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

/*
  Face-API
  homepage: <https://github.com/vladmandic/face-api>
  author: <https://github.com/vladmandic>'
*/

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Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return Hp.nextTensorId++}nextVariableId(){return Hp.nextVariableId++}clone(e){let t=O.runKernel(Di,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return O.runKernel(bi,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,bh(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=ib(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(ib(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=bh(h,this.backendName);A(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let b=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,b);let x=b.map(w=>w.rank!=null?w:this.makeTensorFromTensorInfo(w));if(a){let w=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(w)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:p}=e,d=ib(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(l,u,t,d,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=xb(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Kr(e[0])&&(r=e.map(o=>Sc(o)));let s=a.write(r,t,n),i=new Te(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=_I(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r={dataId:e,shape:t,dtype:n};return this.makeTensorFromTensorInfo(r,a)}makeTensorFromTensorInfo(e,t){let{dataId:n,shape:a,dtype:r}=e,s=new Te(a,r,n,this.nextTensorId());return this.trackTensor(s,t),s}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new ts(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*bb(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ts||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*bb(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=xb(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((u,p)=>{if(u==null){let d=n[p],c=Kh(d.size,d.dtype);return this.makeTensor(c,d.shape,d.dtype)}return u}),a(l.length>1?l:l[0],r,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Nx(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(A(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));A(r instanceof Te,()=>"The result y returned by f() must be a tensor.");let s=cD(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[r.id]=n==null?kD(r.shape):n,dD(i,s,l=>this.tidy(l),ID);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:r,grads:o}})}customGrad(e){return A(es(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{A(t.every(i=>i instanceof Te),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,a={};t.forEach((i,o)=>{a[o]=i});let r=(i,o)=>(n=e(...t,o),A(n.value instanceof Te,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),A(es(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),u=Array.isArray(l)?l:[l];A(u.length===t.length,()=>"The function f passed in customGrad(f) must 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AS=L({basicLSTMCell_:jM});function qM(e,t,n){let a=_(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);A(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),A(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),A(a.shape[0]%r===0,()=>`input tensor batch is ${a.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return O.runKernel(Al,s,i)}var $c=L({batchToSpaceND_:qM});function KM(e){let t;return e.rank===0||e.rank===1?t=W(e,[1,1,1,e.size]):e.rank===2?t=W(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function XM(e,t,n,a,r,s){s==null&&(s=.001);let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;a!=null&&(p=_(a,"offset","batchNorm")),A(o.rank===l.rank,()=>"Batch normalization 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2 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${p.rank}.`),ys(i,o,l,p,u,s)}var Zx=L({batchNorm2d_:YM});function ZM(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),A(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),A(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${p.rank}.`),ys(i,o,l,p,u,s)}var Jx=L({batchNorm3d_:ZM});function JM(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),A(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),A(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),ys(i,o,l,p,u,s)}var Qx=L({batchNorm4d_:JM});function QM(e,t,n){let a=_(e,"x","bincount"),r=_(t,"weights","bincount");A(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),A(n>=0,()=>`size must be non-negative, but got ${n}.`),A(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return O.runKernel(Zh,s,i)}var ev=L({bincount_:QM});function eP(e,t){let n=_(e,"s0","broadcastArgs","int32"),a=_(t,"s1","broadcastArgs","int32");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(a.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${a.rank}`);let r={s0:n,s1:a};return O.runKernel(Jh,r)}var FS=L({broadcastArgs_:eP});function tP(e,t){let n=_(e,"broadcastTo","x"),a=n.shape;if(t.some(l=>!(l>0)||l%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=W(n,l)}let r=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(r[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return rr(n);let i={x:n},o={reps:s};return O.runKernel(hs,i,o)}var Ks=L({broadcastTo_:tP});function nP(e){let t={x:_(e,"x","ceil","float32")};return O.runKernel(xi,t)}var tv=L({ceil_:nP});function gn(e,t,n){let a={shape:e,value:t,dtype:n};return O.runKernel(pc,{},a)}function aP(e,t,n){let a=_(e,"x","clipByValue");if(A(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`),t===n)return gn(a.shape,t,a.dtype);let r={x:a},s={clipValueMin:t,clipValueMax:n};return O.runKernel(ds,r,s)}var en=L({clipByValue_:aP});function rP(e){return Ze(e,0)}var nv=L({concat1d_:rP});function sP(e,t){return Ze(e,t)}var av=L({concat2d_:sP});function iP(e,t){return Ze(e,t)}var rv=L({concat3d_:iP});function oP(e,t){return Ze(e,t)}var sv=L({concat4d_:oP});function lP(e,t,n,a,r="NHWC",s=[1,1],i){let o=_(e,"x","conv2d","float32"),l=_(t,"filter","conv2d","float32"),u=o,p=!1;o.rank===3&&(p=!0,u=W(o,[1,o.shape[0],o.shape[1],o.shape[2]])),A(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),A(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),Tn("conv2d",a,i);let d=r==="NHWC"?u.shape[3]:u.shape[1];A(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),A(ur(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let c={x:u,filter:l},h={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},m=O.runKernel(vi,c,h);return p?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var At=L({conv2d_:lP});function uP(e,t,n,a,r="NWC",s=1,i){let o=_(e,"x","conv1d"),l=_(t,"filter","conv1d"),u=o,p=!1;o.rank===2&&(p=!0,u=W(o,[1,o.shape[0],o.shape[1]])),A(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),A(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),Tn("conv1d",a,i),A(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),A(ur(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),A(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let d=W(l,[1,l.shape[0],l.shape[1],l.shape[2]]),c=W(u,[u.shape[0],1,u.shape[1],u.shape[2]]),h=At(c,d,[1,n],a,"NHWC",[1,s],i);return p?W(h,[h.shape[2],h.shape[3]]):W(h,[h.shape[0],h.shape[2],h.shape[3]])}var Fm=L({conv1d_:uP});function pP(e,t,n,a,r,s="NHWC",i){A(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),A(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),A(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),A(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let p=s==="NHWC"?o[3]:o[1],d=s==="NHWC"?l.shape[3]:l.shape[1];A(p===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${p}) must match input depth for filter ${n.shape[2]}.`),A(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),Tn("conv2dDerInput",r,i);let c={dy:l,filter:n},h={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},m=O.runKernel(wi,c,h);return u?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var iv=L({conv2DBackpropInput_:pP});function cP(e,t,n,a,r,s){let i=_(e,"x","conv2dTranspose"),o=_(t,"filter","conv2dTranspose");return iv(n,i,o,a,r,"NHWC",s)}var Dm=L({conv2dTranspose_:cP});function dP(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=_(e,"x","conv3d"),o=_(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=W(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),A(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),A(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),A(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),A(ur(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),A(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let p={x:l,filter:o},d={strides:n,pad:a,dataFormat:r,dilations:s},c=O.runKernel(lc,p,d);return u?W(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var ov=L({conv3d_:dP});function hP(e,t,n,a,r){A(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=W(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],u=i.shape[4];A(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),A(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),A(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),A(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),A(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let p={dy:i,filter:n},d={pad:r,strides:a,inputShape:s},c=O.runKernel(nm,p,d);return o?W(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var DS=L({conv3DBackpropInput_:hP});function mP(e,t,n,a,r){let s=_(e,"x","conv3dTranspose"),i=_(t,"filter","conv3dTranspose");return DS(n,s,i,a,r)}var lv=L({conv3dTranspose_:mP});function fP(e){let t={x:_(e,"x","cos","float32")};return O.runKernel(ki,t)}var Ac=L({cos_:fP});function gP(e){let t={x:_(e,"x","cosh","float32")};return O.runKernel(Ii,t)}var Rm=L({cosh_:gP});function yP(e,t=0,n=!1,a=!1){let r={x:_(e,"x","cumprod")},s={axis:t,exclusive:n,reverse:a};return O.runKernel(Dl,r,s)}var Kp=L({cumprod_:yP});function bP(e,t=0,n=!1,a=!1){let r={x:_(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:a};return O.runKernel(Si,r,s)}var Mm=L({cumsum_:bP});function xP(e,t,n,a=!1){let r=_(e,"x","denseBincount"),s=_(t,"weights","denseBincount");A(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),A(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),A(n>=0,()=>`size must be non-negative, but got ${n}.`),A(s.size===r.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${s.shape}.`);let i={x:r,weights:s},o={size:n,binaryOutput:a};return O.runKernel(am,i,o)}var Sh=L({denseBincount_:xP});function vP(e,t,n="NHWC"){let a=_(e,"x","depthToSpace","float32"),r=n==="NHWC"?a.shape[1]:a.shape[2],s=n==="NHWC"?a.shape[2]:a.shape[3],i=n==="NHWC"?a.shape[3]:a.shape[1];A(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),A(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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f=Y(z(he(h,Y(ln(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&_e(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),P(()=>{this.accBeta1.assign(_r(this.beta1,this.iterations_+1)),this.accBeta2.assign(_r(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};uf.className="Adam";gs(uf);var pf=class extends Ar{constructor(e,t,n,a=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],P(()=>{this.iteration=xe(0).variable(),this.accBeta1=xe(t).variable()}),a==null&&(this.epsilon=O.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=pe(1,this.accBeta1),a=he(-this.learningRate,Y(z(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=O.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:qe(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:qe(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,d=Y(z(u,this.beta1),z(l,1-this.beta1)),c=z(p,this.beta2),h=Lt(l),m=pr(c,h);u.assign(d),p.assign(m);let f=Y(z(he(a,n),he(d,Y(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(Y(this.iteration,1)),this.accBeta1.assign(z(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&_e(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};pf.className="Adamax";gs(pf);var Bc=class extends Ar{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=O.registeredVariables[t];P(()=>{let s=Y(z(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Jt(xe(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};cf.className="Momentum";gs(cf);var df=class extends Ar{constructor(e,t=.9,n=0,a=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=O.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new V(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new V(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new V(`${s.length} of ${a} weights are not set: ${s}`)}nw(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${dw}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Bb(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return P(()=>{e=yt(e);let n=new Hs;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return Ep(this.outputs,n,t)})}computeMask(e,t){return P(()=>{e=yt(e);let n;return t==null?n=oi(null,e.length):n=yt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Th(e);if(t.length!==this.inputLayers.length)throw new V(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";n[u]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(jd);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(u.id)!==-1)continue;let p=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,x=n[b];p.push(x)}let d=u.computeOutputShape(On(p)),c=Th(d),h=u.inboundNodes.indexOf(l);for(let m=0;m<c.length;m++){let f=`${u.name}_${h}_${m}`;n[f]=c[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],p=`${o.name}_${l}_${u}`;s.push(p)}for(let i=0;i<s.length;i++){let o=s[i];tr(o in n),r.push(n[o])}return On(r)}runInternalGraph(e,t){t==null&&(t=oi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],p=t[o];n[l.id]=[u,p]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(jd);for(let o of a){let l=this.nodesByDepth[o];for(let u of l){let p=u.outboundLayer,d=u.inputTensors,c=u.outputTensors,h=new Array;for(let m of d)m.id in n&&h.push(n[m.id]);if(h.length===d.length){let m={},f,g,y,b;if(u.callArgs!=null&&(m=u.callArgs),h.length===1){let[x,w]=h[0];m.mask==null&&(m.mask=w),y=yt(p.call(x,m)),b=yt(p.computeMask(x,w)),f=[x],g=[w]}else f=h.map(x=>x[0]),g=h.map(x=>x[1]),m.mask==null&&(m.mask=g),y=yt(p.call(f,m)),b=yt(p.computeMask(f,g));if(p.activityRegularizer)throw new Re("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<c.length;++x){let w=c[x],I=y[x],T=b[x];n[w.id]=[I,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){tr(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=n[o.id];i.push(l.shape),r.push(l),s.push(u)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof Qa?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=Qa.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new V(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new V("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new V(`No such layer: ${e}`)}calculateLosses(){return P(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=Qa.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let p=0;p<s.inboundNodes.length;p++){let d=s.inboundNodes[p],c=Qa.nodeKey(s,p),h={};if(this.containerNodes.has(c)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${d.callArgs}. 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};vw.className="ThresholdedReLU";ne.registerClass(vw);var ww=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new mw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ne(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ww.className="Softmax";ne.registerClass(ww);function el(e,t,n){if(typeof e=="number")return oi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!V4(r))throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function Ga(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function nr(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+as([n-t,0]);else if(a==="same")e=e*t;else throw new V(`Unsupport padding mode: ${a}.`);return e}function kw(e,t){return P(()=>(Rt(t),t==="channelsFirst"?Ee(e,[0,2,3,1]):e))}function DN(e,t){return P(()=>(Rt(t),t==="channelsFirst"?Ee(e,[0,2,3,4,1]):e))}function CU(e,t,n,a=1,r="valid",s,i=1){return P(()=>{if(s==null&&(s=ja()),Rt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ee(e,[0,2,1])),r==="causal")throw new Re("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Fm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Xa(o,n)),o})}function Tk(e,t,n,a=[1,1],r="valid",s,i,o=null){return P(()=>{if(s==null&&(s=ja()),Rt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=kw(e,s);if(r==="causal")throw new Re("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=pl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ee(l,[0,3,1,2])),l})}function _U(e,t,n,a=[1,1,1],r="valid",s,i){return P(()=>{if(s==null&&(s=ja()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=DN(e,s);if(r==="causal")throw new Re("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=ov(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Xa(o,n)),s==="channelsFirst"&&(o=Ee(o,[0,4,1,2,3])),o})}var Iw=class extends Ge{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Iw.verifyArgs(t),this.rank=e,Qt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Re(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=el(t.kernelSize,e,"kernelSize"),this.strides=el(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ba(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=ss(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=St(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Kt(t.biasConstraint),this.biasRegularizer=Tt(t.biasRegularizer),this.activityRegularizer=Tt(t.activityRegularizer),this.dilationRate=el(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(tr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!qv(e.