n.shape,EM):o=new tn(n.shape,EM),e.runWebGLProgram(o,[n],n.dtype)}var _M={kernelName:ii,backendName:"webgl",kernelFunc:Cet};var Iet=fr+` if (abs(x) > 1.) { return NAN; 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int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${o}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); for (int i = 0; i < ${o}; i++) { int inIdx = ${u}; float candidate = getA(batch, inIdx); if (candidate ${a} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}};var Zw=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=zt(u),c=Qe("coords",u),p,m;if(i===1){m=u+1;let $=zt(m);p=` ${$} sourceLocR = ${$}(${c.join()}, 0); ++${c[u-1]}; ${$} sourceLocG = ${$}(${c.join()}, 0); ++${c[u-2]}; ${$} sourceLocA = ${$}(${c.join()}, 0); --${c[u-1]}; ${$} sourceLocB = ${$}(${c.join()}, 0); --${c[u-2]};`}else m=u,p=` ${l} sourceLocR = coords; ++${c[u-1]}; ${l} sourceLocG = coords; ++${c[u-2]}; ${l} sourceLocA = coords; --${c[u-1]}; ${l} sourceLocB = coords; --${c[u-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map($=>"int "+$),g=Qe("sourceLocR",m-1).concat("inIdx.r"),x=Qe("sourceLocG",m-1).concat("inIdx.g"),b=Qe("sourceLocB",m-1).concat("inIdx.b"),w=Qe("sourceLocA",m-1).concat("inIdx.a"),C=n==="max"?"greaterThan":"lessThan",N=o?"":` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), getBestIndicesAChannel(${w.join()})));`,_=`vec4( getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?"":` float getBestIndicesAChannel(${h.join()}) { return getChannel(getBestIndicesA(${f.join()}), vec2(${f.slice(-2).join()})); }`;this.userCode=` float getAChannel(${h.join()}) { return getChannel(getA(${f.join()}), vec2(${f.slice(-2).join()})); } ${A} void main() { ${l} coords = getOutputCoords(); bool hasNextCol = ${c[u-1]} < ${a[u-1]-1}; bool hasNextRow = ${c[u-2]} < ${a[u-2]-1}; ${p} ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d}, sourceLocB${d}, sourceLocA${d}) * ${e}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${_}; for (int i = 0; i < ${e}; i++) { inIdx = srcIdx; ${N} vec4 candidate = ${_}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${C}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); bestValue = vec4(replace.x ? candidate.x : bestValue.x, replace.y ? candidate.y : bestValue.y, replace.z ? candidate.z : bestValue.z, replace.w ? candidate.w : bestValue.w); bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace)); srcIdx++; } setOutput(bestIndex); } `}};function LM(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=v.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new Yw(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=LM(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function MM(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=v.computeOptimalWindowSize(s),a=new Zw(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=MM(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function Jw(r,t,e,n){let o=[e];if(v.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!z().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=v.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=st({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=LM(r,m,n);s.push(f);let d=st({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return MM(r,t,n)}function Aet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=v.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Oe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=v.getInnerMostAxes(i.length,u.shape.length)),v.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=Jw(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var zM={kernelName:Wo,backendName:"webgl",kernelFunc:Aet};function $et(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=v.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Oe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=v.getInnerMostAxes(i.length,u.shape.length)),v.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=Jw(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var BM={kernelName:kl,backendName:"webgl",kernelFunc:$et};var Det=fr+` if (abs(x) > 1.) { return NAN; } return asin(x); `,Ret=Ct({opSnippet:Det}),VM={kernelName:la,backendName:"webgl",kernelFunc:Ret};var Fet=fr+"return log(x + sqrt(x * x + 1.0));",Oet=Ct({opSnippet:Fet}),GM={kernelName:ua,backendName:"webgl",kernelFunc:Oet};var Pet=fr+` return atan(x); `,Let=Ct({opSnippet:Pet}),WM={kernelName:ca,backendName:"webgl",kernelFunc:Let};var Met=Sd+` return atan(a, b); `,zet=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); `+Yi+` return result; `,Bet=le({opSnippet:Met,packedOpSnippet:zet}),UM={kernelName:ma,backendName:"webgl",kernelFunc:Bet};var Vet=fr+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Get=Ct({opSnippet:Vet}),HM={kernelName:pa,backendName:"webgl",kernelFunc:Get};var ei=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let $=">=";this.userCode=` const ivec2 strides = ivec2(${a}, ${u}); const ivec2 pads = ivec2(${f}, ${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 float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; float avgValue = 0.0; for (int wR = 0; wR < ${p}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${m}; wC += ${c}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${t.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 = ${o?s?g:x:`wR * ${m} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let w="max",C=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(C="avgValue / count");let N=Math.floor(i/4)*4,_=i%4,A=` if (${h}) { avgValue += dot(values, ones); } else { minMaxValue = ${w}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${a}, ${u}); const ivec2 pads = ivec2(${f}, ${d}); const float initializationValue = ${b}; 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 >= ${t.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${b}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${p}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${N}; wC += 4) { int xC = xCCorner + wC * ${c}; vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), getValue(batch, xR, xC + 3 * ${c}, d) ); ${A} } int xC = xCCorner + ${N}; if (${_===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${A} } else if (${_===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), initializationValue, initializationValue ); ${A} } else if (${_===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), initializationValue ); ${A} } } setOutput(${C}); } `}},$u=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",C="0.0";if(w||(C="-1.0 / 1e-20"),n){let P=">=";this.userCode=` const ivec3 strides = ivec3(${a}, ${u}, ${l}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); 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 < ${f}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${t.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR += ${p}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${h}; wC += ${m}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${t.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 ${P} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} + wR * ${h} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let N="max",_=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(_="avgValue / count");let A=Math.floor(i/4)*4,$=i%4,F=` if (${w}) { avgValue += dot(values, ones); } else { minMaxValue = ${N}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${a}, ${u}, ${l}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); const float initializationValue = ${C}; 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 >= ${t.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${C}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${f}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${t.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR += ${p}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${A}; wC += 4) { int xC = xCCorner + wC * ${m}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), getValue(batch, xD, xR, xC + 2 * ${m}, ch), getValue(batch, xD, xR, xC + 3 * ${m}, ch) ); ${F} } int xC = xCCorner + ${A}; if (${$===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${F} } else if (${$===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), initializationValue, initializationValue ); ${F} } else if (${$===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), getValue(batch, xD, xR, xC + 2 * ${m}, ch), initializationValue ); ${F} } } setOutput(${_}); } } `}};function Wet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Qs(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(v.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=v.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return tr({inputs:{x:o},backend:e});let p=new ei(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var qM={kernelName:Uo,backendName:"webgl",kernelFunc:Wet};function Uet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=v.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new $u(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var KM={kernelName:El,backendName:"webgl",kernelFunc:Uet};var Qw=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=` const ivec2 pads = ivec2(${c}, ${p}); const float avgMultiplier = float(${m}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${u}; wR += ${i}) { float dyR = float(dyRCorner + wR) / ${o}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${l}; wC+= ${a}) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},tC=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=` const ivec3 pads = ivec3(${d}, ${h}, ${g}); const float avgMultiplier = float(${x}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${p}; wD += ${u}) { float dyD = float(dyDCorner + wD) / ${s}.0; if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${m}; wR += ${l}) { float dyR = float(dyRCorner + wR) / ${i}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${f}; wC += ${c}) { float dyC = float(dyCCorner + wC) / ${a}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); dotProd += dyValue * avgMultiplier; } } } setOutput(dotProd); } `}};function Het(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=v.computePool3DInfo(i.shape,a,u,p,l,c),f=new tC(m);return e.runWebGLProgram(f,[o],i.dtype)}var jM={kernelName:lp,backendName:"webgl",kernelFunc:Het};function qet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;Qs([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=v.computePool2DInfo(i.shape,a,u,1,l),p=new Qw(c);return e.runWebGLProgram(p,[o],i.dtype)}var XM={kernelName:ap,backendName:"webgl",kernelFunc:qet};function Ket(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return Uc({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var YM={kernelName:Ho,backendName:"webgl",kernelFunc:Ket};var eC=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],v.assertAndGetBroadcastShape(t,e),v.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(v.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(v.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${a}; float scale = ${u}; float inv = scale * inversesqrt(variance + float(${i})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}};var rC=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],v.assertAndGetBroadcastShape(t,e),v.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(v.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(v.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { vec4 offset = ${a}; vec4 scale = ${u}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${i})); setOutput((x - mean) * inv + offset); } `}};var jet=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=z().getBool("WEBGL_PACK_NORMALIZATION")?new rC(n.shape,o.shape,s.shape,c,p,u):new eC(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},ZM={kernelName:os,backendName:"webgl",kernelFunc:jet};var nC=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Xet(this.rank),o,s=t.map((i,a)=>`sourceLoc.${hk[a]} = start[${a}] + coords.${hk[a]};`);o=` ${e} sourceLoc; ${e} coords = getOutputCoords(); ${s.join(` `)} `,this.userCode=` void main() { ${o} setOutput(getSource(${n})); } `}},hk=["x","y","z","w","u","v"];function Xet(r){if(r===1)return"sourceLoc";if(r<=6)return hk.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var oC=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=zt(this.rank),n=Qe("coords",this.rank),o=Qe("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=` result.x = ${i}; if (++${n[this.rank-1]} < ${t[this.rank-1]}) { ++${o[this.rank-1]}; result.y = ${i}; --${o[this.rank-1]}; } `,u=this.rank===1?"":` --${n[this.rank-1]}; if (++${n[this.rank-2]} < ${t[this.rank-2]}) { ++${o[this.rank-2]}; result.z = ${i}; if (++${n[this.rank-1]} < ${t[this.rank-1]}) { ++${o[this.rank-1]}; result.w = ${i}; } } `,l=this.rank<=4?