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:rs(this.activation),useBias:this.useBias,biasInitializer:Ct(this.biasInitializer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),biasConstraint:qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Kc=class extends Iw{constructor(e,t){super(e,t),this.kernel=null,Kc.verifyArgs(t),this.filters=t.filters,Qt(this.filters,"filters"),this.kernelInitializer=St(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Kt(t.kernelConstraint),this.kernelRegularizer=Tt(t.kernelRegularizer)}build(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return P(()=>{e=Ne(e);let n,a=this.bias==null?null:this.bias.read(),r=BT(this.activation.getClassName());if(r!=null&&this.rank===2)n=Tk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=CU(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Tk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=_U(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Re("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Qe(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=Ga(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:Ct(this.kernelInitializer),kernelRegularizer:pt(this.kernelRegularizer),kernelConstraint:qt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new V(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Xc=class extends Kc{constructor(e){super(2,e),Xc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!qv(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Xc.className="Conv2D";ne.registerClass(Xc);var Yc=class extends Kc{constructor(e){super(3,e),Yc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Yc.className="Conv3D";ne.registerClass(Yc);var Sw=class extends Xc{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ne(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=nr(o,d,u,this.padding),m=nr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ee(n,[0,2,3,1]));let g=Dm(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ee(g,[0,3,1,2])),this.bias!=null&&(g=Xa(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Qe(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=nr(t[a],o,s,this.padding),t[r]=nr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Sw.className="Conv2DTranspose";ne.registerClass(Sw);var Tw=class extends Yc{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ne(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=nr(l,m,d,this.padding),b=nr(u,f,c,this.padding),x=nr(p,g,h,this.padding),w=[r,y,b,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ee(n,[0,2,3,4,1]));let I=lv(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=Ee(I,[0,4,1,2,3])),this.bias!==null&&(I=Xa(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Qe(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=nr(t[a],u,i,this.padding),t[r]=nr(t[r],p,o,this.padding),t[s]=nr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Tw.className="Conv3DTranspose";ne.registerClass(Tw);var RN=class extends Kc{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Tt(t.depthwiseRegularizer),this.depthwiseConstraint=Kt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Tt(t.pointwiseRegularizer),this.pointwiseConstraint=Kt(t.pointwiseConstraint)}build(e){if(e=Qe(e),e.length<this.rank+2)throw new V(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{e=Ne(e);let n;if(this.rank===1)throw new Re("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ee(e,[0,2,3,1])),n=vs(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Xa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ee(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.pointwiseInitializer=Ct(this.pointwiseInitializer),e.depthwiseRegularizer=pt(this.depthwiseRegularizer),e.pointwiseRegularizer=pt(this.pointwiseRegularizer),e.depthwiseConstraint=qt(this.depthwiseConstraint),e.pointwiseConstraint=qt(this.pointwiseConstraint),e}};RN.className="SeparableConv";var Nw=class extends RN{constructor(e){super(2,e)}};Nw.className="SeparableConv2D";ne.registerClass(Nw);var Tf=class extends Kc{constructor(e){super(1,e),Tf.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!qv(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Tf.className="Conv1D";ne.registerClass(Tf);var Cw=class extends Ge{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return P(()=>{if(e=Ne(e),this.dataFormat==="channelsLast"){let n=Kd(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Kd(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Kd(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Kd(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Cw.className="Cropping2D";ne.registerClass(Cw);var _w=class extends Ge{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,z4(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return P(()=>{let n=Ne(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ee(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?za.resizeNearestNeighbor(n,[r,s]):za.resizeBilinear(n,[r,s]);return Ee(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?za.resizeNearestNeighbor(n,[r,s]):za.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};_w.className="UpSampling2D";ne.registerClass(_w);function EU(e,t,n=[1,1],a="valid",r,s){return P(()=>{r==null&&(r=ja()),Rt(r);let i=kw(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=bs(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ee(i,[0,3,1,2])),i})}var Ew=class extends Iw{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Kt(e.depthwiseConstraint),this.depthwiseRegularizer=Tt(e.depthwiseRegularizer)}build(e){if(e=Qe(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{e=Ne(e);let n=EU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Xa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ga(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ga(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.depthwiseRegularizer=pt(this.depthwiseRegularizer),e.depthwiseConstraint=qt(this.depthwiseRegularizer),e}};Ew.className="DepthwiseConv2D";ne.registerClass(Ew);function MN(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function PN(e,t,n,a=!1,r,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ha(2,l));if(t=Ee(t,u),s!=null)throw new Re("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=le(le(r,"bool"),"float32"),r.rank===l-1&&(r=Zt(r,-1)),r=Ee(r,u)),a&&(t=fa(t,0),r!=null&&(r=fa(r,0)));let p=[],d,c=n,h=t.shape[0],m=ct(t),f;r!=null&&(f=ct(r));for(let y=0;y<h;++y){let b=m[y],x=P(()=>e(b,c));if(r==null)d=x[0],c=x[1];else{let w=P(()=>{let I=f[y],T=pe(ta(I),I),C=Y(z(x[0],I),z(c[0],T)),E=c.map(($,R)=>Y(z(x[1][R],I),z($,T)));return{output:C,newStates:E}});d=w.output,c=w.newStates}o&&p.push(d)}let g;return o&&(g=Ft(p,1)),[d,g,c]})}var dr=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new _f({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ha(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Lb(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return P(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Re("Constants support is not implemented in RNN yet.");Lb(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new zt({shape:[t,null,...n]});let a=[e[0]].concat(e.slice(2));this.cell.build(a);let r;if(Array.isArray(this.cell.stateSize)?r=this.cell.stateSize:r=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),r))throw new V(`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=r.map(s=>new zt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("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(a=>It([n,a])):this.states_=[It([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_[0]=It([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(r.shape,i))throw new V(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>Jt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=MN(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Ba){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Ne(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=PN((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return P(()=>{let t=It(e.shape);return t=fe(t,[1,2]),t=Uc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Pb(t,[1,n]):t):this.cell.stateSize>1?[Pb(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===dr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ua(a,n);return new e(Object.assign(t,{cell:r}))}};dr.className="RNN";ne.registerClass(dr);var Zc=class extends Ge{},Nf=class extends Zc{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Qt(this.units,"units"),this.activation=ss(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Kt(e.kernelConstraint),this.recurrentConstraint=Kt(e.recurrentConstraint),this.biasConstraint=Kt(e.biasConstraint),this.dropout=cl([1,as([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=cl([1,as([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>ta(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>ta(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=sr(z(e,s),this.kernel.read()):r=sr(e,this.kernel.read()),this.bias!=null&&(r=Xa(r,this.bias.read())),i!=null&&(n=z(n,i));let o=Y(r,sr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:rs(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Nf.className="SimpleRNNCell";ne.registerClass(Nf);var $w=class extends dr{constructor(e){e.cell=new Nf(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};$w.className="SimpleRNN";ne.registerClass($w);var Cf=class extends Zc{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Qt(this.units,"units"),this.activation=ss(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ss(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Kt(e.kernelConstraint),this.recurrentConstraint=Kt(e.recurrentConstraint),this.biasConstraint=Kt(e.biasConstraint),this.dropout=cl([1,as([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=cl([1,as([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>ta(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>ta(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=z(e,r[0]));let u=sr(e,this.kernel.read());this.useBias&&(u=Xa(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=z(a,s[0]));let p=this.recurrentKernel.read(),[d,c]=zn(p,[2*this.units,this.units],p.rank-1),h=sr(a,d),[m,f,g]=zn(u,3,u.rank-1),[y,b]=zn(h,2,h.rank-1);i=this.recurrentActivation.apply(Y(m,y)),o=this.recurrentActivation.apply(Y(f,b));let x=sr(z(o,a),c);l=this.activation.apply(Y(g,x));let w=Y(z(i,a),z(Y(1,bt(i)),l));return[w,w]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:rs(this.activation),recurrentActivation:rs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},e),t)}};Cf.className="GRUCell";ne.registerClass(Cf);var Aw=class extends dr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Cf(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Aw.className="GRU";ne.registerClass(Aw);var Jc=class extends Zc{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Qt(this.units,"units"),this.activation=ss(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ss(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Kt(e.kernelConstraint),this.recurrentConstraint=Kt(e.recurrentConstraint),this.biasConstraint=Kt(e.biasConstraint),this.dropout=cl([1,as([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=cl([1,as([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Qe(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Fa{apply(i,o){let l=r.apply([s]),u=new ff().apply([s]),p=r.apply([s*2]);return uk(uk(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>ta(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>ta(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0<this.dropout&&this.dropout<1&&(e=z(e,s[0]));let d=sr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=z(a,i[0])),d=Y(d,sr(a,this.recurrentKernel.read())),this.useBias&&(d=Xa(d,this.bias.read()));let[c,h,m,f]=zn(d,4,d.rank-1);o=this.recurrentActivation.apply(c),l=this.recurrentActivation.apply(h),u=Y(z(l,r),z(o,this.activation.apply(m))),p=this.recurrentActivation.apply(f);let g=z(p,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:rs(this.activation),recurrentActivation:rs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),recurrentConstraint:qt(this.recurrentConstraint),biasConstraint:qt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},e),t)}};Jc.className="LSTMCell";ne.registerClass(Jc);var Fw=class extends dr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Jc(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Fw.className="LSTM";ne.registerClass(Fw);var _f=class extends Zc{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Lb(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Xs(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ua(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return zb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}nw(t)}};_f.className="StackedRNNCells";ne.registerClass(_f);function is(e){let{ones:t,rate:n,training:a=!1,count:r=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),n):KT(t(),n),o=()=>Hc(i,t,a);return!r||r<=1?Jt(o().clone()):Array(r).fill(void 0).map(o).map(l=>Jt(l.clone()))}var $U=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},ON=class extends dr{constructor(e){if(e.unroll)throw new Re("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Re("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new zt({ndim:5})]}call(e,t){return P(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return P(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new vr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("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(()=>It(r)):this.states_=[It(r)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_[0]=It(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!v.arraysEqual(i.shape,o))throw new V(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Jt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],p=Ga(l,a[0],r,s[0],i[0]),d=Ga(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};ON.className="ConvRNN2D";var Ef=class extends Jc{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,Qt(this.filters,"filters"),this.kernelSize=el(n,2,"kernelSize"),this.kernelSize.forEach(o=>Qt(o,"kernelSize")),this.strides=el(a||1,2,"strides"),this.strides.forEach(o=>Qt(o,"strides")),this.padding=r||"valid",ba(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=el(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Qt(o,"dilationRate"))}build(e){var t;e=Qe(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Fa{apply(p,d){let c=l.apply([u]),h=Zn([u]),m=l.apply([u*2]);return Kv([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return P(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=is({ones:()=>ta(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=is({ones:()=>ta(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,w,I,T]=zn(this.kernel.read(),i,b),[C,E,$,R]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,w,E,this.padding),d=this.inputConv(d,I,$,this.padding),c=this.inputConv(c,T,R,this.padding);let[F,S,M,B]=zn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,F),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),y=this.recurrentConv(y,B);let U=this.recurrentActivation.apply(Y(u,m)),G=this.recurrentActivation.apply(Y(p,f)),q=Y(z(G,s),z(U,this.activation.apply(Y(d,g)))),K=z(this.recurrentActivation.apply(Y(c,y)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=$U(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=At(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Xa(r,n,this.dataFormat):r}recurrentConv(e,t){return At(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Ef.className="ConvLSTM2DCell";ne.registerClass(Ef);var Dw=class extends ON{constructor(e){let t=new Ef(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};Dw.className="ConvLSTM2D";ne.registerClass(Dw);var $f=class extends Ge{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Hc(()=>KT(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};$f.className="Dropout";ne.registerClass($f);var Rw=class extends $f{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Rw.className="SpatialDropout1D";ne.registerClass(Rw);var Mw=class extends Ge{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Qt(this.units,"units"),this.activation=ss(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Kt(e.kernelConstraint),this.biasConstraint=Kt(e.biasConstraint),this.kernelRegularizer=Tt(e.kernelRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Qe(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Qe(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e),a=BT(this.activation.getClassName()),r;return a!=null?r=sr(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=sr(n,this.kernel.read()),this.bias!=null&&(r=Xa(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:rs(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:qt(this.kernelConstraint),biasConstraint:qt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Mw.className="Dense";ne.registerClass(Mw);var Pw=class extends Ge{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Qe(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Qr(e,1)]}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=Ee(n,a)}return H4(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Pw.className="Flatten";ne.registerClass(Pw);var Ow=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.activation=ss(e.activation)}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e);return this.activation.apply(n)})}getConfig(){let e={activation:rs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Ow.className="Activation";ne.registerClass(Ow);var Lw=class extends Ge{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return P(()=>(e=Ne(e),U4(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Lw.className="RepeatVector";ne.registerClass(Lw);var zw=class extends Ge{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new V("Can only specifiy one unknown dimension.");else r*=l}let i=Qr(e);if(s!==null){if(r===0||i%r!==0)throw new V(n);a[s]=i/r}else if(i!==r)throw new V(n);return a}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return W(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};zw.className="Reshape";ne.registerClass(zw);var Ww=class extends Ge{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Ha(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=Qe(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ee(Ne(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ww.className="Permute";ne.registerClass(Ww);var Bw=class extends Ge{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Ne(e),a=-1;return qp(ii(n,this.maskValue),a)}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e),a=-1,r=!0,s=qp(ii(n,this.maskValue),a,r);return z(n,le(s,n.dtype))})}};Bw.className="Masking";ne.registerClass(Bw);var Vw=class extends Ge{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(yt(e.inputLength))}this.inputDim=e.inputDim,Qt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Qt(this.outputDim,"outputDim"),this.embeddingsInitializer=St(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Tt(e.embeddingsRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.embeddingsConstraint=Kt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return P(()=>this.maskZero?(e=Ne(e),ii(e,qe(e))):null)}computeOutputShape(e){if(e=Qe(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e);n.dtype!=="int32"&&(n=yo(n,"int32"));let a=qT(this.embeddings.read(),W(n,[n.size]));return W(a,Qe(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ct(this.embeddingsInitializer),embeddingsRegularizer:pt(this.embeddingsRegularizer),activityRegularizer:pt(this.activityRegularizer),embeddingsConstraint:qt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Vw.className="Embedding";ne.registerClass(Vw);var xo=class extends Ge{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Re}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new V("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Qe(e)]),e=e,e.length<2)throw new V(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Kw.className="Concatenate";ne.registerClass(Kw);function Sp(e,t){for(;e<0;)e+=t;return e}function AU(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Re("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Re("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return P(()=>{let i;if(a>r){i=a-r;let l=[];for(let u=0;u<i;++u)l.push(1);t=W(t,t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let u=0;u<i;++u)l.push(1);e=W(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=fe(z(e,t),s[0]):o=fe(z(Ee(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Fe(e,t,l,u)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let u=[];for(let p=l;p<l+i;++p)u.push(p);o=ws(o,u)}return o.shape.length===1&&(o=Zt(o,1)),o})}var Xw=class extends xo{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Re("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new V(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} 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Ge{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e);return Hc(()=>Y(mf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Yw.className="GaussianNoise";ne.registerClass(Yw);var Zw=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e);return this.rate>0&&this.rate<1?Hc(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return z(n,mf(n.shape,1,a))},()=>n,t.training||!1):n})}};Zw.className="GaussianDropout";ne.registerClass(Zw);var Jw=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ne(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return P(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Hc(()=>{let a=Ne(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=Er(Au(n),this.rate);o=yo(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,p=Y(z(a,o),z(Y(o,-1),i));return Y(z(p,l),u)},()=>Ne(e),t.training||!1)}return e})}};Jw.className="AlphaDropout";ne.registerClass(Jw);function Zp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=Zx(e,t,n,a,r,s);else if(e.rank===3)i=Jx(e,t,n,a,r,s);else if(e.rank===4)i=Qx(e,t,n,a,r,s);else throw new Re(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function FU(e,t,n,a,r=.001){return P(()=>{let s=Mc(e,a),i=s.mean,o=s.variance;return[Zp(e,i,o,n,t,r),i,o]})}function DU(e,t,n,a,r=.001){return P(()=>{let s=Mc(e,a),i=s.mean,o=s.variance,l=[];for(let h of 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training,a=Ne(e),r=a.shape,s=r.length,i=Ha(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=oi(1,s);l[o]=r[o];let u=i.slice();u.sort();let p=!v.arraysEqual(u,Ha(0,s).slice(0,s-1)),d=()=>{if(p){let g=W(this.movingMean.read(),l),y=W(this.movingVariance.read(),l),b=this.center?W(this.beta.read(),l):null,x=this.scale?W(this.gamma.read(),l):null;return Zp(a,g,y,b,x,this.epsilon)}else return Zp(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[c,h,m]=RU(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{P(()=>{let x=1-b,w=g.read(),I=z(pe(w,y),x);g.write(pe(w,I))})};return f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),movingMeanInitializer:Ct(this.movingMeanInitializer),movingVarianceInitializer:Ct(this.movingVarianceInitializer),betaRegularizer:pt(this.betaRegularizer),gammaRegularizer:pt(this.gammaRegularizer),betaConstraint:qt(this.betaConstraint),gammaConstraint:qt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Qw.className="BatchNormalization";ne.registerClass(Qw);var e0=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=St(e.betaInitializer||"zeros"),this.gammaInitializer=St(e.gammaInitializer||"ones"),this.betaRegularizer=Tt(e.betaRegularizer),this.gammaRegularizer=Tt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Qe(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Jr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Ne(e),a=n.shape,r=a.length;return P(()=>{let{mean:s,variance:i}=Mc(n,this.axis,!0),o=oi(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],c=[];for(let h=0;h<r;++h)this.axis.indexOf(h)!==-1?(d.push(a[h]),c.push(1)):(d.push(1),c.push(a[h]));return s=Ln(s,d),i=Ln(i,d),u!=null&&(u=Ln(u,c)),p!=null&&(p=Ln(p,c)),Zp(n,s,i,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),betaRegularizer:pt(this.betaRegularizer),gammaRegularizer:pt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};e0.className="LayerNormalization";ne.registerClass(e0);function MU(e,t,n){return P(()=>{if(e.rank!==4)throw new V(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ja()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ya(e,a)})}var t0=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ja():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new V(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new V(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new V(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=Qe(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return P(()=>MU(Ne(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};t0.className="ZeroPadding2D";ne.registerClass(t0);function Af(e,t,n,a,r,s){return P(()=>{Rt(r),UT(s),ba(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=kw(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Dt(e,t,n,o):i=ga(e,t,n,o),r==="channelsFirst"&&(i=Ee(i,[0,3,1,2])),i})}function LN(e,t,n,a,r,s){return P(()=>{Rt(r),UT(s),ba(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=DN(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Sv(e,t,n,o):i=Yx(e,t,n,o),r==="channelsFirst"&&(i=Ee(i,[0,4,1,2,3])),i})}var zN=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Qt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ba(this.padding),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){e=Qe(e);let t=Ga(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return P(()=>{this.invokeCallHook(e,t),e=Uc(Ne(e),2);let n=this.poolingFunction(Ne(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ws(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},n0=class extends zN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ba(a),Af(e,t,n,a,r,"max")}};n0.className="MaxPooling1D";ne.registerClass(n0);var a0=class extends zN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ba(a),Af(e,t,n,a,r,"avg")}};a0.className="AveragePooling1D";ne.registerClass(a0);var WN=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new V(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Qt(this.poolSize,"poolSize"),Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),ba(this.padding),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ga(t,this.poolSize[0],this.padding,this.strides[0]),n=Ga(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ne(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},r0=class extends WN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ba(a),Af(e,t,n,a,r,"max")}};r0.className="MaxPooling2D";ne.registerClass(r0);var s0=class extends WN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ba(a),Af(e,t,n,a,r,"avg")}};s0.className="AveragePooling2D";ne.registerClass(s0);var BN=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Qt(this.poolSize,"poolSize"),Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),ba(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ga(t,this.poolSize[0],this.padding,this.strides[0]),n=Ga(n,this.poolSize[1],this.padding,this.strides[1]),a=Ga(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ne(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},i0=class extends BN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ba(a),LN(e,t,n,a,r,"max")}};i0.className="MaxPooling3D";ne.registerClass(i0);var o0=class extends BN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),ba(a),LN(e,t,n,a,r,"avg")}};o0.className="AveragePooling3D";ne.registerClass(o0);var VN=class extends Ge{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Re}},l0=class extends VN{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Ne(e);return Nt(n,1)})}};l0.className="GlobalAveragePooling1D";ne.registerClass(l0);var u0=class extends VN{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Ne(e);return ha(n,1)})}};u0.className="GlobalMaxPooling1D";ne.registerClass(u0);var UN=class extends Ge{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Re}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},p0=class extends UN{call(e,t){return P(()=>{let n=Ne(e);return this.dataFormat==="channelsLast"?Nt(n,[1,2]):Nt(n,[2,3])})}};p0.className="GlobalAveragePooling2D";ne.registerClass(p0);var c0=class extends UN{call(e,t){return P(()=>{let n=Ne(e);return this.dataFormat==="channelsLast"?ha(n,[1,2]):ha(n,[2,3])})}};c0.className="GlobalMaxPooling2D";ne.registerClass(c0);var GN=class extends Ge{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=Ua(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},d0=class extends GN{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=Qe(e),e.length<3)throw new V(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Qe(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return P(()=>(e=Ne(e),PN((n,a)=>[Ne(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};d0.className="TimeDistributed";ne.registerClass(d0);function PU(e){go(L4,"BidirectionalMergeMode",e)}var OU="concat",h0=class extends GN{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ua(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ua(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?OU:e.mergeMode,PU(this.mergeMode),e.weights)throw new Re("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):On(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=MN(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new V("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(p=>new zt({shape:p.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(a!=null)throw new Re("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Ba;for(let l of s)if(l instanceof Ba!==o)throw new V("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=p,d}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=fa(r,1));let i;return this.mergeMode==="concat"?i=Kv([a,r]):this.mergeMode==="sum"?i=Y(a,r):this.mergeMode==="ave"?i=z(.5,Y(a,r)):this.mergeMode==="mul"?i=z(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Xs(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Xs(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Ua(t.layer);if(delete t.layer,t.numConstants!=null)throw new Re("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};h0.className="Bidirectional";ne.registerClass(h0);var m0=class extends Ge{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return 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r=k("default",e,t,n);return[vn(e.name,t,n)||r];case"Placeholder":return[vn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let p=k("x",e,t,n);return[kr(p)]}case"IdentityN":return k("x",e,t,n).map(p=>kr(p));case"Snapshot":let s=k("x",e,t,n);return[kr(s)];case"Shape":return[a.tensor1d(k("x",e,t,n).shape,"int32")];case"ShapeN":return k("x",e,t,n).map(p=>a.tensor1d(p.shape));case"Size":return[a.scalar(k("x",e,t,n).size,"int32")];case"Rank":return[a.scalar(k("x",e,t,n).rank,"int32")];case"NoOp":return[a.scalar(1)];case"Print":let i=k("x",e,t,n),o=k("data",e,t,n),l=k("message",e,t,n),u=k("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(l);for(let p=0;p<o.length;p++)console.log(Array.prototype.slice.call(o[p].dataSync()).slice(0,u));return[i];default:throw TypeError(`Node type ${e.op} is not 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not implemented`)}},s6=(e,t,n,a=un)=>{switch(e.op){case"ResizeBilinear":{let r=k("images",e,t,n),s=k("size",e,t,n),i=k("alignCorners",e,t,n),o=k("halfPixelCenters",e,t,n);return[a.image.resizeBilinear(r,[s[0],s[1]],i,o)]}case"ResizeNearestNeighbor":{let r=k("images",e,t,n),s=k("size",e,t,n),i=k("alignCorners",e,t,n),o=k("halfPixelCenters",e,t,n);return[a.image.resizeNearestNeighbor(r,[s[0],s[1]],i,o)]}case"CropAndResize":{let r=k("image",e,t,n),s=k("boxes",e,t,n),i=k("boxInd",e,t,n),o=k("cropSize",e,t,n),l=k("method",e,t,n),u=k("extrapolationValue",e,t,n);return[a.image.cropAndResize(r,s,i,o,l,u)]}case"ImageProjectiveTransformV3":{let r=k("images",e,t,n),s=k("transforms",e,t,n),i=k("outputShape",e,t,n),o=k("fillValue",e,t,n),l=k("interpolation",e,t,n),u=k("fillMode",e,t,n);return[a.image.transform(r,s,l.toLowerCase(),u.toLowerCase(),o,i)]}default:throw TypeError(`Node type ${e.op} is not 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o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.max(k("x",e,t,n),o,l)]}case"Mean":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.mean(k("x",e,t,n),o,l)]}case"Min":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.min(k("x",e,t,n),o,l)]}case"Sum":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.sum(k("x",e,t,n),o,l)]}case"All":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.all(k("x",e,t,n),o,l)]}case"Any":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.any(k("x",e,t,n),o,l)]}case"ArgMax":{let o=k("axis",e,t,n);return[a.argMax(k("x",e,t,n),o)]}case"ArgMin":{let o=k("axis",e,t,n);return[a.argMin(k("x",e,t,n),o)]}case"Prod":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.prod(k("x",e,t,n),o,l)]}case"Cumprod":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumprod(k("x",e,t,n),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumsum(k("x",e,t,n),o,l,u)]}case"Bincount":let r=k("x",e,t,n),s=k("weights",e,t,n),i=k("size",e,t,n);return[a.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,n),l=k("weights",e,t,n),u=k("size",e,t,n),p=k("binaryOutput",e,t,n);return[a.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},p6=(e,t,n,a=un)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),i=k("tensors",e,t,n);return i=i.slice(0,r),[a.concat(i,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gather(r,a.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),i=k("x",e,t,n),o=k("indices",e,t,n);return[a.gather(i,a.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o<r.length;o++)r[o]&&s.push(o);let i=k("x",e,t,n);return[a.reverse(i,s)]}case"ReverseV2":{let r=k("axis",e,t,n),s=k("x",e,t,n);return[a.reverse(s,r)]}case"Slice":{let r=k("begin",e,t,n),s=k("size",e,t,n);return[a.slice(k("x",e,t,n),r,s)]}case"StridedSlice":{let 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r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),s=k("outputShape",e,t,n),i=k("sparseValues",e,t,n),o=k("defaultValue",e,t,n);return[a.sparseToDense(r,i,s,i.dtype===o.dtype?o:a.cast(o,i.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},c6=(e,t,n,a=un)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},d6=(e,t,n,a=un)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},h6=(e,t,n,a=un)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},m6=(e,t,n,a=un)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Dk(e,t,n,a,r=P){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>GH(i,o,l));case"basic_math":return r(()=>HH(i,o,l));case"control":return ZH(i,o,l);case"convolution":return r(()=>JH(i,o,l));case"creation":return r(()=>QH(i,o,l));case"dynamic":return e6(i,o,l);case"evaluation":return r(()=>t6(i,o,l));case"image":return r(()=>s6(i,o,l));case"graph":return r(()=>n6(i,o,l));case"logical":return r(()=>i6(i,o,l));case"matrices":return r(()=>o6(i,o,l));case"normalization":return r(()=>l6(i,o,l));case"reduction":return r(()=>u6(i,o,l));case"slice_join":return r(()=>p6(i,o,l));case"sparse":return r(()=>c6(i,o,l));case"spectral":return r(()=>d6(i,o,l));case"string":return r(()=>h6(i,o,l));case"transformation":return r(()=>m6(i,o,l));case"hash_table":return r6(i,o,l,a);case"custom":let u=e2(i.op);if(u&&u.customExecutor)return u.customExecutor(new UH(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Rk=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Mk(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>Xn(c)[0]),p=[];a!=null&&(p=a.map(c=>Xn(c.name)[0]));let d=[...t];for(;d.length>0;){let c=d.pop();if((k2(c)||x6(c)||v6(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&u.indexOf(c.name)===-1&&p.