`sourceLoc = coords + ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(` `);this.userCode=` void main() { ${e} coords = getOutputCoords(); ${e} sourceLoc; ${l} vec4 result = vec4(0.); ${a} ${u} setOutput(result); } `}};function Yet(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Le.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function ri(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Le.parseSliceParams(o,s,i);if(Le.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=VL(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Le.isSliceContinous(o.shape,a,u);if(l||!c){let p=z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oC(u):new nC(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),Yet(o,a,u,e)}var JM={kernelName:gi,backendName:"webgl",kernelFunc:ri};var Zet=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=v.getReshaped(o.shape,s,a),l=v.getPermuted(u.length,s.length),c=v.getReshapedPermuted(o.shape,s,a),p=v.getSliceBeginCoords(i,s.length),m=v.getSliceSize(c,i,s.length),f=[],d=st({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Oe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=st({inputs:{x:h},backend:e,attrs:{shape:c}}),x=ri({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},QM={kernelName:ai,backendName:"webgl",kernelFunc:Zet};function Jet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Lw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var tz={kernelName:up,backendName:"webgl",kernelFunc:Jet};function Qet(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=v.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var ez={kernelName:cp,backendName:"webgl",kernelFunc:Qet};var trt="return float(a != b);",gk=le({opSnippet:trt,cpuKernelImpl:DL,dtype:"bool"}),rz={kernelName:Da,backendName:"webgl",kernelFunc:gk};function wl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return tr({inputs:{x:o.complexTensorInfos.real},backend:e})}var nz={kernelName:Rp,backendName:"webgl",kernelFunc:wl};var ert="return float(int(x));";function oz(r,t){let e=new tn(r.shape,ert),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function xk(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return tr({inputs:{x:o},backend:e});let i=Ne(o.shape),a=xk({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=En({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=wl({inputs:{input:o},backend:e}),a=xk({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=tr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=mL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return oz(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=gk({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var sz={kernelName:lo,backendName:"webgl",kernelFunc:xk};var iz="return ceil(x);",rrt=Ct({opSnippet:iz,packedOpSnippet:iz,cpuKernelImpl:fL}),az={kernelName:qo,backendName:"webgl",kernelFunc:rrt};var sC=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` void main() { float value = getAAtOutCoords(); if (isnan(value)) { setOutput(value); return; } setOutput(clamp(value, minVal, maxVal)); } `}};var iC=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` void main() { vec4 value = getAAtOutCoords(); if (any(isnan(value))) { setOutput(value); return; } setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } `}};function nrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;z().getBool("WEBGL_PACK_CLIP")?a=new iC(o.shape):a=new sC(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var lz={kernelName:uo,backendName:"webgl",kernelFunc:nrt};var aC=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); float mx = max(re, im); // sadly the length function in glsl is not underflow-safe // (at least not on Intel GPUs). 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${n} }`:s?C=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:C=` float activation(float x) { ${n} } `,N="result = activation(result);");let _=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${C} const ivec2 strides = ivec2(${u}, ${l}); const ivec2 pads = ivec2(${i}, ${a}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${w}]; ivec2 xRCCorner = ivec2(coords[${x}], coords[${b}]) * 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 * ${c}; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${f}; wC++) { int xC = xCCorner + wC * ${p}; if (xC < 0 || xC >= ${t.inWidth}) { continue; } for (int d1 = 0; d1 < ${d}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${g}) { 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 (${h===1}) { if (${g}) { dotProd += getX(batch, xR, xC, ${d}) * getW(wR, wC, ${d}, d2); } else { dotProd += getX(batch, ${d}, xR, xC) * getW(wR, wC, ${d}, d2); } } else if (${h===2}) { vec2 wValues = vec2( getW(wR, wC, ${d}, d2), getW(wR, wC, ${d} + 1, d2) ); if (${g}) { vec2 xValues = vec2( getX(batch, xR, xC, ${d}), getX(batch, xR, xC, ${d} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${d}, xR, xC), getX(batch, ${d} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${h===3}) { vec3 wValues = vec3( getW(wR, wC, ${d}, d2), getW(wR, wC, ${d} + 1, d2), getW(wR, wC, ${d} + 2, d2) ); if (${g}) { vec3 xValues = vec3( getX(batch, xR, xC, ${d}), getX(batch, xR, xC, ${d} + 1), getX(batch, xR, xC, ${d} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( getX(batch, ${d}, xR, xC), getX(batch, ${d} + 1, xR, xC), getX(batch, ${d} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${_} ${N} setOutput(result); } `}},pC=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${s}, ${i}, ${a}); const ivec3 pads = ivec3(${e}, ${n}, ${o}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d2 = coords.u; ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xFCorner = xFRCCorner.x; int xRCorner = xFRCCorner.y; int xCCorner = xFRCCorner.z; // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; for (int wF = 0; wF < ${p}; wF++) { int xF = xFCorner + wF * ${u}; if (xF < 0 || xF >= ${t.inDepth}) { continue; } for (int wR = 0; wR < ${m}; wR++) { int xR = xRCorner + wR * ${l}; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${f}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${t.inWidth}) { continue; } for (int d1 = 0; d1 < ${d}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${h===1}) { dotProd += getX(batch, xF, xR, xC, ${d}) * getW(wF, wR, wC, ${d}, d2); } else if (${h===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${d}), getX(batch, xF, xR, xC, ${d} + 1) ); vec2 wValues = vec2( getW(wF, wR, wC, ${d}, d2), getW(wF, wR, wC, ${d} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${h===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${d}), getX(batch, xF, xR, xC, ${d} + 1), getX(batch, xF, xR, xC, ${d} + 2) ); vec3 wValues = vec3( getW(wF, wR, wC, ${d}, d2), getW(wF, wR, wC, ${d} + 1, d2), getW(wF, wR, wC, ${d} + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutput(dotProd); } `}};var kd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=we(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=` xC = xCCorner + ${x*u}; `,a===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } `,u===1&&x>0?m+=` xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy); `:m+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { previous.zw = vec2(0.0); } xC${x} = vec4(previous.zw, xTexelC${x}.xy); } else { xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy); } `):m+=` if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } xC${x} = xTexelC${x}; `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.0); } xTexelC${x+1}Ready = 1; } `,u>1?m+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy); } else { xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy); } `:m+=` xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy); `):b===1?m+=` xC${x+1} = xTexelC${x}; `:m+=` xCOffset = xC + ${b}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.0); } xTexelC${x+1}Ready = 1; } xC${x+1} = xTexelC${x+1}; `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.0); } xTexelC${x+1}Ready = 1; } xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw); `,x+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy); `)):(m+=` if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.); } xTexelC${x+1}Ready = 1; } xC${x} = vec4( xTexelC${x}.xy, xTexelC${x+1}.xy); `,x+1= 0) { // Use custom imod instead mod. On Intel GPU, mod may generate // unexpected value. // https://github.com/tensorflow/tfjs/issues/5447 offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1]; d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / inChannels); if(d1 < inputShape[${a}] && d1 >= 0) { ch = imod(pos, inChannels); if (${s}) { innerDims = vec2(d1, ch); result[${c*2+p}] = getChannel( getA(rc.x, d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${c*2+p}] = getChannel( getA(rc.x, ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${l} ${o.output} = result; } `}};function fC(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function dC({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(s!=null){let C=fC(s.shape,f);C!=null&&(s=st({inputs:{x:s},backend:n,attrs:{shape:C}}),x.push(s))}if(o!=null){let C=fC(o.shape,f);C!=null&&(o=st({inputs:{x:o},backend:n,attrs:{shape:C}}),x.push(o))}if(!((p===1||m===1)&&c>dk)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let C=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,C,e.inChannels],dtype:r.dtype},_=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Eu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=st({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let $=Uc({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get($.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=_,F.shape=e.outShape,g=tr({inputs:{x:$},backend:n}),g.shape=e.outShape,x.push($)}else{let C=e.outHeight*e.outWidth,N=st({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,C,e.inChannels]:[e.batchSize,e.inChannels,C]}}),_=st({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=Uc({a:f?N:_,b:f?_:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=st({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(_),x.push(A)}for(let C of x)n.disposeIntermediateTensorInfo(C);return g}function hC({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,C=[];if(s!=null){let Z=fC(s.shape,d);Z!=null&&(s=st({inputs:{x:s},backend:n,attrs:{shape:Z}}),C.push(s))}if(o!=null){let Z=fC(o.shape,d);Z!=null&&(o=st({inputs:{x:o},backend:n,attrs:{shape:Z}}),C.push(o))}let N=st({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});C.push(N);let _=new mC(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],$=n.runWebGLProgram(_,[r],"float32",A),F=st({inputs:{x:$},backend:n,attrs:{shape:x}});C.push($),C.push(F);let P=o!=null,V=s!=null,G=a==="leakyrelu",W=a?bl(a,!0):null,q=new vd(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));H.push(Z),C.push(Z)}let j=n.runWebGLProgram(q,H,"float32"),Y=st({inputs:{x:j},backend:n,attrs:{shape:e.outShape}});C.push(j);for(let Z of C)n.disposeIntermediateTensorInfo(Z);return Y}function irt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=v.convertConv2DDataFormat(u),m=v.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=dC({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&z().getBool("WEBGL_EXP_CONV")){let h=new kd(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],"float32",g)}else if(z().getBool("WEBGL_CONV_IM2COL"))f=hC({x:o,filter:s,convInfo:m,backend:e});else{let h=new Td(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=st({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var fz={kernelName:Ko,backendName:"webgl",kernelFunc:irt};var gC=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.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 < ${t.batchSize}; b++) { for (int yR = 0; yR < ${t.outHeight}; yR++) { int xR = wR + yR * ${e} - ${o}; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int yC = 0; yC < ${t.outWidth}; yC++) { int xC = wC + yC * ${n} - ${s}; if (xC < 0 || xC >= ${t.inWidth}) { continue; } if (${i}) { 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); } `}},xC=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=` const ivec2 pads = ivec2(${a}, ${u}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${p}]; ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - 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 < ${e}; wR++) { float dyR = float(dyRCorner + wR) / ${o}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${e} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; for (int d2 = 0; d2 < ${t.outChannels}; d2++) { if (${i}) { float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } else { float xValue = getDy(batch, d2, idyR, idyC); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}},yC=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.