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function f6(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(p=>Xn(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{a.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{a.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{a.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(d=>{!l.has(d.name)&&a.has(d.name)&&d.inputs.every(c=>l.has(c.name))&&s.push(d)})}return u}var g6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],y6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],b6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function k2(e){return g6.indexOf(e.op)>=0}function x6(e){return y6.indexOf(e.op)>=0}function v6(e){return b6.indexOf(e.op)>=0}var nx=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new nx(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=Mk(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return f6(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(p=>this.graph.nodes[Xn(p)[0]]),r=t.map(p=>Xn(p)[0]),s=r.map(p=>this.graph.nodes[p]);this.resetIntermediateTensors(),s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return P(()=>{let p=new Rk(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=Xn(m),y=[];y[g]=e[m],d[f]=y});let c=this.getFrozenTensorIds(d),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!d[f.name]){let g=Dk(f,d,p,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);d[f.name]=g,this.checkTensorForDisposal(f.name,f,d,p,c,r,h)}}return this.parent==null&&p.dispose(c),t.map(m=>vn(m,d,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=kH(o.name,n,a);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];if(p===1){if(!this.keepTensorForDebug)u.dispose();else{let[d,c]=ar(t.name,a);this.intermediateTensors[d]?this.intermediateTensors[d][c]=u:(this.intermediateTensors[d]=[],this.intermediateTensors[d][c]=u)}delete i[u.id]}else p!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(t=>{t&&!t.kept&&!t.isDisposed&&!this.keepIds.has(t.id)&&t.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,a={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let s=new Rk(this.weightMap,a,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,s,t,n);let i=t.map(u=>vn(u,this.tensorsMap,s)),o=i.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...o,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&s.dispose(this.keepIds),i}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(b=>this.graph.nodes[Xn(b)[0]]),i=n.map(b=>Xn(b)[0]),o=i.map(b=>this.graph.nodes[b]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:d}=Mk(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(b=>({node:b,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(b=>{let[x,w]=Xn(b),I=[];I[w]=e[b],h[x]=I});let m={},f=this.getFrozenTensorIds(h),g={};for(;c.length>0;){let b=this.processStack(s,c,t,h,g,f,i,m,l);await Promise.all(b)}p==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(b=>!k2(b)&&!vn(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw p!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&k("isConstant",p.node,a,n)&&([d]=ar(p.node.name,n)),a[p.node.name]==null){let c=Dk(p.node,a,n,this._resourceManager);d||([d]=ar(p.node.name,n));let h=n.currentContext;v.isPromise(c)?u.push(c.then(m=>(a[d]=m,n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=ar(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!vn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!vn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Xn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Xn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Xn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},w6=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},k6="?tfjs-format=file",I6="model.json",k0=class{constructor(e,t={},n=Ut){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new w6}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new nx(Ek.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Ek.Instance.transformGraph(e.modelInitializer);this.initializer=new nx(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){let n=this.execute(e,this.outputNodes);if(this.structuredOutputKeys){let a=n instanceof Te?[n]:n,r={};return a.forEach((s,i)=>r[this.structuredOutputKeys[i]]=s),r}return n}normalizeInputs(e){if(!(e instanceof Te)&&!Array.isArray(e)){if(this.signature!=null&&this.signature.inputs!=null)for(let a in this.signature.inputs){let r=this.signature.inputs[a];r.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[r.resourceId])}return e}e=Array.isArray(e)?e:[e];let t=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+t!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-t} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((a,r)=>{let s=this.signature?this.signature.inputs[r]:null;return s!=null&&s.resourceId!=null?a[r]=this.resourceIdToCapturedInput[s.resourceId]:a[r]=e[n++],a},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=Object.keys(this.initializerSignature.outputs);for(let n=0;n<t.length;n++){let a=t[n],r=this.initializerSignature.outputs[a];this.resourceIdToCapturedInput[r.resourceId]=e[n]}}}execute(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async 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this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Pk=class extends tn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Va.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Va.getTensorsInContainer(n);for(let r of t)Va.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},T0=class extends tn{constructor(){super(),this.outputQueue=new I0,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}}},q6=class extends T0{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await 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Mismatched at element ${this.count}.`);case Yr.SHORTEST:return{value:null,done:!0};case Yr.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},$2=class extends tn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new C2(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},X6=class extends $2{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=E6.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Mu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
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`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Zd='"',Np=Symbol("out"),Ok=Symbol("field"),Jd=Symbol("quote"),fb=Symbol("quoteafterquote"),Lk=Symbol("quoteinquote"),F2=class extends Mu{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new A2(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=Np;for(let i=0;i<r;i++)switch(s){case Np:switch(e.charAt(i)){case Zd:a=i+1,s=Jd;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Np;break;default:s=Ok,a=i;break}break;case Ok:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=Np,a=i+1;break;default:}break;case Jd:switch(e.charAt(i)){case Zd:s=fb;break;default:}break;case fb:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=Np,a=i+1;break;case Zd:s=Jd;break;default:s=Lk;break}break;case Lk:switch(e.charAt(i)){case Zd:s=Jd;break;default:}break;default:}if(s===fb?n.push(e.substring(a,r-1)):n.push(e.substring(a)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},D2=class extends tn{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!H().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new D2(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),kn(n,t)}},R2=class extends tn{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=_a([s,r,o,i],[1,4])}else this.cropBox=_a([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!H().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new R2(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.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(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=co.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=Zt(le(e,"float32"),0),n;n=za.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},M2=class{},P2=class extends tn{split(e){return new ej(this,e)}},ej=class extends P2{constructor(e,t){super(),this.upstream=e,this.impl=new tj(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},tj=class extends T0{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},nj=class extends tn{decodeUTF8(){return new aj(this)}},aj=class extends P2{constructor(e){super(),this.upstream=e,this.impl=new rj(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},rj=class extends T0{constructor(e){if(super(),this.upstream=e,H().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=wI();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return H().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},O2=class extends nj{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(H().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function sj(e,t={},n){let a,r;typeof e=="string"?a=e:(a=e.url,r=ij(e));let s=await(n||v.fetch)(a,r);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new O2(i,t)}else throw new Error(s.statusText)}var ij=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function L2(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var z2=class extends M2{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(L2(this.input)&&H().get("IS_NODE")){let e=xx();this.input=e.readFileSync(this.input.slice(7))}return new O2(this.input,this.options)}},W2=class extends M2{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return L2(this.url)?new z2(this.url,this.fileOptions).iterator():sj(this.url,this.fileOptions)}};function oj(e,t={}){return new F2(new W2(e),t)}function lj(e){let t=S0(e);return Kn(async()=>t)}function uj(e){return Kn(async()=>{let t=await e();return S0(()=>t.next())})}async function pj(e,t){return R2.create(e,t)}async function cj(e){return D2.create(e)}var dj="4.0.0";function ge(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var hj=cr.whereImpl,N0=class extends rc{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new jh(this,Na())}nextDataId(){return N0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,H().get("IS_NODE")&&N.warn(`
============================
Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. 
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p=t[u+1];for(let d=1;d<l+1;++d)o[u].push(d*p)}for(let u=0;u<e.length;++u){let p=e[u],d=e[u]+1;for(let c=0;c<n.length;++c){let h=n[c],m=c+t.length-1;if(m>=0){let f=o[m],g=f[f.length-1]-h[p];for(let y=p;y<d;++y)o[m].push(h[y+1]+g)}p=h[p],d=h[d]}d!==p&&(r.push([p,d]),s+=d-p)}return{outSplits:o,valueSlices:r,numValues:s}}function nq(e){let t=[];for(let n=0;n<e.length;++n){let a=e[n].length,r=v.getArrayFromDType("int32",a);t.push(r),e[n].forEach((s,i)=>r[i]=s)}return t}function zk(e,t){let n=e.slice(0,t);for(;n.length<t;)n.push(1);for(let a=t;a<e.length;a++)n[t-1]*=e[a];return n}function aq(e,t,n,a,r,s){let i=zk(t,2)[1],o=zk(s,2)[1],l=0;for(let u of n)for(let p=u[0];p<u[1];++p){for(let d=0;d<a;++d)r[l*o+d]=e[p*i+d];++l}}function rq(e,t,n,a,r){let s=t.slice();s[0]=r;let i=v.getArrayFromDType(n,v.sizeFromShape(s)),o=e.length,l=o===0?0:o/t[0];return aq(e,t,a,l,i,s),[i,s]}function hC(e,t,n,a,r,s,i,o){if(e.length===0)throw new Error("paramsNestedSplits must be non empty");if(t[0].length===0)throw new Error("Split tensors must not be scalars");let l=t[0][0]-1;if(Qj(s,i,l),a.length===0)throw new Error("params.rank must be nonzero");let u=a[0],{outSplits:p,valueSlices:d,numValues:c}=tq(s,i,e,u),h=nq(p),m=rq(n,a,r,d,c);return[h,m[0],m[1]]}var Wk=2147483647;function mC(e,t,n,a,r,s,i){if(t.length>1)throw new Error("starts must be a scalar or vector");if(r.length>1)throw new Error("limits must be a scalar or vector");if(i.length>1)throw new Error("deltas must be a scalar or vector");let o=t.length===0,l=r.length===0,u=i.length===0,p=[];o||p.push(t[0]),l||p.push(r[0]),u||p.push(i[0]);for(let g=1;g<p.length;++g)if(p[g]!==p[g-1])throw new Error("starts, limits, and deltas must have the same shape");let d=p.length===0?1:p[0],c=v.getArrayFromDType("int32",d+1);c[0]=0;for(let g=0;g<d;++g){let y=o?e[0]:e[g],b=l?a[0]:a[g],x=u?s[0]:s[g];if(x===0)throw new Error("Requires delta != 0");let w;if(x>0&&b<y||x<0&&b>y)w=0;else if(w=Math.ceil(Math.abs((b-y)/x)),w>Wk)throw new Error(`Requires ((limit - start) / delta) <= ${Wk}`);c[g+1]=c[g]+w}let h=c[d],m=v.getArrayFromDType(n,h),f=0;for(let g=0;g<d;++g){let y=c[g+1]-c[g],b=o?e[0]:e[g],x=u?s[0]:s[g];for(let w=0;w<y;++w)m[f++]=b,b+=x}return[c,m]}var Ia=N.RowPartitionType,ax=class{constructor(e,t,n,a,r,s,i,o,l,u){this.shape=e,this.shapeShape=t,this.values=n,this.valuesShape=a,this.valuesDType=r,this.defaultValue=s,this.defaultValueShape=i,this.rowPartitionValues=o,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=N.getRowPartitionTypesHelper(u),this.raggedRank=N.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===Ia.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===Ia.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){let t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case Ia.VALUE_ROWIDS:return ax.getMaxWidthValueRowID(t);case Ia.ROW_SPLITS:return ax.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${Ia[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){let t=e.length;if(t===0||t===1)return 0;let n=0;for(let a=0;a<t-1;++a){let r=e[a+1]-e[a];r>n&&(n=r)}return n}static getMaxWidthValueRowID(e){let t=e.length;if(t===0)return 0;let n=0,a=e[0],r=0;for(let s=1;s<t;++s){let i=e[s];i!==a&&(a=i,r=Math.max(s-n,r),n=s)}return Math.max(t-n,r)}tensorShapeFromTensor(e,t,n=!0){if(t.length===0){if(e[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return Vk(e,n)}calculateOutputSize(e){let t=this.valuesShape,n=this.defaultValueShape;N.validateDefaultValueShape(n,t);let a=this.tensorShapeFromTensor(this.shape,this.shapeShape),r=N.combineRaggedTensorToTensorShapes(this.raggedRank,a,t);r[0]<0&&(r[0]=e);for(let s=1;s<=this.raggedRank;++s)r[s]<0&&(r[s]=this.getMaxWidth(s));return r}calculateFirstParentOutputIndex(e,t,n){let a=Math.min(e,n),r=[],s=0;for(let i=0;i<a;++i,s+=t)r.push(s);for(let i=a;i<e;++i)r.push(-1);return v.assert(r.length===e,()=>"Final length of result must be equal to firstDimension."),r}calculateOutputIndexRowSplit(e,t,n,a){let r=e.length,s=[];for(let i=0;i<r-1;++i){let o=e[i+1]-e[i],l=Math.min(a,o),u=t[i];u===-1&&(l=0);for(let p=0;p<l;++p)s.push(u),u+=n;for(let p=0;p<o-l;++p)s.push(-1)}if(r>0&&s.length!==e[r-1])throw new Error("Invalid row split size.");return s}calculateOutputIndexValueRowID(e,t,n,a){let r=e.length,s=[];if(r===0)return[];let i=0,o=e[0];if(o>=t.length)throw new Error(`Got currentValueRowId=${o}, which is not less than ${t.length}`);let l=t[o];s.push(l);for(let u=1;u<r;++u){let p=e[u];if(p===o)l>=0&&(++i,i<a?l+=n:l=-1);else{if(i=0,o=p,p>=t.length)throw new Error(`Got nextValueRowId=${p} which is not less than ${t.length}`);l=t[p]}s.push(l)}if(s.length!==e.length)throw new Error("Invalid row ids.");return s}calculateOutputIndex(e,t,n,a){let r=this.getRowPartitionTensor(e),s=this.getRowPartitionTypeByDimension(e);switch(s){case Ia.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(r,t,n,a);case Ia.ROW_SPLITS:if(r.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${r.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(r,t,n,a);default:throw new Error(`Unsupported partition type: ${Ia[s]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let t=this.rowPartitionTypes[0];switch(t){case Ia.FIRST_DIM_SIZE:return e[0];case Ia.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case Ia.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${Ia[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");let e=this.getFirstDimensionSize(),t=this.calculateOutputSize(e),n=new Array(this.raggedRank+1);n[n.length-1]=1;for(let s=n.length-2;s>=0;--s)n[s]=n[s+1]*t[s+1];let a=Vk(t,!1),r=v.getArrayFromDType(this.valuesDType,v.sizeFromShape(a));if(n[0]*t[0]>0){let s=this.calculateFirstParentOutputIndex(e,n[0],t[0]);for(let i=1;i<=this.raggedRank;++i)s=this.calculateOutputIndex(i-1,s,n[i],t[i]);this.setOutput(this.raggedRank,s,r,a)}return[a,r]}setOutput(e,t,n,a){if(n.length===0)return;let r=this.values,s=n,i=a.slice();i=i.slice(e+1);let o=v.sizeFromShape(i),l=t.length,u=this.defaultValue;if(u.length!==o&&u.length!==1){let h=this.defaultValueShape;P(()=>{let m=W(u,h);u=Ks(m,i).dataSync()})}let p=0,d=0,c=0;for(let h=0;h<=l;++h){let m=h<l?t[h]:-1;if(m===c){++c;continue}if(d<c){let f=r.subarray(p*o),g=s.subarray(d*o),y=(c-d)*o;Bk(g,f,y)}if(h>=l){let f=n.length;m=Math.floor(f/o)}if(m>c)if(this.defaultValue.length===1)s.subarray(c*o,m*o).fill(this.defaultValue[0]),c=m;else for(;m>c;){let f=s.slice(c*o);Bk(f,u,o),++c}m<0?(p=h+1,d=c):(p=h,d=c,c=d+1)}}};function Bk(e,t,n){for(let a=0;a<n;a++)e[a]=t[a]}function Vk(e,t){let n=[];for(let a of e){if(a<0){if(!t)throw new Error(`Dimension ${a} must be >= 0`);if(a<-1)throw new Error(`Dimension ${a} must be >= -1`);a=-1}n.push(a)}return n}function fC(e,t,n,a,r,s,i,o,l,u){return new ax(e,t,n,a,r,s,i,o,l,u).compute()}function F0(e,t,n,a){let r=e===t,s=e<t&&n<0,i=t<e&&n>1;if(r||s||i)return v.makeZerosTypedArray(0,a);let o=Math.abs(Math.ceil((t-e)/n)),l=v.makeZerosTypedArray(o,a);t<e&&n===1&&(n=-1),l[0]=e;for(let u=1;u<l.length;u++)l[u]=l[u-1]+n;return l}var gC=ks(e=>1/Math.sqrt(e)),sq=Pu(to,gC),iq={kernelName:to,backendName:"cpu",kernelFunc:sq};function Yo(e,t,n,a,r,s,i,o,l,u){let p=[a/r,r],d=e.values,c=t.values;if(a===0)return Pe(n,t.dtype);let h=Pe(p,t.dtype);typeof l=="string"||typeof l=="number"?h.values.fill(l):typeof l=="boolean"&&h.values.fill(+l);for(let m=0;m<s;m++){let f=[],g=0;for(let y=0;y<i;y++){let b=d[m*i+y];f.push(b),g+=b*o[y]}if(g<0||g>=a/r)throw new Error(`Invalid indices: ${f} does not index into ${n}`);for(let y=0;y<r;y++)u?h.values[g*r+y]+=c[m*r+y]:h.values[g*r+y]=t.rank===0?c[0]:c[m*r+y]}return h}var oq=ks(e=>1/(1+Math.exp(-e))),yC=rt(ao,e=>1/(1+Math.exp(-e))),lq={kernelName:ao,backendName:"cpu",kernelFunc:yC};function Oh(e,t,n,a,r){let s=jt.isSliceContinous(a,t,n),i=v.sizeFromShape(n),o=v.computeStrides(a);if(s){let d=jt.computeFlatOffset(t,o);return r==="string"?e.slice(d,d+i):e.subarray(d,d+i)}let l=r==="string"?N.fromUint8ToStringArray(e):e,u=Pe(a,r,l),p=Pe(n,r);for(let d=0;d<p.size;++d){let c=p.indexToLoc(d),h=c.map((m,f)=>m+t[f]);p.set(u.get(...h),...c)}return r==="string"?N.fromStringArrayToUint8(p.values):p.values}function ui(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a;ge(r,"slice");let[o,l]=jt.parseSliceParams(r,s,i);jt.assertParamsValid(r,o,l);let u=n.data.get(r.dataId).values,p=Oh(u,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}var uq={kernelName:du,backendName:"cpu",kernelFunc:ui};function bC(e,t,n,a,r,s,i){let o=t[0],l=s[0],u=new Array(l),p=new Array(o),d=t[1];if(l===0){if(o!