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 < ${t.batchSize}; b++) { for (int yF = 0; yF < ${t.outDepth}; yF++) { int xF = wF + yF * ${e} - ${s}; if (xF < 0 || xF >= ${t.inDepth}) { continue; } for (int yR = 0; yR < ${t.outHeight}; yR++) { int xR = wR + yR * ${n} - ${i}; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int yC = 0; yC < ${t.outWidth}; yC++) { int xC = wC + yC * ${o} - ${a}; if (xC < 0 || xC >= ${t.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},bC=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${u}, ${l}, ${c}); 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 < ${e}; wF++) { float dyF = float(dyFCorner + wF) / ${s}.0; if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); int wFPerm = ${e} - 1 - wF; for (int wR = 0; wR < ${n}; wR++) { float dyR = float(dyRCorner + wR) / ${i}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${n} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${a}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${o} - 1 - wC; for (int d2 = 0; d2 < ${t.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function art(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=v.convertConv2DDataFormat(u),m=v.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new gC(m);return e.runWebGLProgram(f,[o,s],"float32")}var dz={kernelName:mp,backendName:"webgl",kernelFunc:art};function lrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=v.convertConv2DDataFormat(l),m=v.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p),f=new xC(m);return e.runWebGLProgram(f,[o,s],"float32")}var hz={kernelName:jo,backendName:"webgl",kernelFunc:lrt};function urt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=v.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new pC(l);return e.runWebGLProgram(c,[o,s],"float32")}var gz={kernelName:Al,backendName:"webgl",kernelFunc:urt};function crt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=v.computeConv3DInfo(o.shape,u,i,1,a),c=new yC(l);return e.runWebGLProgram(c,[o,s],"float32")}var xz={kernelName:fp,backendName:"webgl",kernelFunc:crt};function prt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=v.computeConv3DInfo(u,s.shape,a,1,i),c=new bC(l);return e.runWebGLProgram(c,[o,s],"float32")}var yz={kernelName:dp,backendName:"webgl",kernelFunc:prt};var mrt=Po+` return cos(x); `,frt=Ct({opSnippet:mrt}),bz={kernelName:Xo,backendName:"webgl",kernelFunc:frt};var drt=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,hrt=Ct({opSnippet:drt}),wz={kernelName:Yo,backendName:"webgl",kernelFunc:hrt};var wC=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,C,N]=m>1?[`${(u-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` const float height_ratio = float(${g}); const float width_ratio = float(${w}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int y = coords[1]; int x = coords[2]; int d = coords[3]; // get box vals float y1 = getBoxes(b,0); float x1 = getBoxes(b,1); float y2 = getBoxes(b,2); float x2 = getBoxes(b,3); // get image in batch index int bInd = round(getBoxInd(b)); if(bInd < 0 || bInd >= ${i}) { return; } float height_scale = ${x}; float width_scale = ${C}; float in_y = ${b}; if( in_y < 0.0 || in_y > ${d} ) { setOutput(float(${s})); return; } float in_x = ${N}; if( in_x < 0.0 || in_x > ${h} ) { setOutput(float(${s})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${f} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}};var grt=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new wC(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},Cz={kernelName:da,backendName:"webgl",kernelFunc:grt};var qc;(function(r){r.Prod="*",r.Sum="+"})(qc||(qc={}));var ng=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===qc.Prod?"1.0":"0.0",a=n?i:`getX(${Iz(s,"coords",this.op)})`,u=this.outputShape[this.outputShape.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=` void main() { ${zt(s)} coords = getOutputCoords(); int end = ${Sz(s,"coords",this.op)}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${c}; ${Sz(s,"coords",this.op)} = idx; val ${this.op}= getX(${Iz(s,"coords",this.op)}); } setOutput(val); } `}};function Iz(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function Sz(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function CC(r,t,e,n,o,s){let i=t.shape.length,a=v.getAxesPermutation([n],i),u=t;a!=null&&(u=Oe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=v.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=tr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new ng(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new ng(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=v.getUndoAxesPermutation(a),f=Oe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function xrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return CC(qc.Prod,o,e,s,i,a)}var vz={kernelName:fa,backendName:"webgl",kernelFunc:xrt};function yrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return CC(qc.Sum,o,e,s,i,a)}var Nz={kernelName:Zo,backendName:"webgl",kernelFunc:yrt};function brt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Lw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=pL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var Tz={kernelName:hp,backendName:"webgl",kernelFunc:brt};var IC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,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 / ${e}; int offset_h = imod(h, ${e}); int in_w = w / ${e}; int offset_w = imod(w, ${e}); int offset_d = (offset_h * ${e} + offset_w) * ${this.getOutputDepthSize()}; int in_d = d + offset_d; float result = ${this.getInputSamplingString()}; setOutput(result); } `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function wrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new IC(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var kz={kernelName:ha,backendName:"webgl",kernelFunc:wrt};var Ed=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=we(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:s?l=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:l=` float activation(float x) { ${n} } `,c="result = activation(result);");let p=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${l} void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${u}; int q = d2 - d1 * ${u}; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations. for (int wR = 0; wR < ${i}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } for (int wC = 0; wC < ${a}; wC++) { int xC = xCCorner + wC * dilations[1]; if (xC < 0 || xC >= inDims[1]) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${p} ${c} setOutput(result); } `}};var _d=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=we(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) { `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=` xC = xCCorner + ${b*l}; `,u===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } `,l===1&&b>0?f+=` xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy); `:f+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { previous.zw = vec2(0.0); } xC${b} = vec4(previous.zw, xTexelC${b}.xy); } else { xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy); } `):f+=` if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } xC${b} = xTexelC${b}; `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } `,l>1?f+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy); } else { xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy); } `:f+=` xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy); `):w===1?f+=` xC${b+1} = xTexelC${b}; `:f+=` xCOffset = xC + ${w}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } xC${b+1} = xTexelC${b+1}; `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw); `,b+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy); `)):(f+=` if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.); } xTexelC${b+1}Ready = 1; } xC${b} = vec4( xTexelC${b}.xy, xTexelC${b+1}.xy); `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=v.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;z().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new _d(p):m=new Ed(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var Ez={kernelName:Jo,backendName:"webgl",kernelFunc:Crt};var SC=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int dm = coords.w; int d2 = d1 * ${i} + dm; float dotProd = 0.0; // TO DO: Vec4 over the batch size for (int b = 0; b < ${t.batchSize}; b++) { for (int yR = 0; yR < ${t.outHeight}; yR++) { int xR = wR + yR * ${e} - ${o}; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int yC = 0; yC < ${t.outWidth}; yC++) { int xC = wC + yC * ${n} - ${s}; if (xC < 0 || xC >= ${t.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},vC=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=` const ivec2 pads = ivec2(${i}, ${a}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[3]; ivec2 dyCorner = coords.yz - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; float dotProd = 0.0; for (int wR = 0; wR < ${e}; wR++) { float dyR = float(dyRCorner + wR) / ${o}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${e} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${t.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 < ${u}; dm++) { int d2 = d1 * ${u} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function Irt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=v.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new SC(p);return e.runWebGLProgram(m,[o,s],"float32")}var _z={kernelName:gp,backendName:"webgl",kernelFunc:Irt};function Srt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=v.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new vC(p);return e.runWebGLProgram(m,[o,s],"float32")}var Az={kernelName:xp,backendName:"webgl",kernelFunc:Srt};var NC=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function vrt(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=st({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new NC(s),u=e.runWebGLProgram(a,[i],i.dtype),l=st({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var $z={kernelName:yp,backendName:"webgl",kernelFunc:vrt};var TC=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=` const ivec2 strides = ivec2(${s}, ${i}); const ivec2 pads = ivec2(${p}, ${m}); const float neg_infinity = -3.4e38; void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.w; ivec2 outTopLeftCorner = coords.yz * strides - pads; int hBeg = outTopLeftCorner.x; int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; for (int h = 0; h < ${a}; h++) { int hIn = hBeg + h * ${l}; if (hIn >= 0 && hIn < ${e}) { for (int w = 0; w < ${u}; w++) { int wIn = wBeg + w * ${c}; if (wIn >= 0 && wIn < ${n}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); float val = xVal + wVal; if (val > curVal) { curVal = val; } } } } } float result = curVal; setOutput(result); } `}};function Nrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=v.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new TC(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=st({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var Dz={kernelName:$l,backendName:"webgl",kernelFunc:Nrt};function Trt(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=v.decodeEinsumEquation(o,s.length);v.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=v.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=Wc({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var Rz={kernelName:bp,backendName:"webgl",kernelFunc:Trt};var krt="return (x >= 0.0) ? x : (exp(x) - 1.0);",Ert=` 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; `,_rt=Ct({opSnippet:krt,packedOpSnippet:Ert}),Fz={kernelName:ts,backendName:"webgl",kernelFunc:_rt};var Art="return (b >= 1.0) ? a : a * (b + 1.0);",$rt=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,Drt=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Oo($rt,n.shape,o.shape):new io(Art,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},Oz={kernelName:wp,backendName:"webgl",kernelFunc:Drt};var Rrt=` return vec4(equal(a, b)); `,Frt="return float(a == b);",Ort=le({opSnippet:Frt,packedOpSnippet:Rrt,dtype:"bool",cpuKernelImpl:hL}),Pz={kernelName:xa,backendName:"webgl",kernelFunc:Ort};var Prt=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${v.ERF_P}; float a1 = ${v.ERF_A1}; float a2 = ${v.ERF_A2}; float a3 = ${v.ERF_A3}; float a4 = ${v.ERF_A4}; float a5 = ${v.