==0)throw new Error(N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(o));let g=v.getArrayFromDType(n,0),y=v.getArrayFromDType(r,0);return[g,[0,d],y,u,p]}let c=!0,h=0,m=new Array(l).fill(0);for(let g=0;g<o;++g){let y=e[g*d];if(y<0)throw new Error(N.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,y));if(y>=l)throw new Error(N.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,y,l));++m[y],c=c&&y>=h,h=y}let f=!0;for(let g=0;g<l;++g){let y=m[g]===0;u[g]=y,f=f&&!y,m[g]=Math.max(m[g],1),g>0&&(m[g]+=m[g-1])}if(f&&c){let g=e,y=a;for(let b=0;b<o;++b)p[b]=b;return[g,[o,d],y,u,p]}else{let g=m[l-1],y=v.getArrayFromDType(n,g*d),b=v.getArrayFromDType(r,g),x=new Array(l).fill(0);for(let w=0;w<o;++w){let I=e[w*d],T=x[I],C=(I===0?0:m[I-1])+T;x[I]++;for(let E=0;E<d;++E)y[C*d+E]=e[w*d+E];b[C]=a[w],p[w]=C}for(let w=0;w<l;++w)if(x[w]===0){let I=w===0?0:m[w-1];y[I*d+0]=w;for(let T=1;T<d;++T)y[I*d+T]=0;b[I]=i}return[y,[g,d],b,u,p]}}function xC(e,t,n,a,r){let s=v.sizeFromShape(a),i=t[0],o=r.length,l=[],u=1,p=-1;for(let f=0;f<o;++f){let g=r[f];if(g===-1){if(p!==-1)throw new Error(N.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(p,f));p=f,l.push(1)}else{if(g<0)throw new Error(N.getSparseReshapeNegativeOutputDimErrorMessage(f,g));u*=g,l.push(g)}}if(p!==-1){if(u<=0)throw new Error(N.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());let f=Math.trunc(s/u);if(u*f!==s)throw new Error(N.getSparseReshapeInputOutputMultipleErrorMessage(a,l));l[p]=f}if(v.sizeFromShape(l)!==s)throw new Error(N.getSparseReshapeInputOutputMismatchErrorMessage(a,l));let d=a.length,c=[];if(d>0){c[d-1]=1;for(let f=d-2;f>=0;--f)c[f]=c[f+1]*a[f+1]}let h=[];if(o>0){h[o-1]=1;for(let f=o-2;f>=0;--f)h[f]=h[f+1]*l[f+1]}let m=v.getArrayFromDType(n,i*o);for(let f=0;f<i;++f){let g=0;for(let y=0;y<d;++y)g+=e[f*d+y]*c[y];for(let y=0;y<o;++y)m[f*o+y]=Math.trunc(g/h[y]),g%=h[y]}return[m,[i,o],l]}function D0(e,t,n,a,r,s=!1,i=0){let o=a.length,l=[t[0],e.length/t[0]],u=l[1],p=o>0?r[o-1]+1:0;if(p<0)throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let d=t.slice();d[0]=p;let c=d.reduce((b,x)=>b*x,1),h=v.getArrayFromDType(n,c);if(o===0)return p>0&&h.fill(i),[h,d];if(p<=0)throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=0,f=1,g=0,y=r[m];for(;;){let b=0;if(f<o){if(b=r[f],y===b){++f;continue}if(y>=b)throw new Error(N.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(y<0||y>=p)throw new Error(N.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(y,p));y>g&&h.fill(i,g*u,y*u);for(let x=m;x<f;++x){let w=a[x];if(w<0||w>=l[0])throw new Error(N.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(x,a[x],l[0]));for(let I=0;I<u;I++)h[y*u+I]+=e[w*u+I]}if(s)for(let x=0;x<u;x++)h[y*u+x]/=f-m;if(m=f,++f,g=y+1,y=b,f>o)break}return g<p&&h.fill(i,g*u,p*u),[h,d]}var pq=ks(e=>Math.sqrt(e)),cq=rt(ro,e=>Math.sqrt(e)),dq={kernelName:ro,backendName:"cpu",kernelFunc:cq},vC=Vt((e,t)=>{let n=e-t;return n*n}),hq=nn(oo,vC),mq={kernelName:oo,backendName:"cpu",kernelFunc:hq};function wC(e,t,n,a){let r=Pe(e,t.dtype);for(let s=0;s<r.size;s++){let i=r.indexToLoc(s),o=new Array(i.length);for(let l=0;l<o.length;l++)o[l]=i[l]*n[l]+a[l];r.set(t.get(...o),...i)}return r}var fq=class{constructor(e,t,n,a,r,s){this.separator=v.encodeString(e),this.nGramWidths=t,this.leftPad=v.encodeString(n),this.rightPad=v.encodeString(a),this.padWidth=r,this.preserveShort=s}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,a,r,s){for(let i=0;i<r;++i){let o=this.getPadWidth(s),l=Math.max(0,o-i),u=Math.max(0,o-(r-(i+1))),p=s-(l+u),d=t+(l>0?0:i-o),c=0;c+=l*this.leftPad.length;for(let y=0;y<p;++y)c+=e[d+y].length;c+=u*this.rightPad.length;let h=l+u+p-1;c+=h*this.separator.length,n[a+i]=new Uint8Array(c);let m=n[a+i],f=0,g=y=>y.forEach(b=>m[f++]=b);for(let y=0;y<l;++y)g(this.leftPad),g(this.separator);for(let y=0;y<p-1;++y)g(e[d+y]),g(this.separator);if(p>0){g(e[d+p-1]);for(let y=0;y<u;++y)g(this.separator),g(this.rightPad)}else{for(let y=0;y<u-1;++y)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let n=e.length,a=t.length;if(a>0){let o=t[0];if(o!==0)throw new Error(`First split value must be 0, got ${o}`);for(let l=1;l<a;++l){let u=t[l]>=o;if(u=u&&t[l]<=n,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${o}, ${n}]`);o=t[l]}if(o!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${o}`)}let r=a-1,s=v.getArrayFromDType("int32",a);if(n===0||a===0){let o=new Array(n);for(let l=0;l<=r;++l)s[l]=0;return[o,s]}s[0]=0;for(let o=1;o<=r;++o){let l=t[o]-t[o-1],u=0;this.nGramWidths.forEach(p=>{u+=this.getNumNGrams(l,p)}),this.preserveShort&&l>0&&u===0&&(u=1),s[o]=s[o-1]+u}let i=new Array(s[r]);for(let o=0;o<r;++o){let l=t[o],u=s[o];if(this.nGramWidths.forEach(p=>{let d=t[o+1]-t[o],c=this.getNumNGrams(d,p);this.createNGrams(e,l,i,u,c,p),u+=c}),this.preserveShort&&u===s[o]){let p=t[o+1]-t[o];if(p===0)continue;let d=p+2*this.padWidth,c=1;this.createNGrams(e,l,i,u,c,d)}}return[i,s]}};function R0(e,t,n,a,r,s,i,o){return new fq(n,a,r,s,i,o).compute(e,t)}function gq(e,t,n,a){if(!e.length)return;if(t.length===0){for(let s=0;s<e.length;++s)a.push(e.subarray(s,s+1));return}if(t.length===1){let s=t[0],i=e.indexOf(s);for(;i!==-1;){let o=e.subarray(0,i);(!n||o.length!==0)&&a.push(o),e=e.subarray(i+1),i=e.indexOf(s)}(!n||e.length!==0)&&a.push(e);return}let r=0;for(let s=0;s<e.length+1;s++)if(s===e.length||t.indexOf(e[s])!==-1){let i=e.subarray(r,s);(!n||i.length!==0)&&a.push(i),r=s+1}}function M0(e,t,n){let a=e.length,r=[],s=0,i=0,o=new Array(a);for(let c=0;c<a;++c){let h=r.length;gq(e[c],t,n,r);let m=r.length-h;o[c]=m,s+=m,i=Math.max(i,m)}let l=v.getArrayFromDType("int32",s*2),u=new Array(s),p=[a,i],d=0;for(let c=0;c<a;++c)for(let h=0;h<o[c];++h)l[d*2]=c,l[d*2+1]=h,u[d]=r[d],++d;return[l,u,p]}function P0(e,t){let n=v.getArrayFromDType("int32",e.length);for(let a=0;a<e.length;++a)n[a]=v.fingerPrint64(e[a]).modulo(t).getLowBitsUnsigned();return n}var kC=Vt((e,t)=>e-t),yq=C0((e,t,n,a)=>({real:e-n,imag:t-a})),O0=nn(lo,kC,yq),bq={kernelName:lo,backendName:"cpu",kernelFunc:O0};function IC(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let a=Pe(n,e.dtype);for(let r=0;r<a.values.length;++r){let s=a.indexToLoc(r),i=new Array(e.rank);for(let l=0;l<i.length;l++)i[l]=s[l]%e.shape[l];let o=e.locToIndex(i);a.values[r]=e.values[o]}return a}var Ap=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function SC(e,t,n=0,a=e.length-1){for(;a>n;){if(a-n>600){let o=a-n+1,l=t-n+1,u=Math.log(o),p=.5*Math.exp(2*u/3),d=.5*Math.sqrt(u*p*(o-p)/o)*Math.sign(l-o/2),c=Math.max(n,Math.floor(t-l*p/o+d)),h=Math.min(a,Math.floor(t+(o-l)*p/o+d));SC(e,t,c,h)}let r=e[t],s=n,i=a;for(v.swap(e,n,t),Ap(e[a],r)>0&&v.swap(e,n,a);s<i;){for(v.swap(e,s,i),s++,i--;Ap(e[s],r)<0;)s=s+1;for(;Ap(e[i],r)>0;)i=i-1}Ap(e[n],r)===0?v.swap(e,n,i):(i=i+1,v.swap(e,i,a)),i<=t&&(n=i+1),t<=i&&(a=i-1)}}function TC(e,t,n,a,r){let s=t[t.length-1],[i,o]=[e.length/s,s],l=v.getTypedArrayFromDType(n,i*a),u=v.getTypedArrayFromDType("int32",i*a);for(let d=0;d<i;d++){let c=d*o,h=e.subarray(c,c+o),m=new Array(h.length);h.forEach((b,x)=>m[x]={value:b,index:x}),a<m.length&&(SC(m,a),m=m.slice(0,a)),r&&m.sort(Ap);let f=d*a,g=l.subarray(f,f+a),y=u.subarray(f,f+a);for(let b=0;b<a;b++)g[b]=m[b].value,y[b]=m[b].index}let p=t.slice();return p[p.length-1]=a,[Pe(p,n,l),Pe(p,"int32",u)]}function NC(e,t,n,a){let r=v.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let m=0;m<r;m++)s[0]*=n[m];s[1]=n[r];for(let m=r+1;m<n.length;m++)s[2]*=n[m];let i={},o=new Int32Array(n[r]),l=new Ht(s,a,e),u=[],p=s[0]===1&&s[2]===1;for(let m=0;m<n[r];m++){let f;if(p)f=e[m].toString();else{let g=[];for(let y=0;y<s[0];y++)for(let b=0;b<s[2];b++)g.push(l.get(y,m,b));f=g.join(",")}if(i[f]!==void 0)o[m]=i[f];else{let g=Object.keys(i).length;i[f]=g,o[m]=g,u.push(m)}}let d=s.slice();d[1]=Object.keys(i).length;let c=new Ht(d,a);u.forEach((m,f)=>{for(let g=0;g<s[0];g++)for(let y=0;y<s[2];y++)c.set(l.get(g,m,y),g,f,y)});let h=n.slice();return h[r]=d[1],{outputValues:c.values,outputShape:h,indices:o}}Em("cpu",()=>new N0,1);var CC=rt(Ci,e=>e>=0?e:Math.exp(e)-1),xq={kernelName:Ci,backendName:"cpu",kernelFunc:CC};function _C(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a;ge([r],"leakyRelu");let i=v.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,l=v.getTypedArrayFromDType("float32",i);for(let u=0;u<o.length;u++)l[u]=o[u]<0?s*o[u]:o[u];return n.makeTensorInfo(r.shape,"float32",l)}var vq={kernelName:Ri,backendName:"cpu",kernelFunc:_C},wq=Vt((e,t)=>e<0?t*e:e);function EC(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t;ge([a,r],"prelu");let s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,[o,l]=wq(a.shape,r.shape,s,i,"float32");return n.makeTensorInfo(l,"float32",o)}var kq={kernelName:qi,backendName:"cpu",kernelFunc:EC},$C=rt(Xi,e=>Math.max(0,e)),Iq={kernelName:Xi,backendName:"cpu",kernelFunc:$C},AC=rt(Ji,e=>Math.min(Math.max(0,e),6)),Sq={kernelName:Ji,backendName:"cpu",kernelFunc:AC};function Lh(e,t,n,a,r){if(n==="linear")return lr({inputs:{x:t},backend:e});if(n==="relu")return $C({inputs:{x:t},backend:e});if(n==="elu")return CC({inputs:{x:t},backend:e});if(n==="relu6")return AC({inputs:{x:t},backend:e});if(n==="prelu")return 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Nq={kernelName:yi,backendName:"cpu",kernelFunc:FC};function Cq(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c,h,m,f=[];c=FC({inputs:{a:r,b:s},attrs:{transposeA:l,transposeB:u},backend:n}),i&&(h=gl({inputs:{a:c,b:i},backend:n}),f.push(c),c=h),p&&(m=Lh(n,c,p,o,d),f.push(c),c=m);for(let g of f)n.disposeIntermediateTensorInfo(g);return c}var _q={kernelName:Js,backendName:"cpu",kernelFunc:Cq},Eq=rt(kl,e=>Math.acos(e)),$q={kernelName:kl,backendName:"cpu",kernelFunc:Eq},Aq=rt(Il,e=>Math.acosh(e)),Fq={kernelName:Il,backendName:"cpu",kernelFunc:Aq};function Dq(e){let{inputs:t,backend:n}=e,a=t;ge(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=Pe(a[0].shape,a[0].dtype),i=s.values;for(let o=0;o<a.length;o++){let l=r[o];for(let u=0;u<i.length;u++)i[u]+=l[u]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var Rq={kernelName:mi,backendName:"cpu",kernelFunc:Dq};function 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i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],N.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[p,d]=N.computeOutAndReduceShapes(l.shape,i),c=v.sizeFromShape(p),h=v.makeZerosTypedArray(c,"int32"),m=v.sizeFromShape(d),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],x=0;for(let w=0;w<m;++w){let I=f[y+w];I>b&&(b=I,x=w)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(p,"int32",h)}var Wq={kernelName:fi,backendName:"cpu",kernelFunc:zq};function Bq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ge(r,"argMin");let i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],N.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[p,d]=N.computeOutAndReduceShapes(l.shape,i),c=v.sizeFromShape(p),h=v.makeZerosTypedArray(c,"int32"),m=v.sizeFromShape(d),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],x=0;for(let w=0;w<m;++w){let I=f[y+w];I<b&&(b=I,x=w)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(p,"int32",h)}var Vq={kernelName:sc,backendName:"cpu",kernelFunc:Bq},Uq=rt(Nl,e=>Math.asin(e)),Gq={kernelName:Nl,backendName:"cpu",kernelFunc:Uq},Hq=rt(Cl,e=>Math.asinh(e)),jq={kernelName:Cl,backendName:"cpu",kernelFunc:Hq},qq=rt(_l,e=>Math.atan(e)),Kq={kernelName:_l,backendName:"cpu",kernelFunc:qq},Xq=Vt((e,t)=>Math.atan2(e,t)),Yq=nn($l,Xq),Zq={kernelName:$l,backendName:"cpu",kernelFunc:Yq},Jq=rt(El,e=>Math.atanh(e)),Qq={kernelName:El,backendName:"cpu",kernelFunc:Jq};function L0(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,d=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Pe(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],b=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let w=0;w<r.batchSize;++w){let I=w*y,T=w*a[0];for(let C=0;C<r.inChannels;++C)for(let E=0;E<r.outHeight;++E){let $=E*i-c,R=Math.max(0,$),F=Math.min(r.inHeight,p+$),S=I+E*b;for(let M=0;M<r.outWidth;++M){let B=M*o-h,U=Math.max(0,B),G=Math.min(r.inWidth,d+B),q=m,K=0,Z=0;for(let ee=R;ee<F;ee+=l){let ae=T+ee*a[1];for(let te=U;te<G;te+=u){let ie=ae+te*a[2],oe=e[ie+C];s==="max"&&oe>q?q=oe:s==="avg"&&(K+=oe,Z++)}if(isNaN(q))break}let J=S+M*x+C;g[J]=s==="avg"?K/Z:q}}}return f}function DC(e,t,n,a,r=!1,s=!1){let i=Pe(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,u=a.dilationHeight,p=a.dilationWidth,d=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=Pe(t,n,e);for(let g=0;g<a.batchSize;++g)for(let y=0;y<a.inChannels;++y)for(let b=0;b<a.outHeight;++b){let x=b*o-h,w=x;for(;w<0;)w+=u;let I=Math.min(a.inHeight,d+x);for(let T=0;T<a.outWidth;++T){let C=T*l-m,E=C;for(;E<0;)E+=p;let $=Math.min(a.inWidth,c+C),R=Number.NEGATIVE_INFINITY,F=-1;for(let S=w;S<I;S+=u){let M=S-x;for(let B=E;B<$;B+=p){let U=B-C,G=f.get(g,S,B,y);G>R&&(R=G,r?F=s?((g*a.inHeight+S)*a.inWidth+B)*a.inChannels+y:(S*a.inWidth+B)*a.inChannels+y:F=M*c+U)}}i.set(F,g,b,T,y)}}return i}function RC(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,d=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Pe(r.outShape,n),w=x.values,I=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[3]*r.outShape[4],E=r.outShape[4];for(let $=0;$<r.batchSize;++$){let R=$*I,F=$*a[0];for(let S=0;S<r.inChannels;++S)for(let M=0;M<r.outDepth;++M){let B=M*i-f,U=B;for(;U<0;)U+=u;let G=Math.min(r.inDepth,c+B),q=R+M*T;for(let K=0;K<r.outHeight;++K){let Z=K*o-g,J=Z;for(;J<0;)J+=p;let ee=Math.min(r.inHeight,h+Z),ae=q+K*C;for(let te=0;te<r.outWidth;++te){let ie=te*l-y,oe=ie;for(;oe<0;)oe+=d;let ye=Math.min(r.inWidth,m+ie),ue=ae+te*E,be=b,ke=0,Se=0;for(let Ve=U;Ve<G;Ve+=u){let nt=F+Ve*a[1];for(let st=J;st<ee;st+=p){let Je=nt+st*a[2];for(let at=oe;at<ye;at+=d){let ze=Je+at*a[3],dt=e[ze+S];if(s==="max"&&dt>be?be=dt:s==="avg"&&(ke+=dt,Se++),isNaN(be))break}if(isNaN(be))break}if(isNaN(be))break}let Le=ue+S;w[Le]=s==="avg"?ke/Se:be}}}}return x}function e5(e,t){let n=Pe(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,d=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let b=y*a-c,x=b;for(;x<0;)x+=i;let w=Math.min(t.inDepth,u+b);for(let I=0;I<t.outHeight;++I){let T=I*r-h,C=T;for(;C<0;)C+=o;let E=Math.min(t.inHeight,p+T);for(let $=0;$<t.outWidth;++$){let R=$*s-m,F=R;for(;F<0;)F+=l;let S=Math.min(t.inWidth,d+R),M=Number.NEGATIVE_INFINITY,B=-1;for(let U=x;U<w;U+=i){let G=U-b;for(let q=C;q<E;q+=o){let K=q-T;for(let Z=F;Z<S;Z+=l){let J=Z-R,ee=e.get(f,U,q,Z,g);ee>=M&&(M=ee,B=G*p*d+K*p+J)}}}n.set(B,f,y,I,$,g)}}}return n}function t5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ge(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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p=N.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,y=p.dilationDepth,b=p.dilationHeight,x=p.dilationWidth,w=p.effectiveFilterDepth,I=p.effectiveFilterHeight,T=p.effectiveFilterWidth,C=w-1-p.padInfo.front,E=T-1-p.padInfo.left,$=I-1-p.padInfo.top,R=Pe(s.shape,"float32"),F=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M<p.batchSize;++M)for(let B=0;B<p.inChannels;++B)for(let U=0;U<p.inDepth;++U)for(let G=0;G<p.inHeight;++G)for(let q=0;q<p.inWidth;++q){let K=U-C,Z=G-$,J=q-E,ee=0;for(let ae=0;ae<w;ae+=y){let te=(K+ae)/d;if(!(te<0||te>=p.outDepth||Math.floor(te)!==te))for(let ie=0;ie<I;ie+=b){let oe=(Z+ie)/c;if(!(oe<0||oe>=p.outHeight||Math.floor(oe)!==oe))for(let ye=0;ye<T;ye+=x){let ue=(J+ye)/h;if(ue<0||ue>=p.outWidth||Math.floor(ue)!==ue)continue;let be=S.get(M,te,oe,ue,B);ee+=be}}}R.set(ee*F,M,U,G,q,B)}return n.makeTensorInfo(R.shape,R.dtype,R.values)}var i5={kernelName:Yh,backendName:"cpu",kernelFunc:s5};function o5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,b=p.effectiveFilterWidth,x=b-1-p.padInfo.left,w=y-1-p.padInfo.top,I=Pe(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,E=Pe(r.shape,"float32",C);for(let $=0;$<p.batchSize;++$)for(let R=0;R<p.inChannels;++R)for(let F=0;F<p.inHeight;++F)for(let S=0;S<p.inWidth;++S){let M=F-w,B=S-x,U=0;for(let G=0;G<y;G+=f){let q=(M+G)/d;if(!(q<0||q>=p.outHeight||Math.floor(q)!==q))for(let K=0;K<b;K+=g){let Z=(B+K)/c;if(Z<0||Z>=p.outWidth||Math.floor(Z)!==Z)continue;let J=E.get($,q,Z,R);U+=J}}I.set(U*T,$,F,S,R)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var l5={kernelName:Xh,backendName:"cpu",kernelFunc:o5};function 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n.makeTensorInfo(r.shape,r.dtype,f)}var p5={kernelName:Ai,backendName:"cpu",kernelFunc:u5};function c5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;ge([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=ft({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:p}}),g=ui({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var d5={kernelName:Al,backendName:"cpu",kernelFunc:c5};function h5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=_0(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var m5={kernelName:Zh,backendName:"cpu",kernelFunc:h5};function f5(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var g5={kernelName:Jh,backendName:"cpu",kernelFunc:f5},y5=rt(ds,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),b5={kernelName:ds,backendName:"cpu",kernelFunc:y5},x5=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let p=o[u],d=l[u];a[u]=Math.hypot(p,d)}return n.makeOutput(a,t.shape,"float32")},v5={kernelName:oc,backendName:"cpu",kernelFunc:x5};function yl(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var w5={kernelName:cm,backendName:"cpu",kernelFunc:yl};function bl(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(f=>f.