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,Lrt=Ct({opSnippet:Prt}),Lz={kernelName:ga,backendName:"webgl",kernelFunc:Lrt};var Mrt=Po+` return exp(x); `,zrt=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,bk=Ct({opSnippet:Mrt,packedOpSnippet:zrt,cpuKernelImpl:gL,dtype:"float32"}),Mz={kernelName:es,backendName:"webgl",kernelFunc:bk};function kC(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),st({inputs:{x:s},backend:n,attrs:{shape:a}})}var zz={kernelName:ui,backendName:"webgl",kernelFunc:kC};var Bz="return exp(x) - 1.0;",Brt=Ct({opSnippet:Bz,packedOpSnippet:Bz,cpuKernelImpl:xL}),Vz={kernelName:ya,backendName:"webgl",kernelFunc:Brt};var og=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=` const float exponentMultiplier = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { ${a} } float mulMatDFT(int batch, int index) { float indexRatio = float(index) / float(${o}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; for (int i = 0; i < ${o}; i++) { // x = (-2|2 * PI / N) * index * i; float x = exponentMultiplierTimesIndexRatio * float(i); float expR = cos(x); float expI = sin(x); float real = getReal(batch, i); float imag = getImag(batch, i); result += unaryOpComplex(real, expR, imag, expI) / ${i}; } return result; } void main() { ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } `}};function EC(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=st({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new og("real",u,t),c=new og("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=En({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=st({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Vrt(r){let{inputs:t,backend:e}=r,{input:n}=t;return EC(n,!1,e)}var Gz={kernelName:Cp,backendName:"webgl",kernelFunc:Vrt};var _C=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } `}};function Cl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s==="string"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new _C(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var Wz={kernelName:Dl,backendName:"webgl",kernelFunc:Cl};var AC=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int coordX = ${e} - x - 1; float outputValue; if(coordX >= 0 && coordX < ${e}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}};var Uz={kernelName:ba,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new AC(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var Hz="return floor(x);",Grt=Ct({opSnippet:Hz,packedOpSnippet:Hz,cpuKernelImpl:yL}),qz={kernelName:rs,backendName:"webgl",kernelFunc:Grt};var Wrt=` 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; } `,Urt=` 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); `,Hrt=le({opSnippet:Wrt,packedOpSnippet:Urt,dtype:"int32"}),Kz={kernelName:ns,backendName:"webgl",kernelFunc:Hrt};var $C=class{constructor(t){this.variableNames=["A"];let e=Ge(),[n,o]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0); vec4 values = ${e.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}};var DC=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=Ge(),[n,o]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0); vec4 values = ${e.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); } } ${e.output} = result; } `}};var jz={kernelName:Yd,backendName:"webgl",kernelFunc:qrt},Ad,wk=z().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function qrt(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=z().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ad==null||h!==wk)&&(wk=h,Ad=document.createElement("canvas").getContext("2d",{willReadFrequently:wk})),Ad.canvas.width=u,Ad.canvas.height=l,Ad.drawImage(o,0,0,u,l),o=Ad.canvas}let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=jr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=z().getBool("WEBGL_PACK")?new DC(p):new $C(p),d=e.runWebGLProgram(f,[m],"int32");return e.disposeData(m.dataId),d}function Krt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=v.convertConv2DDataFormat(c),g=v.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,C=a!=null,N=f==="leakyrelu",_=()=>{let $=[o,s],F=(P,V)=>{if(V==="NCHW"&&P.shape.length===1&&P.shape[0]!==1){let G=st({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(G),G}return P};if(w&&$.push(F(i,c)),C&&$.push(F(a,c)),N){let P=e.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));$.push(P),b.push(P)}return $};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=dC({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h==="channelsLast"&&z().getBool("WEBGL_EXP_CONV")){let $=f?bl(f,!0):null,F=new kd(g,w,$,C,N),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=_();x=e.runWebGLProgram(F,V,"float32",P)}else if(z().getBool("WEBGL_CONV_IM2COL"))x=hC({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let $=f?bl(f,!1):null,F=new Td(g,w,$,C,N),P=_();x=e.runWebGLProgram(F,P,"float32")}let A=st({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach($=>e.disposeIntermediateTensorInfo($)),A}var Xz={kernelName:Ii,backendName:"webgl",kernelFunc:Krt};function jrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(v.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=v.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=z().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?bl(m,x):null,w=[o,s],C=i!=null,N=a!=null,_=m==="leakyrelu";if(C&&w.push(i),N&&w.push(a),_){let P=e.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));w.push(P),d.push(P)}let A;x?A=new _d(g,C,b,N,_):A=new Ed(g,C,b,N,_);let $=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,"float32",$);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),F}var Yz={kernelName:Si,backendName:"webgl",kernelFunc:jrt};var RC=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=["x","indices"],this.outputShape=n;let s=zt(n.length),i=` int index;`;for(let a=0;a= ${this.paramsShape[a]}; flattenIndex += index * ${this.strides[a]};`;this.userCode=` void main() { ${s} coords = getOutputCoords(); int flattenIndex = 0; bool out_of_bounds = false; ${i} setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1])); } `}};function Xrt(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=v.prepareAndValidate(n,o),m=st({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=st({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype==="string"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=bL(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new RC(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=st({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var Zz={kernelName:wa,backendName:"webgl",kernelFunc:Xrt};var FC=class{constructor(t,e){this.variableNames=["A","indices"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=Yrt(t,2);this.userCode=` void main() { ${n} resRC = getOutputCoords(); int index = int(getIndices(resRC.x, resRC.z)); float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0; setOutput(inBounds * getA(${o})); } `}};function Yrt(r,t){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=v.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=st({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=st({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=e.bufferSync(f),w=e.bufferSync(m),C=wL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,C.dtype,C.values)}let h=new FC(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=st({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var Jz={kernelName:ci,backendName:"webgl",kernelFunc:Ck};var Zrt="return float(a > b);",Jrt=` return vec4(greaterThan(a, b)); `,Qrt=le({opSnippet:Zrt,packedOpSnippet:Jrt,cpuKernelImpl:CL,dtype:"bool"}),Qz={kernelName:Ca,backendName:"webgl",kernelFunc:Qrt};var tnt="return float(a >= b);",ent=` return vec4(greaterThanEqual(a, b)); `,rnt=le({opSnippet:tnt,packedOpSnippet:ent,dtype:"bool",cpuKernelImpl:IL}),t3={kernelName:ss,backendName:"webgl",kernelFunc:rnt};function nnt(r){let{inputs:t,backend:e}=r,{input:n}=t;return EC(n,!0,e)}var e3={kernelName:Ip,backendName:"webgl",kernelFunc:nnt};var ont="return float(!isnan(x) && !isinf(x));",snt=Ct({opSnippet:ont,dtype:"bool"}),r3={kernelName:Ia,backendName:"webgl",kernelFunc:snt};var int="return float(isinf(x));",ant=Ct({opSnippet:int,dtype:"bool"}),n3={kernelName:Sa,backendName:"webgl",kernelFunc:ant};var lnt="return float(isnan(x));",unt=Ct({opSnippet:lnt,dtype:"bool"}),o3={kernelName:va,backendName:"webgl",kernelFunc:unt};var cnt="return float(a < b);",pnt=` return vec4(lessThan(a, b)); `,mnt=le({opSnippet:cnt,packedOpSnippet:pnt,cpuKernelImpl:SL,dtype:"bool"}),s3={kernelName:Na,backendName:"webgl",kernelFunc:mnt};var fnt="return float(a <= b);",dnt=` return vec4(lessThanEqual(a, b)); `,hnt=le({opSnippet:fnt,packedOpSnippet:dnt,cpuKernelImpl:vL,dtype:"bool"}),i3={kernelName:Ta,backendName:"webgl",kernelFunc:hnt};function gnt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=NL(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var a3={kernelName:vp,backendName:"webgl",kernelFunc:gnt};var xnt=Po+` return x < 0.0 ? 0./0. : log(x); `,ynt=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g); result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; `,bnt=Ct({opSnippet:xnt,packedOpSnippet:ynt,cpuKernelImpl:TL}),l3={kernelName:as,backendName:"webgl",kernelFunc:bnt};var wnt=Po+` return log(1.0 + x); `,Cnt=Ct({opSnippet:wnt}),u3={kernelName:ka,backendName:"webgl",kernelFunc:Cnt};var Int="return float(a >= 1.0 && b >= 1.0);",Snt=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,vnt=le({opSnippet:Int,packedOpSnippet:Snt,dtype:"bool"}),c3={kernelName:Ea,backendName:"webgl",kernelFunc:vnt};var Nnt="return float(!(x >= 1.0));",Tnt=Ct({opSnippet:Nnt}),p3={kernelName:_a,backendName:"webgl",kernelFunc:Tnt};var knt="return float(a >= 1.0 || b >= 1.0);",Ent=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,_nt=le({opSnippet:knt,packedOpSnippet:Ent,dtype:"bool"}),m3={kernelName:Aa,backendName:"webgl",kernelFunc:_nt};var OC=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; for (int j = -${i}; j <= ${i}; j++) { int idx = d + j; if (idx >= 0 && idx <= ${a}) { float z = getX(b, r, c, idx); sum += z * z; } } float val = x * ${u}; setOutput(val); } `}};var PC=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; int r = coords.y; int c = coords.z; int d = coords.w; bool hasNextCol = d < ${this.outputShape[3]}; bool hasNextRow = c < ${this.outputShape[2]}; vec4 sum = vec4(0.); vec4 xFragAtOutputCoords = getX(b, r, c, d); vec4 xAtOutputCoords = vec4( getChannel(xFragAtOutputCoords, vec2(c, d)), hasNextCol ? getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0, hasNextRow ? getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0, (hasNextRow && hasNextCol) ? getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); int firstChannel = d - ${i}; vec2 cache = vec2(0.); if(firstChannel >= 0){ vec4 firstChannelFrag = getX(b, r, c, firstChannel); cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel)); if(hasNextRow){ cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel)); } } ivec2 depth = ivec2(d, d + 1); for (int j = - ${i}; j <= ${i}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a})); bool depthInRange = aboveLowerBound.x && belowUpperBound.x; bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; if(depthInRange || depthPlusOneInRange){ vec4 z = vec4(0.); vec4 xFragAtCurrentDepth; z.xz = cache.xy; if(depthPlusOneInRange && hasNextCol){ xFragAtCurrentDepth = idx.y != d ? getX(b, r, c, idx.y) : xFragAtOutputCoords; z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y)); if(hasNextRow){ z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y)); } } cache.xy = z.yw; sum += z * z; } } vec4 result = xAtOutputCoords * ${u}; setOutput(result); } `}};var Ant=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=z().getBool("WEBGL_PACK_NORMALIZATION")?new PC(o.shape,s,i,a,u):new OC(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},f3={kernelName:Rl,backendName:"webgl",kernelFunc:Ant};var LC=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,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 - ${e}))); int depthEnd = int(min(float(${this.depth}), float(d + ${e} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${o}) * norm + float(${n}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${o}) * float(${s}) * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${s}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}};var $nt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new LC(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},d3={kernelName:Np,backendName:"webgl",kernelFunc:$nt};function h3(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=st({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Un(a,r.dtype,"max",n),l=st({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function Ik(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=v.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,C=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=v.