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return lr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(w=>li({inputs:{input:w},backend:n})),g=l.map(w=>yl({inputs:{input:w},backend:n})),y=bl({inputs:f,backend:n,attrs:{axis:s}}),b=bl({inputs:g,backend:n,attrs:{axis:s}}),x=Yn({inputs:{real:y,imag:b},backend:n});return 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S5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a;ge([r,s],"conv2dBackpropFilter");let d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),{strideHeight:h,strideWidth:m,filterHeight:f,filterWidth:g}=c,y=c.dataFormat==="channelsLast",b=new Ht(c.filterShape,"float32"),x=c.padInfo.left,w=c.padInfo.top,I=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=new Ht(r.shape,r.dtype,I),E=new Ht(s.shape,s.dtype,T);for(let $=0;$<f;++$){let R=Math.max(0,Math.ceil((w-$)/h)),F=Math.min(c.outHeight,(c.inHeight+w-$)/h);for(let S=0;S<g;++S){let M=Math.max(0,Math.ceil((x-S)/m)),B=Math.min(c.outWidth,(c.inWidth+x-S)/m);for(let U=0;U<c.inChannels;++U)for(let G=0;G<c.outChannels;++G){let q=0;for(let K=0;K<c.batchSize;++K)for(let Z=R;Z<F;++Z){let J=$+Z*h-w;for(let ee=M;ee<B;++ee){let ae=S+ee*m-x;y?q+=C.get(K,J,ae,U)*E.get(K,Z,ee,G):q+=C.get(K,U,J,ae)*E.get(K,G,Z,ee)}}b.set(q,$,S,U,G)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var T5={kernelName:em,backendName:"cpu",kernelFunc:S5};function N5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a;ge([r,s],"conv2dBackpropInput");let d=v.computeStrides(s.shape),c=v.computeStrides(r.shape),h=N.convertConv2DDataFormat(u),m=N.computeConv2DInfo(i,s.shape,o,1,l,p,!1,h),f=new Ht(m.inShape,"float32"),g=f.values,y=n.data.get(r.dataId).values,b=n.data.get(s.dataId).values,[x,w,I]=d,{batchSize:T,filterHeight:C,filterWidth:E,inChannels:$,inHeight:R,inWidth:F,outChannels:S,outHeight:M,outWidth:B,strideHeight:U,strideWidth:G}=m;h=m.dataFormat;let q=C-1-m.padInfo.top,K=E-1-m.padInfo.left,Z=h==="channelsLast",J=f.strides[0],ee=Z?f.strides[1]:f.strides[2],ae=Z?f.strides[2]:1,te=Z?1:f.strides[1],ie=c[0],oe=Z?c[1]:c[2],ye=Z?c[2]:1,ue=Z?1:c[1];for(let be=0;be<T;++be)for(let ke=0;ke<$;++ke)for(let Se=0;Se<R;++Se){let 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u=v.computeStrides(r.shape),p=v.computeStrides(s.shape),d=N.computeConv3DInfo(r.shape,l,i,1,o),c=d.strideDepth,h=d.strideHeight,m=d.strideWidth,f=d.filterDepth,g=d.filterHeight,y=d.filterWidth,b=new Ht(d.filterShape,"float32"),x=b.values,[w,I,T,C]=b.strides,E=n.data.get(s.dataId).values,[$,R,F,S]=p,M=n.data.get(r.dataId).values,[B,U,G,q]=u,K=d.padInfo.front,Z=d.padInfo.left,J=d.padInfo.top;for(let ee=0;ee<f;++ee){let ae=Math.max(0,Math.ceil((K-ee)/c)),te=Math.min(d.outDepth,(d.inDepth+K-ee)/c),ie=ee*w;for(let oe=0;oe<g;++oe){let ye=Math.max(0,Math.ceil((J-oe)/h)),ue=Math.min(d.outHeight,(d.inHeight+J-oe)/h),be=oe*I+ie;for(let ke=0;ke<y;++ke){let Se=Math.max(0,Math.ceil((Z-ke)/m)),Le=Math.min(d.outWidth,(d.inWidth+Z-ke)/m),Ve=ke*T+be;for(let nt=0;nt<d.inChannels;++nt){let st=nt*C+Ve;for(let Je=0;Je<d.outChannels;++Je){let at=0;for(let ze=0;ze<d.batchSize;++ze){let dt=ze*B,Hn=ze*$;for(let Mt=ae;Mt<te;++Mt){let sa=(ee+Mt*c-K)*U+dt,sn=Mt*R+Hn;for(let Fn=ye;Fn<ue;++Fn){let ia=(oe+Fn*h-J)*G+sa,Dn=Fn*F+sn;for(let it=Se;it<Le;++it){let Rn=(ke+it*m-Z)*q+ia,jn=it*S+Dn;at+=M[Rn+nt]*E[jn+Je]}}}}x[st+Je]=at}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var A5={kernelName:tm,backendName:"cpu",kernelFunc:$5};function F5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;ge([r],"conv3dBackpropInputV2");let u=v.computeStrides(r.shape),p=v.computeStrides(s.shape),d=N.computeConv3DInfo(l,s.shape,o,1,i),c=new Ht(d.inShape,"float32"),h=c.values,[m,f,g,y]=c.strides,b=n.data.get(r.dataId).values,[x,w,I,T]=u,C=n.data.get(s.dataId).values,[E,$,R,F]=p,{batchSize:S,filterDepth:M,filterHeight:B,filterWidth:U,inChannels:G,inDepth:q,inHeight:K,inWidth:Z,outChannels:J,outDepth:ee,outHeight:ae,outWidth:te,strideDepth:ie,strideHeight:oe,strideWidth:ye}=d,ue=M-1-d.padInfo.front,be=B-1-d.padInfo.top,ke=U-1-d.padInfo.left;for(let Se=0;Se<S;++Se)for(let Le=0;Le<G;++Le)for(let Ve=0;Ve<q;++Ve){let nt=Ve-ue,st=Math.max(0,Math.ceil(nt/ie)),Je=Math.min(ee,(M+nt)/ie);for(let at=0;at<K;++at){let ze=at-be,dt=Math.max(0,Math.ceil(ze/oe)),Hn=Math.min(ae,(B+ze)/oe);for(let Mt=0;Mt<Z;++Mt){let sa=Mt-ke,sn=Math.max(0,Math.ceil(sa/ye)),Fn=Math.min(te,(U+sa)/ye),ia=0;for(let Dn=st;Dn<Je;++Dn){let it=Dn*ie-nt;for(let Rn=dt;Rn<Hn;++Rn){let jn=Rn*oe-ze;for(let gr=sn;gr<Fn;++gr){let Mo=gr*ye-sa,Ja=x*Se+w*Dn+I*Rn+T*gr,mp=E*(M-1-it)+$*(B-1-jn)+R*(U-1-Mo)+F*Le;for(let wa=0;wa<J;++wa){let Po=b[Ja+wa],Xt=C[mp+wa];ia+=Po*Xt}}}}h[m*Se+f*Ve+g*at+y*Mt+Le]=ia}}}return n.makeTensorInfo(c.shape,c.dtype,c.values)}var D5={kernelName:nm,backendName:"cpu",kernelFunc:F5},R5=rt(ki,e=>Math.cos(e)),M5={kernelName:ki,backendName:"cpu",kernelFunc:R5},P5=rt(Ii,e=>Math.cosh(e)),O5={kernelName:Ii,backendName:"cpu",kernelFunc:P5};function L5(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,y=Pe([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,w=n.data.get(r.dataId).values,I=v.computeStrides(r.shape),T=v.computeStrides(y.shape);for(let C=0;C<m;C++){let E=C*4,$=b[E],R=b[E+1],F=b[E+2],S=b[E+3],M=x[C];if(M>=p)continue;let B=f>1?(F-$)*(d-1)/(f-1):0,U=g>1?(S-R)*(c-1)/(g-1):0;for(let G=0;G<f;G++){let q=f>1?$*(d-1)+G*B:.5*($+F)*(d-1);if(q<0||q>d-1){for(let K=0;K<g;K++)for(let Z=0;Z<h;Z++){let J=Z+K*T[2]+G*T[1]+C*T[0];y.values[J]=u}continue}if(l==="bilinear"){let K=Math.floor(q),Z=Math.ceil(q),J=q-K;for(let ee=0;ee<g;ee++){let ae=g>1?R*(c-1)+ee*U:.5*(R+S)*(c-1);if(ae<0||ae>c-1){for(let ye=0;ye<h;ye++){let ue=ye+ee*T[2]+G*T[1]+C*T[0];y.values[ue]=u}continue}let te=Math.floor(ae),ie=Math.ceil(ae),oe=ae-te;for(let ye=0;ye<h;ye++){let ue=ye+te*I[2]+K*I[1]+M*I[0],be=w[ue];ue=ye+ie*I[2]+K*I[1]+M*I[0];let ke=w[ue];ue=ye+te*I[2]+Z*I[1]+M*I[0];let Se=w[ue];ue=ye+ie*I[2]+Z*I[1]+M*I[0];let Le=w[ue],Ve=be+(ke-be)*oe,nt=Se+(Le-Se)*oe;ue=ye+ee*T[2]+G*T[1]+C*T[0],y.values[ue]=Ve+(nt-Ve)*J}}}else for(let K=0;K<g;++K){let Z=g>1?R*(c-1)+K*U:.5*(R+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;ae<h;ae++){let te=ae+K*T[2]+G*T[1]+C*T[0];y.values[te]=u}continue}let J=Math.round(Z),ee=Math.round(q);for(let ae=0;ae<h;ae++){let te=ae+J*I[2]+ee*I[1]+M*I[0],ie=ae+K*T[2]+G*T[1]+C*T[0];y.values[ie]=w[te]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var z5={kernelName:Rl,backendName:"cpu",kernelFunc:L5};function W5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;ge(r,"cumprod");let l=N.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=Vn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=N.getInnerMostAxes(1,r.shape.length)[0];if(p!==u.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${p}`);let d=ma(u.dtype,"int32"),c=v.makeOnesTypedArray(v.sizeFromShape(u.shape),d),h=n.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let x=f(y,b);if(b===0)c[x]=i?1:h[x];else{let w=f(y,b-1);c[x]=i?h[w]*c[w]:h[x]*c[w]}}let g=n.makeTensorInfo(u.shape,d,c);if(l!=null){let y=N.getUndoAxesPermutation(l),b=Vn({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),b}return g}var B5={kernelName:Dl,backendName:"cpu",kernelFunc:W5};function V5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;ge(r,"cumsum");let l=N.getAxesPermutation([s],r.shape.length),u=r;l!=null&&(u=Vn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=N.getInnerMostAxes(1,r.shape.length)[0];if(p!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${p}`);let d=ma(u.dtype,"int32"),c=v.makeZerosTypedArray(v.sizeFromShape(u.shape),d),h=n.data.get(u.dataId).values,m=u.shape[u.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let x=f(y,b);if(b===0)c[x]=i?0:h[x];else{let w=f(y,b-1);c[x]=i?h[w]+c[w]:h[x]+c[w]}}let g=n.makeTensorInfo(u.shape,d,c);if(l!=null){let y=N.getUndoAxesPermutation(l),b=Vn({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),b}return g}var U5={kernelName:Si,backendName:"cpu",kernelFunc:V5};function G5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,p=_0(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=H2(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var H5={kernelName:am,backendName:"cpu",kernelFunc:G5};function j5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;v.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let y=0;y<o;++y)for(let b=0;b<d;++b){let x=Math.floor(b/s),w=b%s;for(let I=0;I<c;++I){let T=Math.floor(I/s),C=I%s,E=(w*s+C)*h;for(let $=0;$<h;++$){let R=$+E+p*(T+u*(x+l*y));f[g++]=m[R]}}}return n.makeTensorInfo([o,d,c,h],r.dtype,f)}var q5={kernelName:Ml,backendName:"cpu",kernelFunc:j5};function PC(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a;ge([r,s],"depthwiseConv2DNative");let p=v.computeStrides(r.shape),d=v.computeStrides(s.shape),c=l;c==null&&(c=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=N.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,x=b.left,w=b.top,I=h.outChannels/h.inChannels,T=new Ht(h.outShape,r.dtype),C=n.data.get(r.dataId).values,E=n.data.get(s.dataId).values,$=T.values;for(let R=0;R<h.batchSize;++R){let F=R*p[0],S=R*T.strides[0];for(let M=0;M<h.outHeight;++M){let B=S+M*T.strides[1],U=M*h.strideHeight-w;for(let G=0;G<m;++G){let q=U+G*g;if(q<0||q>=h.inHeight)continue;let K=G*d[0],Z=F+q*p[1];for(let J=0;J<h.outWidth;++J){let ee=B+J*T.strides[2],ae=J*h.strideWidth-x;for(let te=0;te<f;++te){let ie=ae+te*y;if(ie<0||ie>=h.inWidth)continue;let oe=K+te*d[1],ye=Z+ie*h.inChannels,ue=ee,be=oe;for(let ke=0;ke<h.inChannels;++ke){let Se=C[ye+ke];for(let Le=0;Le<I;++Le)$[ue+Le]+=Se*E[be+Le];ue+=I,be+=I}}}}}}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var K5={kernelName:Ti,backendName:"cpu",kernelFunc:PC};function X5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a;ge([r,s],"depthwiseConv2dNativeBackpropFilter");let d=N.computeConv2DInfo(r.shape,p,i,o,l,u,!0),{strideHeight:c,strideWidth:h,filterHeight:m,filterWidth:f}=d,g=new Ht(d.filterShape,"float32"),y=d.padInfo.left,b=d.padInfo.top,x=d.outChannels/d.inChannels,w=n.data.get(r.dataId).values,I=new Ht(r.shape,r.dtype,w),T=n.data.get(s.dataId).values,C=new Ht(s.shape,s.dtype,T);for(let E=0;E<m;++E){let $=Math.max(0,Math.ceil((b-E)/c)),R=Math.min(d.outHeight,(d.inHeight+b-E)/c);for(let F=0;F<f;++F){let S=Math.max(0,Math.ceil((y-F)/h)),M=Math.min(d.outWidth,(d.inWidth+y-F)/h);for(let B=0;B<d.outChannels;++B){let U=Math.trunc(B/x),G=B%x,q=0;for(let K=0;K<d.batchSize;++K)for(let Z=$;Z<R;++Z){let J=E+Z*c-b;for(let ee=S;ee<M;++ee){let ae=F+ee*h-y;q+=I.get(K,J,ae,U)*C.get(K,Z,ee,B)}}g.set(q,E,F,U,G)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Y5={kernelName:rm,backendName:"cpu",kernelFunc:X5};function Z5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a;ge([r,s],"depthwiseConv2DNativeBackpropInput");let d=v.computeStrides(r.shape),c=v.computeStrides(s.shape),h=N.computeConv2DInfo(p,s.shape,i,o,l,u,!0),m=new Ht(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,x=n.data.get(r.dataId).values,[w,I,T]=d,C=n.data.get(s.dataId).values,[E,$,R]=c,{batchSize:F,filterHeight:S,filterWidth:M,inChannels:B,inHeight:U,inWidth:G,outChannels:q,outHeight:K,outWidth:Z,strideHeight:J,strideWidth:ee}=h,ae=S-1-h.padInfo.top,te=M-1-h.padInfo.left,ie=q/B;for(let oe=0;oe<F;++oe)for(let ye=0;ye<B;++ye)for(let ue=0;ue<U;++ue){let be=ue-ae,ke=Math.max(0,Math.ceil(be/J)),Se=Math.min(K,(S+be)/J);for(let Le=0;Le<G;++Le){let Ve=Le-te,nt=Math.max(0,Math.ceil(Ve/ee)),st=Math.min(Z,(M+Ve)/ee),Je=0;for(let at=ke;at<Se;++at){let ze=at*J-be;for(let dt=nt;dt<st;++dt){let Hn=dt*ee-Ve,Mt=w*oe+I*at+T*dt,sa=E*(S-1-ze)+$*(M-1-Hn)+R*ye;for(let sn=0;sn<ie;++sn){let Fn=ye*ie+sn,ia=x[Mt+Fn],Dn=C[sa+sn];Je+=ia*Dn}}}f[g*oe+y*ue+b*Le+ye]=Je}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var J5={kernelName:sm,backendName:"cpu",kernelFunc:Z5};function Q5(e){let{inputs:t,backend:n}=e,{x:a}=t,r=v.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=Pe([r,r],a.dtype),o=i.values;for(let u=0;u<s.length;u++)o[u*r+u]=s[u];let l=[...a.shape,...a.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var e8={kernelName:im,backendName:"cpu",kernelFunc:Q5},t8={kernelName:uc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:w,strideWidth:I,filterHeight:T,filterWidth:C,dilationHeight:E,dilationWidth:$,outShape:R}=N.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),F=v.sizeFromShape(R),S=R.length,M=v.getArrayFromDType(a.dtype,F);for(let B=0;B<h;++B)for(let U=0;U<y;++U){let G=U*w-x.top;for(let q=0;q<b;++q){let K=q*I-x.left;for(let Z=0;Z<g;++Z){let J=Number.MIN_SAFE_INTEGER;for(let ae=0;ae<T;++ae){let te=G+ae*E;if(te>=0&&te<m)for(let ie=0;ie<C;++ie){let oe=K+ie*$;if(oe>=0&&oe<f){let ye=v.locToIndex([B,te,oe,Z],p,v.computeStrides(a.shape)),ue=v.locToIndex([ae,ie,Z],c,v.computeStrides(r.shape)),be=u[ye]+d[ue];be>J&&(J=be)}}}let ee=v.locToIndex([B,U,q,Z],S,v.computeStrides(R));M[ee]=J}}}return{dataId:l.write(v.toTypedArray(M,a.dtype),R,a.dtype),shape:R,dtype:a.dtype}}},n8={kernelName:gh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:$}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===$.length,()=>`Error in ${gh}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let R=v.toNestedArray($,u.data.get(s.dataId).values),F=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S<c;++S)for(let M=0;M<g;++M){let B=M*x-b.top;for(let U=0;U<y;++U){let G=U*w-b.left;for(let q=0;q<f;++q){let K=Number.MIN_SAFE_INTEGER,Z=0,J=0;for(let ee=0;ee<I;++ee){let ae=B+ee*C;if(ae>=0&&ae<h)for(let te=0;te<T;++te){let ie=G+te*E;if(ie>=0&&ie<m){let oe=p[S][ae][ie][q]+d[ee][te][q];oe>K&&(K=oe,Z=ee,J=te)}}}F[Z][J][q]+=R[S][M][U][q]}}}return{dataId:u.write(v.toTypedArray(F,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},a8={kernelName:fh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:$}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===$.length,()=>`Error in ${fh}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let 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mK={kernelName:fm,backendName:"cpu",kernelFunc:hK};function fK(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;ge([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=N.computePool2DInfo(o.shape,l,u,1,p,d),h=n.data.get(o.dataId).values,m=Pe(c.outShape,o.dtype,DC(h,o.shape,o.dtype,c).values),f=c.strideHeight,g=c.strideWidth,y=c.dilationHeight,b=c.dilationWidth,x=c.effectiveFilterHeight,w=c.effectiveFilterWidth,I=w-1-c.padInfo.left,T=x-1-c.padInfo.top,C=Pe(o.shape,"float32"),E=n.data.get(r.dataId).values,$=Pe(r.shape,"float32",E);for(let R=0;R<c.batchSize;++R)for(let F=0;F<c.inChannels;++F)for(let S=0;S<c.inHeight;++S)for(let M=0;M<c.inWidth;++M){let B=S-T,U=M-I,G=0;for(let q=0;q<x;q+=y){let K=(B+q)/f;if(!(K<0||K>=c.outHeight||Math.floor(K)!==K))for(let Z=0;Z<w;Z+=b){let J=(U+Z)/g;if(J<0||J>=c.outWidth||Math.floor(J)!==J)continue;let ee=x*w-1-m.get(R,K,J,F),ae=q*w+Z,te=ee===ae?1:0;if(te===0)continue;let 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ve=H();ve.registerFlag("HAS_WEBGL",()=>ve.getNumber("WEBGL_VERSION")>0);ve.registerFlag("WEBGL_VERSION",()=>lx(2)?2:lx(1)?1:0);ve.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ve.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ve.get("WEBGL_VERSION")===2);ve.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ve.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ve.registerFlag("WEBGL_PACK",()=>ve.getBool("HAS_WEBGL"));ve.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_CLIP",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_REDUCE",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_LAZILY_UNPACK",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_CONV_IM2COL",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>u_(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>p_(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=ve.getNumber("WEBGL_VERSION");return e===0?0:c_(e)});ve.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ve.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Tc.isMobile());ve.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>d_(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ve.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ve.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ve.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>h_(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_FENCE_API_ENABLED",()=>m_(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>ve.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);ve.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});ve.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Tc.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});ve.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);ve.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);ve.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);ve.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);ve.registerFlag("WEBGL_EXP_CONV",()=>!1);ve.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>ve.getBool("IS_TEST"));ve.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);ve.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);ve.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);function Cn(){let e,t,n,a,r,s,i,o,l,u;return H().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=H().getBool("WEBGL2_ISNAN_CUSTOM")?`
      bool isnan_custom(float val) {
        uint floatToUint = floatBitsToUint(val);
        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
      }