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return tr({inputs:{x:o},backend:e});let p=new ei(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var y3={kernelName:cs,backendName:"webgl",kernelFunc:Ont};function Pnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=v.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new $u(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var b3={kernelName:Fl,backendName:"webgl",kernelFunc:Pnt};var MC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=` const ivec2 pads = ivec2(${a}, ${u}); 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 < ${s}; wR += ${o}) { float dyR = float(dyRCorner + wR) / ${e}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${i}; wC++) { float dyC = float(dyCCorner + wC) / ${n}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wR * ${i} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},zC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${m}, ${f}); 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 < ${u}; wD += ${s}) { float dyD = float(dyDCorner + wD) / ${e}.0; if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${l}; wR += ${i}) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${c}; wC += ${a}) { float dyC = float(dyCCorner + wC) / ${o}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); int maxPosValue = ${d} - int(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wD * ${l} * ${c} + wR * ${c} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function Lnt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=v.computePool3DInfo(i.shape,a,u,p,l,c),f=new $u(m,"max",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new zC(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var w3={kernelName:kp,backendName:"webgl",kernelFunc:Lnt};function Mnt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;Qs([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=v.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new ei(m,"max",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new MC(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var C3={kernelName:Tp,backendName:"webgl",kernelFunc:Mnt};function I3(r,t,e,n){let o=new ei(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new ei(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var S3={kernelName:Ep,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(v.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=v.computePool2DInfo(n.shape,o,s,l,i),[p,m]=I3(n,a,c,u);return[p,m]}};function v3(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=st({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Un(a,"float32","mean",n),l=st({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var N3={kernelName:ps,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=y.parseAxisParam(s,n.shape),l=u,c=v.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let C=i.texData.get(d.dataId).values,N=new Array(a);for(let $=0;$c[0]+t[p]+c[1]);let o=t.length,s=zt(o),i=e.map(c=>c[0]).join(","),a=e.map((c,p)=>c[0]+t[p]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),l=n==="reflect"?0:1;if(o===1){this.userCode=` int start = ${i}; int end = ${a}; void main() { int outC = getOutputCoords(); if (outC < start) { outC = start * 2 - outC - ${l}; } else if(outC >= end) { outC = (end - 1) * 2 - outC + ${l}; } setOutput(getX(outC - start)); } `;return}this.userCode=` ${s} start = ${s}(${i}); ${s} end = ${s}(${a}); void main() { ${s} outC = getOutputCoords(); for (int i = 0; i < ${o}; i++) { if (outC[i] < start[i]) { outC[i] = start[i] * 2 - outC[i] - ${l}; } else if(outC[i] >= end[i]) { outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; } } ${s} coords = outC - start; setOutput(getX(${u})); } `}};var VC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=zt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=Qe("rc",o),l=Qe("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; } else if (source >= end) { source = (end - 1) * 2 - source + ${m}; } source -= start; `;f=` ${s} rc = outputLoc; ${d} result[0] = getChannel(getX(${l.join()}), ${p}); ${u[o-1]} += 1; if(${c}) { ${d} result[1] = getChannel(getX(${l.join()}), ${p}); } `}else{let d=` ${s} source = rc; ${s} lt = ${s}(lessThan(source, start)); ${s} gte = ${s}(greaterThanEqual(source, end)); ${s} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${m}) + gte * ((end - 1) * 2 - source + ${m}); source -= start; `;f=` ${s} rc = outputLoc; ${d} result[0] = getChannel(getX(${l.join()}), ${p}); ${u[o-1]} += 1; if(${c}) { ${d} result[1] = getChannel(getX(${l.join()}), ${p}); } rc = outputLoc; ${u[o-2]} += 1; if(${u[o-2]} < ${this.outputShape[o-2]}) { ${d} result[2] = getChannel(getX(${l.join()}), ${p}); ${u[o-1]} += 1; if(${c}) { ${d} result[3] = getChannel(getX(${l.join()}), ${p}); } } `}this.userCode=` const ${s} start = ${s}(${i}); const ${s} end = ${s}(${a}); void main() { ${s} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${f} setOutput(result); } `}};var Wnt=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VC(n.shape,o,s):new BC(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},E3={kernelName:ds,backendName:"webgl",kernelFunc:Wnt};var Unt=`if (b == 0.0) return NAN; return mod(a, b);`,Hnt=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); `+Yi+` return result; `,qnt=le({opSnippet:Unt,packedOpSnippet:Hnt}),_3={kernelName:$a,backendName:"webgl",kernelFunc:qnt};var GC=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; float r = random(seed); float cdf = 0.0; for (int i = 0; i < ${e-1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { setOutput(float(i)); return; } } // If no other event happened, last event happened. setOutput(float(${e-1})); } `}};var Knt=` if (a == b) { return 1.0; }; return a / b;`,jnt=` // 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; `,Sk=le({opSnippet:Knt,packedOpSnippet:jnt,checkOutOfBounds:!0}),A3={kernelName:Qo,backendName:"webgl",kernelFunc:Sk};var $3="return a - b;",vk=le({opSnippet:$3,packedOpSnippet:$3,supportsComplex:!0,cpuKernelImpl:XL}),D3={kernelName:Fs,backendName:"webgl",kernelFunc:vk};function Nk(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=Ik({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=v.expandShapeToKeepDim(a.shape,i),l=st({inputs:{x:a},backend:e,attrs:{shape:u}}),c=vk({inputs:{a:o,b:l},backend:e}),p=bk({inputs:{x:c},backend:e}),m=Wc({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=st({inputs:{x:m},backend:e,attrs:{shape:u}}),d=Sk({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var R3={kernelName:Ds,backendName:"webgl",kernelFunc:Nk};function Xnt(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:Nk({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new GC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var F3={kernelName:_p,backendName:"webgl",kernelFunc:Xnt};var Ynt=fr+` return -x; `,Znt=` vec4 result = -x; bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `;function Jnt(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=$L(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new so(n.shape,Znt):o=new tn(n.shape,Ynt),e.runWebGLProgram(o,[n],n.dtype)}var O3={kernelName:pi,backendName:"webgl",kernelFunc:Jnt};var Qnt=Ur.nonMaxSuppressionV3Impl;function tot(r){v.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=Qnt(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var P3={kernelName:Ra,backendName:"webgl",kernelFunc:tot};var eot=Ur.nonMaxSuppressionV4Impl;function rot(r){v.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=eot(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var L3={kernelName:Fa,backendName:"webgl",kernelFunc:rot};var not=Ur.nonMaxSuppressionV5Impl;function oot(r){v.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=not(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var M3={kernelName:Oa,backendName:"webgl",kernelFunc:oot};var WC=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${o}), float(${n}), float(index == coords.y))); } `}};var sot=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new WC(l,i,a,u),p=st({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=st({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},z3={kernelName:gs,backendName:"webgl",kernelFunc:sot};function sg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=wl({inputs:{input:n},backend:e}),s=sg({inputs:{x:o},backend:e}),i=Hc({inputs:{input:n},backend:e}),a=sg({inputs:{x:i},backend:e}),u=En({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Cl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var B3={kernelName:wi,backendName:"webgl",kernelFunc:sg};function V3(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=wl({inputs:{input:n},backend:e}),s=V3({inputs:{x:o},backend:e}),i=Hc({inputs:{input:n},backend:e}),a=sg({inputs:{x:i},backend:e}),u=En({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Cl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var G3={kernelName:mi,backendName:"webgl",kernelFunc:V3};function iot(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return kC({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=kC({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=yk({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var W3={kernelName:fi,backendName:"webgl",kernelFunc:iot};var UC=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=zt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=` int start = ${i}; int end = ${a}; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(value); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${s} start = ${s}(${i}); ${s} end = ${s}(${a}); void main() { ${s} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { ${s} coords = outC - start; setOutput(getX(${u})); } } `}};var HC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=Qe("rc",o),l=Qe("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1; if(${c}) { `,o===1?"":`} rc = outputLoc; ${u[o-2]} += 1; if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${u[o-1]} += 1; if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===1?2:4;h{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Cl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new HC(o.shape,s,i):new UC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},U3={kernelName:xs,backendName:"webgl",kernelFunc:Tk};var aot=` 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); `,lot=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); vec4 result = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS bvec4 isExpZero = equal(b, vec4(0.0)); result.r = isExpZero.r ? 1.0 : result.r; result.g = isExpZero.g ? 1.0 : result.g; result.b = isExpZero.b ? 1.0 : result.b; result.a = isExpZero.a ? 1.0 : result.a; bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); `+Yi+` return result; `,uot=le({opSnippet:aot,packedOpSnippet:lot}),H3={kernelName:ys,backendName:"webgl",kernelFunc:uot};function cot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=v.getAxesPermutation(c,a),m=o;p!=null&&(m=Oe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=v.getInnerMostAxes(c.length,a),u.push(m)),v.assertAxesAreInnerMostDims("prod",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=RL(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=v.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=st({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=Wu(o.dtype),w=Un(x,b,"prod",e);f=st({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=v.expandShapeToKeepDim(f.shape,l);f=st({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var q3={kernelName:ws,backendName:"webgl",kernelFunc:cot};function pot(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=FL(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var K3={kernelName:Ap,backendName:"webgl",kernelFunc:pot};function mot(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=OL(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var j3={kernelName:$p,backendName:"webgl",kernelFunc:mot};function fot(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=PL(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var X3={kernelName:Dp,backendName:"webgl",kernelFunc:fot};var kk=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=LL(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},Y3={kernelName:Ol,backendName:"webgl",kernelFunc:kk};var dot="return 1.0 / x;",hot=Ct({opSnippet:dot}),Z3={kernelName:Pa,backendName:"webgl",kernelFunc:hot};var got=fr+` return (x < 0.0) ? 0.0 : x; `,xot=` 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; `,yot=Ct({opSnippet:got,packedOpSnippet:xot}),J3={kernelName:Cs,backendName:"webgl",kernelFunc:yot};var bot=fr+` return (x < 0.0) ? 0.0 : min(6.0, x); `,wot=` 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; `,Cot=Ct({opSnippet:bot,packedOpSnippet:wot}),Q3={kernelName:vs,backendName:"webgl",kernelFunc:Cot};var qC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/p[0]}, ${c[1]/p[1]}); const vec2 inputShapeRC = vec2(${a}.0, ${u}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${m}; // Compute the four integer indices. ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0))); ivec2 sourceCeilRC = ivec2( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d); float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d); float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d); float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d); vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC); float top = topLeft + (topRight - topLeft) * fracRC.y; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; float newValue = top + (bottom - top) * fracRC.x; setOutput(newValue); } `}};var KC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/p[0]}, ${c[1]/p[1]}, ${c[1]/p[1]}); const vec3 inputShapeRC = vec3(${a}.0, ${u}.0, ${u}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${m}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); ivec3 sourceCeilRC = ivec3( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-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); } `}};function Iot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new KC(o.shape,u,l,s,i):new qC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var tB={kernelName:Ss,backendName:"webgl",kernelFunc:Iot};var jC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*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(${c}); const float widthScale = float(${p}); const float invHeightScale = float(${m}); const float invWidthScale = float(${f}); const int winHeight = int(${d}); const int winWidth = int(${h}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(startRLerp - float(winHeight / 2)); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(startCLerp - float(winWidth / 2)); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${i}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${a}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); int bottomDxRIndex = int(min(ceil(dxR), ${o-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), ${s-1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; if (r == topDxRIndex && c == leftDxCIndex) { // topLeft accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; } if (r == topDxRIndex && c == rightDxCIndex) { // topRight accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; } if (r == bottomDxRIndex && c == leftDxCIndex) { // bottomLeft accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; } if (r == bottomDxRIndex && c == rightDxCIndex) { // bottomRight accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; } } } // End loop over dy setOutput(accumulator); } `}};function Sot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new jC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var eB={kernelName:Op,backendName:"webgl",kernelFunc:Sot};var XC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/p[0]}, ${c[1]/p[1]}); const vec2 inputShapeRC = vec2(${a}.0, ${u}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${f}; // 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); } `}};var YC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/p[0]}, ${c[1]/p[1]}, ${c[1]/p[1]}); const vec3 inputShapeRC = vec3(${a}.0, ${u}.0, ${u}.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 = ${f}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m}))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${n-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function vot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new YC(o.shape,u,l,s,i):new XC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var rB={kernelName:Is,backendName:"webgl",kernelFunc:vot};var ZC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${p}); const float invHeightScale = float(${m}); const float invWidthScale = float(${f}); const int winHeight = int(${d}); const int winWidth = int(${h}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(floor(startRLerp - float(winHeight / 2))); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(floor(startCLerp - float(winWidth / 2))); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${i}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${a}) { continue; } float sourceFracRow = float(${u[0]}) * (float(dyR) / float(${l[0]})); float sourceFracCol = float(${u[1]}) * (float(dyC) / float(${l[1]})); int sourceNearestRow = int(min( float(int(${o}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${s}) - 1), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function Not(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new ZC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var nB={kernelName:Fp,backendName:"webgl",kernelFunc:Not};var JC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${t[0]} - coord - 1)); } `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(","),i=zt(n);this.userCode=` void main() { ${i} coords = getOutputCoords(); setOutput(getX(${s})); } `}};var QC=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=Qe("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${t[0]} - rc - 1), ${t[0]} - rc - 1); if(${s}){ result.g = getChannel(getX(${t[0]} - (rc + 1) - 1), ${t[0]} - (rc + 1) - 1); } setOutput(result); } `:this.userCode=` void main() { ${a} rc = getOutputCoords(); vec4 result = vec4(0.); result.r = ${u(o.slice())}; if(${s}){ result.g = ${l(o.slice())}; } if(${i}) { result.b = ${c(o.slice())}; if(${s}) { result.a = ${p(o.slice())}; } } setOutput(result); } `;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function Tot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return tr({inputs:{x:o},backend:e});let u=z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new QC(o.shape,a):new JC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var oB={kernelName:Ns,backendName:"webgl",kernelFunc:Tot};var tI=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=t[1],o=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=` vec3 fill = vec3(${e.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int y = coords[1]; float coordXFloat = (float(x) - params[0]) * params[3] - (float(y) - params[1]) * params[2]; float coordYFloat = (float(x) - params[0]) * params[2] + (float(y) - params[1]) * params[3]; int coordX = int(round(coordXFloat + params[0])); int coordY = int(round(coordYFloat + params[1])); ${s} if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}};var sB={kernelName:qa,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new tI(n.shape,s),[l,c]=v.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var kot=` // 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; } } `,Eot=Ct({opSnippet:kot}),iB={kernelName:Ts,backendName:"webgl",kernelFunc:Eot};var _ot="return inversesqrt(x);",Aot=Ct({opSnippet:_ot,cpuKernelImpl:ML}),aB={kernelName:ks,backendName:"webgl",kernelFunc:Aot};var $d=class{constructor(t,e,n,o,s,i,a=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let u=zt(s.length),l=zt(i.length),c="";n===1?c="i":n===2&&(c="i, j");let p=`getIndices(${c})`,m="";o===1?m="i":o===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=e>1?"strides[j]":"strides";this.userCode=` ${u} strides = ${u}(${s}); void main() { ${l} coords = getOutputCoords(); float sum = 0.0; bool found = false; for (int i = 0; i < ${t}; i++) { int flattenedIndex = 0; for (int j = 0; j < ${e}; j++) { int index = round(${p}); flattenedIndex += index * ${d}; } if (flattenedIndex == coords[0]) { sum += ${f}; found = true; } } setOutput(mix(getDefaultValue(), sum, float(found))); } `}};function $ot(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=v.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=st({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=st({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g=new $d(u,a,f.shape.length,d.shape.length,c,m),x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=st({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var lB={kernelName:La,backendName:"webgl",kernelFunc:$ot};var eI=class{constructor(t,e,n,o){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,n];let s="while (left < right) {",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=z().getNumber("WEBGL_VERSION")===2?s:i,u=o==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; int mid; ${a} mid = (left + right) / 2; if (getSortedSequence(batch, mid) ${u} value) { left = mid + 1; } else { right = mid; } } return right; } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int valueIndex = coords[1]; float value = getValues(batch, valueIndex); setOutput(float(findBound(batch, value))); } `}};function Dot(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new eI(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],"int32",u)}var uB={kernelName:Pp,backendName:"webgl",kernelFunc:Dot};var rI=class{constructor(t,e,n){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c= 1.0) { setOutput(getA(${s})); } else { setOutput(getB(${s})); } } `}};function Rot(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new rI(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],sr(o.dtype,s.dtype))}var cB={kernelName:hi,backendName:"webgl",kernelFunc:Rot};var Fot=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${v.SELU_SCALEALPHA}; float scale = ${v.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,Oot=Ct({opSnippet:Fot}),pB={kernelName:Ma,backendName:"webgl",kernelFunc:Oot};var Pot=Po+` return 1.0 / (1.0 + exp(-1.0 * x)); `,Lot=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,Mot=Ct({opSnippet:Pot,packedOpSnippet:Lot,cpuKernelImpl:BL}),mB={kernelName:_s,backendName:"webgl",kernelFunc:Mot};var zot=` if (isnan(x)) { return 0.0; } return sign(x); `,Bot=Ct({opSnippet:zot}),fB={kernelName:Ba,backendName:"webgl",kernelFunc:Bot};var Vot=Po+` return sin(x); `,Got=Ct({opSnippet:Vot}),dB={kernelName:Es,backendName:"webgl",kernelFunc:Got};var Wot=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,Uot=Ct({opSnippet:Wot}),hB={kernelName:za,backendName:"webgl",kernelFunc:Uot};var Hot=` 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; `,qot=Ct({opSnippet:Hot}),gB={kernelName:Va,backendName:"webgl",kernelFunc:qot};var Kot=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},xB={kernelName:xi,backendName:"webgl",kernelFunc:Kot};function jot(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw: ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw: ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=GL(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var yB={kernelName:Pl,backendName:"webgl",kernelFunc:jot};function Xot(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=WL(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var bB={kernelName:Ga,backendName:"webgl",kernelFunc:Xot};function Yot(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=zw(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var wB={kernelName:Ll,backendName:"webgl",kernelFunc:Yot};function Zot(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=zw(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var CB={kernelName:Ml,backendName:"webgl",kernelFunc:Zot};function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=v.calculateShapes(s,o,a),f=!1;if(s.dtype==="string"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),C=zL(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,C.dtype,C.values)}let d=new $d(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=st({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var IB={kernelName:Lp,backendName:"webgl",kernelFunc:Jot};function Qot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=v.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=ri({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var SB={kernelName:yi,backendName:"webgl",kernelFunc:Qot};var vB="return sqrt(x);",tst=Ct({opSnippet:vB,packedOpSnippet:vB,cpuKernelImpl:UL}),NB={kernelName:As,backendName:"webgl",kernelFunc:tst};var est="return x * x;",rst=Ct({opSnippet:est}),TB={kernelName:zl,backendName:"webgl",kernelFunc:rst};var kB="return (a - b) * (a - b);",nst=le({opSnippet:kB,packedOpSnippet:kB}),EB={kernelName:Rs,backendName:"webgl",kernelFunc:nst};function ost({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=fr+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new tn(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var _B={kernelName:po,backendName:"webgl",kernelFunc:ost};var nI=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` ${s} begin = ${s}(${t}); ${s} strides = ${s}(${e}); void main() { ${i} coords = getOutputCoords(); setOutput(getX(${a})); } `}};function sst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:C}=Le.