      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan_custom(val.x),
          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
      }

      #define isnan(value) isnan_custom(value)
    `:"",l="",u=`
      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 newRound(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `):(e="",t="attribute",n="varying",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
      #define isnan(value) isnan_custom(value)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
      }
    `,l=`
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `,u=`
      int round(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function vo(e,t,n="index"){let a=v.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Df(e,t,n="index"){let a=v.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function XY(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function YY(e,t,n="index"){let a=e.map((s,i)=>i),r=XY(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function U0(e){let t=v.computeStrides(e).map(n=>n.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}function G0(){return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`}var f_=`
  const float FLOAT_MAX = 1.70141184e38;
  const float FLOAT_MIN = 1.17549435e-38;

  lowp vec4 encode_float(highp float v) {
    if (isnan(v)) {
      return vec4(255, 255, 255, 255);
    }

    highp float av = abs(v);

    if(av < FLOAT_MIN) {
      return vec4(0.0, 0.0, 0.0, 0.0);
    } else if(v > FLOAT_MAX) {
      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
    } else if(v < -FLOAT_MAX) {
      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
    }

    highp vec4 c = vec4(0,0,0,0);

    highp float e = floor(log2(av));
    highp float m = exp2(fract(log2(av))) - 1.0;

    c[2] = floor(128.0 * m);
    m -= c[2] / 128.0;
    c[1] = floor(32768.0 * m);
    m -= c[1] / 32768.0;
    c[0] = floor(8388608.0 * m);

    highp float ebias = e + 127.0;
    c[3] = floor(ebias / 2.0);
    ebias -= c[3] * 2.0;
    c[2] += floor(ebias) * 128.0;

    c[3] += 128.0 * step(0.0, -v);

    return c / 255.0;
  }
`,{getBroadcastDims:g_}=N;function ZY(e,t,n){let a=[];if(e.forEach(c=>{let h=v.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:m}=H0(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(m.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${c.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${c.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${c.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${c.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(`
`),s=e.map(c=>JY(c,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),i=t.texShape,o=Cn(),l=tZ(o),u,p,d=rZ(o);return t.isPacked?(u=QY(t.logicalShape,i,n.enableShapeUniforms),p=aZ(o)):(u=eZ(t.logicalShape,i,n.enableShapeUniforms),p=nZ(o)),n.packedInputs&&(d+=lZ),[d,l,p,r,u,s,n.userCode].join(`
`)}function zu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return vZ(e,t);case 1:return kZ(e,t);case 2:return SZ(e,t);case 3:return NZ(e,t);case 4:return _Z(e,t);case 5:return EZ(e);case 6:return $Z(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function y_(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return xZ(e);case 1:return wZ(e,t);case 2:return IZ(e,t);case 3:return TZ(e,t);default:return CZ(e,t)}}function JY(e,t,n=!1,a){let r="";n?r+=y_(e,a):r+=zu(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=AZ(e,t):r+=FZ(e,t)),r}function QY(e,t,n){switch(e.length){case 0:return b_();case 1:return uZ(e,t,n);case 2:return yZ(e,t,n);case 3:return cZ(e,t,n);default:return hZ(e,t,n)}}function eZ(e,t,n){switch(e.length){case 0:return b_();case 1:return pZ(e,t,n);case 2:return bZ(e,t,n);case 3:return dZ(e,t,n);case 4:return mZ(e,t,n);case 5:return fZ(e,t);case 6:return gZ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function tZ(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function nZ(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function aZ(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function rZ(e){return`${e.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${e.varyingFs} vec2 resultUV;
    ${e.defineOutput}
    const vec2 halfCR = vec2(0.5, 0.5);

    struct ivec5
    {
      int x;
      int y;
      int z;
      int w;
      int u;
    };

    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

    uniform float NAN;
    ${e.defineSpecialNaN}
    ${e.defineSpecialInf}
    ${e.defineRound}

    int imod(int x, int y) {
      return x - y * (x / y);
    }

    int idiv(int a, int b, float sign) {
      int res = a / b;
      int mod = imod(a, b);
      if (sign < 0. && mod != 0) {
        res -= 1;
      }
      return res;
    }

    //Based on the work of Dave Hoskins
    //https://www.shadertoy.com/view/4djSRW
    #define HASHSCALE1 443.8975
    float random(float seed){
      vec2 p = resultUV * seed;
      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
      p3 += dot(p3, p3.yzx + 19.19);
      return fract((p3.x + p3.y) * p3.z);
    }

    ${sZ}
    ${iZ}
    ${oZ}
  `}var sZ=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
  int texelIndex = index / 2;
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,iZ=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
  int texNumC, int row, int col) {
  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,oZ=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
    int texelsInBatch, int texelsInLogicalRow, int b,
    int row, int col) {
  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,lZ=`
  float getChannel(vec4 frag, vec2 innerDims) {
    vec2 modCoord = mod(innerDims, 2.);
    return modCoord.x == 0. ?
      (modCoord.y == 0. ? frag.r : frag.g) :
      (modCoord.y == 0. ? frag.b : frag.a);
  }
  float getChannel(vec4 frag, int dim) {
    float modCoord = mod(float(dim), 2.);
    return modCoord == 0. ? frag.r : frag.g;
  }
`;function b_(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function uZ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${a[1]}.0);
      }
    `:a[1]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${a[0]}.0);
      }
    `:n?`
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));
      return 2 * (resTexRC.x * ${a[1]} + resTexRC.y);
    }
  `}function pZ(e,t,n){return t[0]===1?n?`
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?n?`
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:n?`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function cZ(e,t,n){if(n)return`
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec3(b, r, c);
    }
  `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),s=r*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));
      int index = resTexRC.x * ${a[1]} + resTexRC.y;

      int b = index / ${s};
      index -= b * ${s};

      int r = 2 * (index / ${r});
      int c = imod(index, ${r}) * 2;

      return ivec3(b, r, c);
    }
  `}function dZ(e,t,n){if(n)return`
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${Df(["r","c","d"],e)}
    return ivec3(r, c, d);
  }
`;let a=vo(["r","c","d"],e);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${a}
      return ivec3(r, c, d);
    }
  `}function hZ(e,t,n){if(n)return`
    ivec4 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatchN = texelsInBatch * outShape[1];

      int b2 = index / texelsInBatchN;
      index -= b2 * texelsInBatchN;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec4(b2, b, r, c);
    }
  `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),s=r*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u<e.length-1;u++)i*=e[e.length-u-1],o=`
      int b${u} = index / ${i};
      index -= b${u} * ${i};
    `+o,l=`b${u}, `+l;return`
    ivec${e.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));
      int index = resTexRC.x * ${a[1]} + resTexRC.y;

      ${o}

      int b = index / ${s};
      index -= b * ${s};

      int r = 2 * (index / ${r});
      int c = imod(index, ${r}) * 2;

      return ivec${e.length}(${l});
    }
  `}function mZ(e,t,n){if(n)return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      ${Df(["r","c","d","d2"],e)}
      return ivec4(r, c, d, d2);
    }
  `;let a=vo(["r","c","d","d2"],e);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${a}
      return ivec4(r, c, d, d2);
    }
  `}function fZ(e,t){let n=vo(["r","c","d","d2","d3"],e);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