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=st({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=Le.computeOutShape(b,w,C),$=ri({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=st({inputs:{x:$},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo($)}else if(e.shouldExecuteOnCPU([o])){let $=e.readSync(o.dataId),F=wt(o.shape,o.dtype,$),P=HL(f,F,C,b);N=e.makeTensorInfo(d,o.dtype,P.values)}else{let $=new nI(b,C,f);N=e.runWebGLProgram($,[o],o.dtype)}let _=st({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),_}var AB={kernelName:Wa,backendName:"webgl",kernelFunc:sst};function ist(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=qL(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var $B={kernelName:Bl,backendName:"webgl",kernelFunc:ist};function ast(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=KL(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],"int32",l),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(p))]}var DB={kernelName:Vl,backendName:"webgl",kernelFunc:ast};function lst(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let i=e.readSync(s.dataId),a=jL(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var RB={kernelName:Gl,backendName:"webgl",kernelFunc:lst};var ust="return tan(x);",cst=Ct({opSnippet:ust}),FB={kernelName:Os,backendName:"webgl",kernelFunc:cst};var pst=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,mst=Ct({opSnippet:pst}),OB={kernelName:Ps,backendName:"webgl",kernelFunc:mst};var oI=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o5){let u=e.readSync(o.dataId),l=o.dtype==="string"?u.map(m=>y.decodeString(m)):u,c=wt(o.shape,o.dtype,l),p=YL(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new oI(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var PB={kernelName:Jn,backendName:"webgl",kernelFunc:Ek};var sI=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=t,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // We compare elements pair-wise within a group of size 2 * inc. // The comparing rule for each group alternates between ascending // and descending. Within each group, we compare each pair at // positions i and i+inc. To decide whether an element at position i // is x0 or x1, we mod it by 2 * inc, if the result is smaller than // inc, it is in the first half of the group, we denote it as x0, // otherwise we denote it as x1. // For example, as shown in the Bitonic top K paper referenced above, // Figure5(a) shows that element[1] is in the // second half of the group when group size is 2, but it is in the // first half of the group when group size is 4. bool isFirstInPair = imod(elemIdx, 2 * inc) < inc; int i = isFirstInPair ? elemIdx : elemIdx - inc; int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc)); float x0 = i0 < n ? getX(batch, i0) : negativeInf; float x1 = i1 < n ? getX(batch, i1) : negativeInf; // Denotes which direction indices are in (ascending or descending). bool reverse = imod(elemIdx, 2 * dir) >= dir; bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction int iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutput(float(i0)); } else { setOutput(float(i1)); } } `}},iI=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=t,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // The output size is half of the previous size. // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4), // we only need to output the indices at positions |, the indices at // positions _ can be thrown away, see Figure5(b) After Phase 2 // (Merge phase) in the Bitonic Top K paper referenced above. // For example, the paper shows we only need to output the orange bars. // The output sequence should look like this | | | | | | | |. // Because the sequence is halved, to map the output index back // to the previous sequence to find the corresponding value, // we need to double the index. When we double the index, // we basically interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position // of each 2k positions by - elemIdx % k. E.g. for output at // index 4,5,6,7, we want to get the corresponding element at // original index 8,9,10,11, for output at index 8,9,10,11, // we want to get the corresponding element at original index // 16,17,18,19, so on and so forth. int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k)); int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k)); float x0 = getX(batch, i0); float x1 = i1 < n ? getX(batch, i1) : x0; setOutput(x0 >= x1 ? float(i0) : float(i1)); } `}};function Kc(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function LB(r){let t=1;for(;tu){let P=e.readSync(o.dataId),[V,G]=ZL(P,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,"int32",[])];if(c===1)return[o,Cl({attrs:{shape:l,dtype:"int32",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=st({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&Kc(e,f);let x=LB(s),b=LB(c),w=null,C=()=>w===null?[g,g]:[g,w],N=(P,V,G)=>{let W=C(),q=new sI(G),j=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[P],[V]],Y=w;w=e.runWebGLProgram(q,W,"int32",j),Kc(e,Y)};for(let P=1;P=1;G/=2)N(V,G,[h,b])}for(let P=b;P>x;P/=2){let V=C(),G=new iI([h,P/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,"int32",q),Kc(e,H);let j=x/2,Y=j*2;for(let Z=j;Z>=1;Z/=2)N(Y,Z,w.shape)}let _=w;w=ri({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),Kc(e,_);let A=Ck({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});Kc(e,g);let $=l.slice(0,-1);$.push(s),_=w,w=st({inputs:{x:w},attrs:{shape:$},backend:e}),Kc(e,_);let F=A;return A=st({inputs:{x:A},attrs:{shape:$},backend:e}),Kc(e,F),[A,w]}var MB={kernelName:Ua,backendName:"webgl",kernelFunc:dst};var aI=class{constructor(t,e,n,o,s,i){this.variableNames=["Image","Transforms"],this.outputShape=i;let a=n==="nearest"?1:2,u;switch(o){case"constant":u=1;break;case"reflect":u=2;break;case"wrap":u=3;break;case"nearest":u=4;break;default:u=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${u} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * float(int(float(-inCoord / sz2))) + inCoord; } inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0; } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; inCoord -= sz2 * float(int(float(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } else if (${u} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord += len * (float(int(float(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord -= len * float(int(float(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (${u} == 4) { return clamp(outCoord, 0.0, len - 1.0); } else { return outCoord; } } float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${s}); } return outputValue; } void main() { ivec4 coords = getOutputCoords(); float outputValue; int batch = coords[0]; int x = coords[2]; int y = coords[1]; int channel = coords[3]; float xf = float(x); float yf = float(y); float a1 = getTransforms(batch, 0); float a2 = getTransforms(batch, 1); float a3 = getTransforms(batch, 2); float b1 = getTransforms(batch, 3); float b2 = getTransforms(batch, 4); float b3 = getTransforms(batch, 5); float c1 = getTransforms(batch, 6); float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = float(${s}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${e})); float mapY = mapCoord(inY, float(${t})); if (${a} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { float yFloor = floor(mapY); float xFloor = floor(mapX); float yCeil = yFloor + 1.0; float xCeil = xFloor + 1.0; float valueYFloor = (xCeil - mapX) * readWithFillValue(batch, int(yFloor), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yFloor), int(xCeil), channel); float valueYCeil = (xCeil - mapX) * readWithFillValue(batch, int(yCeil), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yCeil), int(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutput(outputValue); } `}};function hst(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new aI(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],"float32")}var zB={kernelName:Ha,backendName:"webgl",kernelFunc:hst};function gst(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;Qs(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=JL(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],"int32",l)]}var BB={kernelName:Mp,backendName:"webgl",kernelFunc:gst};function xst(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),d}var VB={kernelName:bi,backendName:"webgl",kernelFunc:xst};var lI=class{constructor(t,e){this.variableNames=["x","segmentIds"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u="0.0",l="sumValue",c=Math.floor(n/4)*4,p=n%4,m=` sumValue += dot(values, segFilter); `,f="";s%n>0&&(f=` if (inIdx < 0 || inIdx >= ${s}) { return initializationValue; } `);let d="";s%n>0&&(d=` if (inIdx < 0 || inIdx >= ${s}) { return -1.0; } `),this.userCode=` const float initializationValue = ${u}; float getValue(int batch, int inIdx) { ${f} return getX(batch, inIdx); } float getSegmentIdAtIndex(int inIdx) { ${d} return getSegmentIds(inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = int(floor(float(outIdx) / float( ${i})) * float(${n})); int currentSeg = int(mod(float(outIdx), float(${i}))); float sumValue = 0.0; for (int i = 0; i < ${c}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 ); ${m} } int inIdx = inOffset + ${c}; if (${p===1}) { vec4 values = vec4( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); int inIdxSeg = int(getSegmentIdAtIndex(inIdx)); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, 0, 0, 0 ); ${m} } else if (${p===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, 0, 0 ); ${m} } else if (${p===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, 0 ); ${m} } setOutput(${l}); } `}};function yst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=v.getAxesPermutation([l],a),p=o;c!=null&&(p=Oe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=v.getInnerMostAxes(1,a)[0]);let m=v.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=st({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=Wu(o.dtype),g=(C,N,_,A,$)=>{let F=C.shape[0],P=C.shape[1],V=v.segment_util.segOpComputeOptimalWindowSize(P,$),G={windowSize:V,inSize:P,batchSize:F,numSegments:$},W=new lI(G,N),q=e.compileAndRun(W,[C,_],A);if(u.push(q),q.shape[1]===$)return q;let H=kk({backend:e,attrs:{start:0,stop:$,step:1,dtype:"float32"}}),j=Ek({inputs:{x:H},backend:e,attrs:{reps:[P/V]}});return u.push(H),u.push(j),g(q,N,j,A,$)},x=g(d,"unsortedSegmentSum",s,h,i),b=st({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let C=v.getUndoAxesPermutation(c);w=Oe({inputs:{x:w},backend:e,attrs:{perm:C}})}return u.forEach(C=>e.disposeIntermediateTensorInfo(C)),w}var GB={kernelName:Wl,backendName:"webgl",kernelFunc:yst};var bst=[kM,_M,AM,$M,RM,FM,OM,PM,zM,BM,VM,GM,WM,UM,HM,qM,KM,jM,XM,YM,ZM,QM,tz,ez,sz,az,lz,xM,cz,mz,fz,dz,hz,gz,xz,yz,bz,wz,Cz,vz,Nz,Tz,kz,Ez,_z,Az,$z,Dz,Rz,Fz,Oz,Pz,Lz,Mz,zz,Vz,Gz,Wz,Uz,qz,Kz,jz,Xz,Yz,Zz,Jz,Qz,t3,gM,e3,pz,r3,n3,o3,yM,s3,i3,a3,l3,u3,c3,p3,m3,f3,d3,g3,x3,y3,b3,w3,C3,S3,N3,T3,k3,E3,_3,F3,CM,O3,P3,L3,M3,rz,z3,G3,W3,U3,H3,bM,q3,K3,j3,X3,Y3,nz,A3,Z3,J3,Q3,SM,tB,eB,rB,nB,oB,sB,iB,aB,lB,uB,cB,pB,mB,fB,dB,hB,JM,R3,gB,xB,yB,bB,wB,CB,IB,SB,NB,TB,EB,_B,AB,$B,DB,RB,D3,NM,FB,OB,PB,MB,zB,TM,BB,VB,GB,B3];for(let r of bst)Lu(r);var qt;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(qt||(qt={}));var Du;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu",r[r.sigmoid=5]="sigmoid",r[r.elu=6]="elu"})(Du||(Du={}));var WB;function wst(r){WB=r.wasm.cwrap(Ci,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Cst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let $=e.dataIdMap.get(i.dataId);if($.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${$.shape.length}.`);d=$.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=Du[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Vr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),C=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(C.dataId).id,_=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return WB(m,_,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),C}var UB={kernelName:Ci,backendName:"wasm",setupFunc:wst,kernelFunc:Cst};function se(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,["number","number","number"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,qt[a.dtype],c),l}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:o}}var HB=se(ii);function ue(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=v.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return(()=>n(p,g,l.shape.length,m,x,c.shape.length,qt[l.dtype],b))(),h}return{kernelName:r,backendName:"wasm",setupFunc:o,kernelFunc:s}}var Ist=!0,qB=ue(Zn,Ist);var KB;function Sst(r){KB=r.wasm.cwrap(Go,null,["array","number","number","number"])}function vst(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return KB(s,o.length,qt[n.dtype],i),n}var jB={kernelName:Go,backendName:"wasm",setupFunc:Sst,kernelFunc:vst};function jc(r){let{inputs:{x:t},backend:e}=r;if(t.dtype==="string")return ur(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var XB={kernelName:co,backendName:"wasm",kernelFunc:jc};var YB;function Nst(r){YB=r.wasm.cwrap(Qn,null,["number","array","number","number","number","array","number"])}function ao(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=kst(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var ZB={kernelName:Qn,backendName:"wasm",kernelFunc:ao,setupFunc:Nst};function bn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=v.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. 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ni(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var pV={kernelName:lo,backendName:"wasm",kernelFunc:ni};var mV=se(qo);var fV;function Gst(r){fV=r.wasm.cwrap(uo,null,["number","number","number","number"])}function Wst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return fV(a,s,i,l),u}var dV={kernelName:uo,backendName:"wasm",setupFunc:Gst,kernelFunc:Wst};function _k(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);v.assertParamsConsistent(o,n);let s=v.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return jc({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype==="string"){let f=i.map(w=>{let 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g=m;if(l!==null){let x=v.getUndoAxesPermutation(l);g=ao({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var NV={kernelName:fa,backendName:"wasm",setupFunc:Yst,kernelFunc:Zst};var TV;function Jst(r){TV=r.wasm.cwrap(Zo,null,["number","number","number","number","number","number"])}function Qst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=v.getAxesPermutation([s],u),c=o;l!==null&&(c=ao({inputs:{x:o},attrs:{perm:l},backend:e}));let p=v.getInnerMostAxes(1,u)[0];v.assertAxesAreInnerMostDims("cumsum",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;TV(d,i?1:0,a?1:0,f,h,qt[o.dtype]);let g=m;if(l!==null){let x=v.getUndoAxesPermutation(l);g=ao({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var kV={kernelName:Zo,backendName:"wasm",setupFunc:Jst,kernelFunc:Qst};var EV;function tit(r){EV=r.wasm.cwrap(ha,null,["number","number","number","array","number","array","array","number","number"])}function eit(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,"float32"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),C=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return EV(x,s,i==="NHWC"?1:0,b,o.shape.length-1,w,C,d.length,N),h}var _V={kernelName:ha,backendName:"wasm",setupFunc:tit,kernelFunc:eit};var AV;function rit(r){AV=r.wasm.cwrap(Jo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nit(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=v.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,C=f.dilationHeight,N=f.dilationWidth,_=f.strideHeight,A=f.strideWidth,$=f.inChannels,F=f.outChannels,P=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. 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Bit(r){IG=r.wasm.cwrap(Ra,"number",["number","number","number","number","number"])}function Vit(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i}=n,{boxes:a,scores:u}=e,l=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=IG(l,c,s,o,i),{pSelectedIndices:m,selectedSize:f,pSelectedScores:d,pValidOutputs:h}=Dd(t,p);return t.wasm._free(d),t.wasm._free(h),t.makeOutput([f],"int32",m)}var SG={kernelName:Ra,backendName:"wasm",setupFunc:Bit,kernelFunc:Vit};var vG;function Git(r){vG=r.wasm.cwrap(Fa,"number",["number","number","number","number","number","bool"])}function Wit(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:a}=n,{boxes:u,scores:l}=e,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,m=vG(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Dd(t,m);t.wasm._free(h);let x=t.makeOutput([d],"int32",f),b=t.makeOutput([],"int32",g);return[x,b]}var NG={kernelName:Fa,backendName:"wasm",setupFunc:Git,kernelFunc:Wit};var TG;function Uit(r){TG=r.wasm.cwrap(Oa,"number",["number","number","number","number","number","number"])}function Hit(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i,softNmsSigma:a}=n,{boxes:u,scores:l}=e,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,m=TG(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Dd(t,m);t.wasm._free(g);let x=t.makeOutput([d],"int32",f),b=t.makeOutput([d],"float32",h);return[x,b]}var kG={kernelName:Oa,backendName:"wasm",setupFunc:Uit,kernelFunc:Hit};var qit=!1,EG=ue(Da,qit,"bool");var _G;function Kit(r){_G=r.wasm.cwrap(gs,null,["number","number","number","number","number"])}function 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BatchToSpaceND,up as Bincount,cp as BroadcastArgs,p1 as BroadcastTo,Sb as Callback,Py as CallbackList,lo as Cast,qo as Ceil,uo as ClipByValue,pp as Complex,_l as ComplexAbs,li as Concat,Ko as Conv2D,mp as Conv2DBackpropFilter,jo as Conv2DBackpropInput,Al as Conv3D,fp as Conv3DBackpropFilterV2,dp as Conv3DBackpropInputV2,Xo as Cos,Yo as Cosh,da as CropAndResize,fa as Cumprod,Zo as Cumsum,My as CustomCallback,ra as DataStorage,hp as DenseBincount,ha as DepthToSpace,Jo as DepthwiseConv2dNative,gp as DepthwiseConv2dNativeBackpropFilter,xp as DepthwiseConv2dNativeBackpropInput,yp as Diag,$l as Dilation2D,Xd as Dilation2DBackpropFilter,jd as Dilation2DBackpropInput,i0 as ENV,vb as EarlyStopping,bp as Einsum,ts as Elu,wp as EluGrad,qd as Environment,xa as Equal,ga as Erf,es as Exp,ui as ExpandDims,ya as Expm1,Cp as FFT,Dl as Fill,ba as FlipLeftRight,rs as Floor,ns as FloorDiv,Yd as FromPixels,os as FusedBatchNorm,Ii as FusedConv2D,Si as FusedDepthwiseConv2D,Bc as GPGPUContext,wa as GatherNd,ci as GatherV2,Ph as GraphModel,Ca as Greater,ss as GreaterEqual,Ly as History,Ip as IFFT,co as Identity,Sp as Imag,ye as InputSpec,Ia as IsFinite,Sa as IsInf,va as IsNan,zo as KernelBackend,Rl as LRN,Np as LRNGrad,Ch as LayerVariable,Bn as LayersModel,is as LeakyRelu,Na as Less,Ta as LessEqual,vp as LinSpace,as as Log,ka as Log1p,f1 as LogSoftmax,Ea as LogicalAnd,_a as LogicalNot,Aa as LogicalOr,m1 as LogicalXor,wlt as LowerBound,_u as MathBackendWebGL,ls as Max,cs as MaxPool,Fl as MaxPool3D,kp as MaxPool3DGrad,Tp as MaxPoolGrad,Ep as MaxPoolWithArgmax,us as Maximum,ps as Mean,ms as Min,fs as Minimum,ds as MirrorPad,$a as Mod,du as MomentumOptimizer,_p as Multinomial,hs as Multiply,pi as Neg,Ra as NonMaxSuppressionV3,Fa as NonMaxSuppressionV4,Oa as NonMaxSuppressionV5,Da as NotEqual,k0 as OP_SCOPE_SUFFIX,gs as OneHot,mi as OnesLike,Wr as Optimizer,Ws as OptimizerConstructors,fi as Pack,xs as PadV2,Clt as Pool,ys as Pow,bs as Prelu,ws as Prod,hu as RMSPropOptimizer,Tn as RNN,Ap as RaggedGather,$p as RaggedRange,Dp as RaggedTensorToTensor,Ol as Range,x0 as Rank,Rp as Real,Qo as RealDiv,Pa as Reciprocal,Xe as Reduction,Cs as Relu,vs as Relu6,di as Reshape,Ss as ResizeBilinear,Op as ResizeBilinearGrad,Is as ResizeNearestNeighbor,Fp as ResizeNearestNeighborGrad,Ns as Reverse,qa as RotateWithOffset,Ts as Round,ks as Rsqrt,Bi as SGDOptimizer,La as ScatterNd,Pp as SearchSorted,hi as Select,Ma as Selu,qi as Sequential,_s as Sigmoid,Ba as Sign,Es as Sin,za as Sinh,gi as Slice,Ds as Softmax,Va as Softplus,xi as SpaceToBatchND,Pl as SparseFillEmptyRows,Ga as SparseReshape,Ll as SparseSegmentMean,Ml as SparseSegmentSum,Lp as SparseToDense,yi as SplitV,As as Sqrt,zl as Square,Rs as SquaredDifference,po as Step,Wa as StridedSlice,Bl as StringNGrams,Vl as StringSplit,Gl as StringToHashBucketFast,Fs as Sub,$s as Sum,Jr as SymbolicTensor,Os as Tan,Ps as Tanh,Ft as Tensor,pe as TensorBuffer,Jn as Tile,Ua as TopK,Ha as Transform,Qn as Transpose,Mp as Unique,bi as Unpack,Wl as UnsortedSegmentSum,Ilt as UpperBound,Ka as Variable,wi as ZerosLike,Ci as _FusedMatMul,Ee as abs,ax as acos,lx as acosh,X as add,LE as addN,Zp as all,qu as any,Ai as argMax,ux as argMin,cx as asin,px as asinh,mx as atan,fx as atan2,dx as atanh,Yl as avgPool,gx as avgPool3d,gE as backend,v as backend_util,BE as basicLSTMCell,Di as batchNorm,xx as batchNorm2d,yx as batchNorm3d,bx as batchNorm4d,Zl as batchToSpaceND,wx as bincount,r6 as booleanMaskAsync,GE as broadcastArgs,Ri as broadcastTo,Vr as broadcast_util,nx as browser,wt as buffer,BZ as callbacks,J as cast,Cx as ceil,Cr as clipByValue,sn as clone,wn as complex,ne as concat,Ix as concat1d,Sx as concat2d,vx as concat3d,Nx as concat4d,K$ as constraints,Qp as conv1d,In as conv2d,em as conv2dTranspose,Tx as conv3d,Ex as conv3dTranspose,_lt as copyRegisteredKernels,Jl as cos,rm as cosh,hh as cosineWindow,Xu as cumprod,nm as cumsum,un as customGrad,AR as data,ch as denseBincount,W0 as deprecationWarn,_x as depthToSpace,Fi as depthwiseConv2d,UZ as deregisterOp,Kl as device_util,WE as diag,Ax as dilation2d,bpt as disableDeprecationWarnings,vt as dispose,wpt as disposeVariables,pt as div,$x as divNoNan,Dx as dot,lv as dropout,UE as einsum,Oi as elu,ypt as enableDebugMode,xpt as enableProdMode,uv as enclosingPowerOfTwo,Pn as engine,z as env,$r as equal,Rx as erf,Fx as euclideanNorm,er as exp,rr as expandDims,Ox as expm1,Yu as eye,au as fft,xo as fill,Tpt as findBackend,kpt as findBackendFactory,Pi as floor,Yp as floorDiv,hM as forceHalfFloat,uu as fused,Li as gather,p6 as gatherND,ox as gather_util,vpt as getBackend,u0 as getGradient,Jd as getKernel,zg as getKernelsForBackend,nlt as getThreadsCount,ik as gpgpu_util,yK as grad,bK as grads,Re as greater,ln as greaterEqual,tl as ifft,Xl as imag,Gs as image,d6 as inTopKAsync,j$ as initializers,Pv as input,_r as io,xm as irfft,Px as isFinite,Lx as isInf,Mx as isNaN,De as keep,Ur as kernel_impls,ED as layers,Ql as leakyRelu,om as less,Ln as lessEqual,pv as linalg,KE as linspace,L7 as loadGraphModel,M7 as loadGraphModelSync,hD as loadLayersModel,zx as localResponseNormalization,Sr as log,tu as log1p,Gx as logSigmoid,sm as logSoftmax,im as logSumExp,Rr as logicalAnd,eu as logicalNot,am as logicalOr,Wx as logicalXor,dX as losses,jE as lowerBound,Lt as matMul,yE as math,Ir as max,ru as maxPool,Hx as maxPool3d,XE as maxPoolWithArgmax,Sn as maximum,ve as mean,ah as memory,YE as meshgrid,_D as metrics,Ja as min,Mi as minimum,qx as mirrorPad,Kx as mod,H8 as model,AD as models,Zu as moments,o6 as movingAverage,D as mul,ZE as multiRNNCell,JE as multinomial,Ht as neg,gh as nextFrame,Qa as norm,Bs as notEqual,Ei as oneHot,cr as ones,yr as onesLike,T as op,QE as outerProduct,cn as pad,t_ as pad1d,e_ as pad2d,r_ as pad3d,n_ as pad4d,jx as pool,an as pow,ou as prelu,Jg as print,Xx as prod,Cpt as profile,o_ as raggedGather,s_ as raggedRange,i_ as raggedTensorToTensor,a_ as rand,v_ as randomGamma,tc as randomNormal,N_ as randomStandardNormal,zi as randomUniform,su as range,Spt as ready,Za as real,ty as reciprocal,Xp as registerBackend,K8 as registerCallbackConstructor,h1 as registerGradient,Lu as registerKernel,WZ as registerOp,$D as regularizers,Fr as relu,lm as relu6,Npt as removeBackend,R as reshape,pr as reverse,T_ as reverse1d,k_ as reverse2d,E_ as reverse3d,__ as reverse4d,lu as rfft,um as round,cm as rsqrt,mt as scalar,i6 as scatterND,lh as scatter_util,mh as searchSorted,pm as selu,mm as separableConv2d,q8 as sequential,Q as serialization,Q4 as setBackend,Ept as setPlatform,rlt as setThreadsCount,tlt as setWasmPath,elt as setWasmPaths,wT as setWebGLContext,A_ as setdiff1dAsync,Yr as sigmoid,ey as sign,fX as signal,fm as sin,dm as sinh,Rt as slice,hm as slice1d,dh as slice2d,gm as slice3d,ec as slice4d,Le as slice_util,iu as softmax,zs as softplus,nu as spaceToBatchND,hX as sparse,u6 as sparseToDense,mX as spectral,mr as split,Se as sqrt,Mt as square,ym as squaredDifference,Mn as squeeze,nr as stack,bo as step,ry as stridedSlice,gX as string,ct as sub,ft as sum,Wu as sumOutType,ny as tan,$i as tanh,ur as tensor,Me as tensor1d,Vs as tensor2d,rx as tensor3d,$_ as tensor4d,D_ as tensor5d,R_ as tensor6d,go as tensor_util,OE as test_util,B as tidy,Dr as tile,Ipt as time,oy as topk,ic as train,Ot as transpose,bm as truncatedNormal,sy as unique,Elt as unregisterGradient,klt as unregisterKernel,wm as unsortedSegmentSum,vr as unstack,sr as upcastType,F_ as upperBound,y as util,wK as valueAndGrad,CK as valueAndGrads,iy as variable,Bx as variableGrads,dlt as version,cR as version_converter,PE as version_core,Um as version_layers,olt as version_wasm,dM as version_webgl,t1e as webgl,dd as webgl_util,_e as where,ly as whereAsync,Ne as zeros,It as zerosLike};