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

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function gZ(e,t){let n=vo(["r","c","d","d2","d3","d4"],e);return`
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${n}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function yZ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
      ivec2 getOutputCoords() {
        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${a[0]}, ${a[1]}));
      }
    `;let r=Math.ceil(e[1]/2);return n?`
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));

      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${a[0]}, ${a[1]}));

      int index = resTexRC.x * ${a[1]} + resTexRC.y;
      int r = 2 * (index / ${r});
      int c = imod(index, ${r}) * 2;

      return ivec2(r, c);
    }
  `}function bZ(e,t,n){return v.arraysEqual(e,t)?n?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:e[1]===1?n?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:e[0]===1?n?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:n?`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${e[1]};
      int c = index - r * ${e[1]};
      return ivec2(r, c);
    }
  `}function wo(e){return`offset${e}`}function xZ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=Cn();return`
    vec4 ${n}() {
      return ${a.texture2D}(${t}, halfCR);
    }
  `}function vZ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return`
      float ${a}() {
        return sampleTexture(${n}, halfCR);
      }
    `;let i=wo(n);if(t)return`
    float ${a}() {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
      return sampleTexture(${n}, uv);
    }
  `;let[o,l]=e.shapeInfo.texShape;return`
    float ${a}() {
      vec2 uv = uvFromFlat(${o}, ${l}, ${i});
      return sampleTexture(${n}, uv);
    }
  `}function wZ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=Cn();if(t)return`
    vec4 ${a}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${s.texture2D}(${n}, uv);
    }
  `;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
    vec4 ${a}(int index) {
      vec2 uv = packedUVfrom1D(
        ${i[0]}, ${i[1]}, index);
      return ${s.texture2D}(${n}, uv);
    }
  `}function kZ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
      float ${a}(int index) {
        ${Wu(e)}
      }
    `;let r=e.shapeInfo.texShape,s=r[0],i=r[1];if(i===1&&s===1)return`
      float ${a}(int index) {
        return sampleTexture(${n}, halfCR);
      }
    `;let o=wo(n);return i===1?t?`
      float ${a}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${a}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
        return sampleTexture(${n}, uv);
      }
    `:s===1?t?`
      float ${a}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${a}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
        return sampleTexture(${n}, uv);
      }
    `:t?`
    float ${a}(int index) {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});
      return sampleTexture(${n}, uv);
    }
  `:`
    float ${a}(int index) {
      vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
      return sampleTexture(${n}, uv);
    }
  `}function IZ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Cn();if(s!=null&&v.arraysEqual(n,s))return t?`
      vec4 ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);

        return ${l.texture2D}(${a}, uv);
      }
    `:`
      vec4 ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);

        return ${l.texture2D}(${a}, uv);
      }
    `;if(t)return`
    vec4 ${r}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${a}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${l.texture2D}(${a}, uv);
    }
  `;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],p=Math.ceil(n[1]/2);return`
    vec4 ${r}(int row, int col) {
      vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col);
      return ${l.texture2D}(${a}, uv);
    }
  `}function SZ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(n,s)){if(t)return`
      float ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `;let c=s[0],h=s[1];return`
    float ${r}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${c}.0);
      return sampleTexture(${a}, uv);
    }
  `}let{newShape:i,keptDims:o}=v.squeezeShape(n),l=i;if(l.length<n.length){let c=Bu(e,l),h=["row","col"];return`
      ${zu(c,t)}
      float ${r}(int row, int col) {
        return ${r}(${Vu(h,o)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
        ${Wu(e)}
      }
    `;let u=s[0],p=s[1],d=wo(a);return p===1?t?`
      float ${r}(int row, int col) {
        float index = dot(vec3(row, col, ${d}), vec3(${a}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${a}TexShape[0]));
        return sampleTexture(${a}, uv);
      }
    `:`
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
      return sampleTexture(${a}, uv);
    }
  `:u===1?t?`
      float ${r}(int row, int col) {
        float index = dot(vec3(row, col, ${d}), vec3(${a}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${a}TexShape[1]), 0.5);
        return sampleTexture(${a}, uv);
      }
    `:`
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${p}.0, 0.5);
      return sampleTexture(${a}, uv);
    }
  `:t?`
      float ${r}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${a}Shape[1] + col + ${d};
        vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
        return sampleTexture(${a}, uv);
      }
    `:`
  float ${r}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${n[1]} + col + ${d};
    vec2 uv = uvFromFlat(${u}, ${p}, index);
    return sampleTexture(${a}, uv);
  }
`}function TZ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(n[0]===1){let c=n.slice(1),h=[1,2],m=Bu(e,c),f=["b","row","col"];return`
        ${y_(m,t)}
        vec4 ${r}(int b, int row, int col) {
          return ${r}(${Vu(f,h)});
        }
      `}let o=Cn();if(t)return`
    vec4 ${r}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${a}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${a}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${o.texture2D}(${a}, uv);
    }
  `;let l=i[0],u=i[1],p=Math.ceil(n[2]/2),d=p*Math.ceil(n[1]/2);return`
    vec4 ${r}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${l}, ${u}, ${d}, ${p}, b, row, col);
      return ${o.texture2D}(${a}, uv);
    }
  `}function NZ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=v.squeezeShape(n),u=o;if(u.length<n.length){let f=Bu(e,u),g=["row","col","depth"];return`
        ${zu(f,t)}
        float ${r}(int row, int col, int depth) {
          return ${r}(${Vu(g,l)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${s}, ${i}, 1)));
        ${Wu(e)}
      }
    `;let p=e.shapeInfo.texShape,d=p[0],c=p[1],h=e.shapeInfo.flatOffset;if(c===s&&h==null)return t?`
      float ${r}(int row, int col, int depth) {
        int stride1 = ${a}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `:`
        float ${r}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${i}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${c}.0, ${d}.0);
          return sampleTexture(${a}, uv);
        }
      `;if(c===i&&h==null)return t?`
      float ${r}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${a}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `:`
    float ${r}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}.0, ${d}.0);
      return sampleTexture(${a}, uv);
    }
  `;let m=wo(a);return t?`
    float ${r}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${a}Shape[1] * ${a}Shape[2];
      int stride1 = ${a}Shape[2];
      int index = row * stride0 + col * stride1 + depth + ${m};
      vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
      return sampleTexture(${a}, uv);
    }
    `:`
      float ${r}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${s} + col * ${i} + depth + ${m};
        vec2 uv = uvFromFlat(${d}, ${c}, index);
        return sampleTexture(${a}, uv);
      }
  `}function CZ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Cn();if(t)return`
    vec4 ${a}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${n}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int texR = index / packedTexShape[1];
      int texC = index - texR * packedTexShape[1];
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
    }
  `;let s=e.shapeInfo.logicalShape,i=s.length,o=e.shapeInfo.texShape,l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=l[0],p=l[1],d=Math.ceil(s[i-1]/2),c=d*Math.ceil(s[i-2]/2),h="int b, int row, int col",m=`b * ${c} + (row / 2) * ${d} + (col / 2)`;for(let f=2;f<i-1;f++)h=`int b${f}, `+h,c*=s[i-f-1],m=`b${f} * ${c} + `+m;return`
    vec4 ${a}(${h}) {
      int index = ${m};
      int texR = index / ${p};
      int texC = index - texR * ${p};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}, ${u});
      return ${r.texture2D}(${n}, uv);
    }
  `}function _Z(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[3],i=n[2]*s,o=n[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(n);if(l.length<n.length){let b=Bu(e,l),x=["row","col","depth","depth2"];return`
      ${zu(b,t)}
      float ${r}(int row, int col, int depth, int depth2) {
        return ${r}(${Vu(x,u)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${o}, ${i}, ${s}, 1)));
        ${Wu(e)}
      }
    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,c=d[0],h=d[1],m=`int stride2 = ${a}Shape[3];`,f=`int stride1 = ${a}Shape[2] * stride2;`,g=`int stride0 = ${a}Shape[1] * stride1;`;if(h===o&&p==null)return t?`
      float ${r}(int row, int col, int depth, int depth2) {
        ${m}
        ${f}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `:`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${i}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${h}.0, ${c}.0);
        return sampleTexture(${a}, uv);
      }
    `;if(h===s&&p==null)return t?`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${a}Shape[1] * ${a}Shape[2], ${a}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${a}TexShape[1], ${a}TexShape[0]);
        return sampleTexture(${a}, uv);
      }
    `:`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${n[1]*n[2]}, ${n[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${h}.0, ${c}.0);
        return sampleTexture(${a}, uv);
      }
    `;let y=wo(a);return t?`
    float ${r}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${m}
      ${f}
      ${g}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index + ${y});
      return sampleTexture(${a}, uv);
    }
  `:`
    float ${r}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${o} + col * ${i} +
          depth * ${s} + depth2;
      vec2 uv = uvFromFlat(${c}, ${h}, index + ${y});
      return sampleTexture(${a}, uv);
    }
  `}function EZ(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let f=Bu(e,l),g=["row","col","depth","depth2","depth3"];return`
      ${zu(f)}
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        return ${a}(${Vu(g,u)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
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          vec4(${o}, ${i}, ${s}, ${r})) +
          depth3;
        ${Wu(e)}
      }
    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,c=d[0],h=d[1];if(h===o&&p==null)return`
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${i}, ${s}, ${r}, 1));
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      }
    `;if(h===r&&p==null)return`
      float ${a}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
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                  vec2(${h}.0, ${c}.0);
        return sampleTexture(${n}, uv);
      }
    `;let m=wo(n);return`
    float ${a}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${o} + col * ${i} + depth * ${s} +
          depth2 * ${r} + depth3 + ${m};
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      return sampleTexture(${n}, uv);
    }
  `}function $Z(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=v.squeezeShape(t);if(r.length<t.length){let g=Bu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
      ${zu(g)}
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${a}(${Vu(y,s)});
      }
    `}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,p=t[1]*u;if(e.shapeInfo.isUniform)return`
      float ${a}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${p}, ${u}, ${l}, ${o})) +
          dot(
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            vec2(${i}, 1)));
        ${Wu(e)}
      }
    `;let d=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],m=c[1];if(m===p&&d==null)return`
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${u}, ${l}, ${o}, ${i})) +
               float(depth4);
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                   vec2(${m}.0, ${h}.0);
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      }
    `;if(m===i&&d==null)return`
      float ${a}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
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        return sampleTexture(${n}, uv);
      }
    `;let f=wo(n);return`
    float ${a}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
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          depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
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      return sampleTexture(${n}, uv);
    }
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    for (int i = 0; i < ${n}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function AZ(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=g_(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,p,d=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(`
`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,y)=>`coords.${d[y+u]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(m&&!f)i===1?h=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:h=`
        return vec4(outputValue.x);
      `;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${r}() {
      ${l} coords = getOutputCoords();
      ${p}
      vec4 outputValue = get${a}(${c});
      ${h}
    }
  `}function FZ(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
      float ${r}() {
        return sampleTexture(${n}, resultUV);
      }
    `;let u=gt(l),p=g_(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+d]}`).join(", "),`
    float ${r}() {
      ${u} coords = getOutputCoords();
      ${c}
      return get${a}(${m});
    }
  `}function gt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function H0(e,t,n){let{newShape:a,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!v.arraysEqual(t,n)&&a.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:r}}function Bu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Vu(e,t){return t.map(n=>e[n]).join(", ")}function DZ(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=ZY(r,i,t),l=KC(e.gl,o),u=e.createProgram(l);return H().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},x_(e,t,u))}function x_(e,t,n){let a={},r={},s={},i=[],o,l,u,p=null,d=null;d=e.getUniformLocation(n,"NAN",!1),H().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(n,"INFINITY",!1));let c=!1;for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h];a[m]=e.getUniformLocation(n,m,c),a[`offset${m}`]=e.getUniformLocation(n,`offset${m}`,c),t.enableShapeUniforms&&(r[`${m}Shape`]=e.getUniformLocation(n,`${m}Shape`,c),s[`${m}TexShape`]=e.getUniformLocation(n,`${m}TexShape`,c))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,"outShape",c),u=e.getUniformLocation(n,"outShapeStrides",c),l=e.getUniformLocation(n,"outTexShape",c)),t.customUniforms&&t.customUniforms.forEach((h,m)=>{i[m]=e.getUniformLocation(n,h.name,c)}),{uniformLocations:a,customUniformLocations:i,infLoc:p,nanLoc:d,inShapesLocations:r,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function Hk(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function RZ(e,t,n,a,r){t.program.enableShapeUniforms||(Hk(t.inShapeInfos,n),Hk([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),H().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let p=t.program.variableNames[u],d=t.uniformLocations[p],c=t.uniformLocations[`offset${p}`],h=t.inShapesLocations[`${p}Shape`],m=t.inTexShapesLocations[`${p}TexShape`];if(h){let{uniformShape:f}=H0(t.program.packedInputs,l.shape,l.texData.texShape);switch(f.length){case 1:e.gl.uniform1iv(h,new Int32Array(f));break;case 2:e.gl.uniform2iv(h,new Int32Array(f));break;case 3:e.gl.uniform3iv(h,new Int32Array(f));break;case 4:e.gl.uniform4iv(h,new Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(d,f)}return}l.texData.slice!=null&&c!=null&&e.gl.uniform1i(c,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,d,u)}});let o=t.outShapeLocation;if(o)switch(a.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(a.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(a.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(a.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(a.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(a.shape);switch(a.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,a.texData.texShape[0],a.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let p=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(p,d);else if(l.type==="vec2")e.gl.uniform2fv(p,d);else if(l.type==="vec3")e.gl.uniform3fv(p,d);else if(l.type==="vec4")e.gl.uniform4fv(p,d);else if(l.type==="int")e.gl.uniform1iv(p,d);else if(l.type==="ivec2")e.gl.uniform2iv(p,d);else if(l.type==="ivec3")e.gl.uniform3iv(p,d);else if(l.type==="ivec4")e.gl.uniform4iv(p,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function MZ(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=H0(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=v.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,b=N.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&v.arraysEqual(l,n.texData.texShape),w=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${y}_${b}_${g}_${c}_${h}_${m}_${w}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${H().getNumber("WEBGL_VERSION")}`,s}function _n(e){return H().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var PZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Jp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Cn();this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Df(["r","c","d"],e):vo(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getA(rc.x, rc.y, rc.z);
        }

        ${t.output} = result;
      }
    `}},OZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Jp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Cn();this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Df(["r","c","d"],e):vo(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
        }

        ${t.output} = result;
      }
    `}},LZ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=pa.DOWNLOAD;let t=Cn();this.outputShape=e,this.userCode=`
      ${f_}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}},zZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=pa.DOWNLOAD;let t=Cn();this.outputShape=e,this.userCode=`
      ${f_}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},WZ={R:0,G:1,B:2,A:3},jk=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let a=Cn();this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;i<n.length;i++){let o=n[i];s+=`
          if(offset == ${i}) {
            result = values[${WZ[o]}];
          }`}this.userCode=`
      ${this.enableShapeUniforms?G0():U0(e)}

      void main() {
        ivec3 coords = getOutputCoords();
        int flatIndex = getFlatIndex(coords);
        float result = 0.;
        int offset = imod(flatIndex, ${n.length});

        flatIndex = idiv(flatIndex, ${n.length}, 1.);

        int r = flatIndex / texShape[1];
        if (r < texShape[0]) {
          int c = imod(flatIndex, texShape[1]);
          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
          vec4 values = ${a.texture2D}(A, uv);
          ${s}
        }
        ${a.output} = vec4(${r}, 0., 0., 0.);
      }
    `}},BZ=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Cn();this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length);let a="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;a+=`
          localCoords = coords;
          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
          localCoords[2] += ${i};
          if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
            localCoords[1] += ${s};

            flatIndex = getFlatIndex(localCoords);
            offset = imod(flatIndex, 4);

            flatIndex = idiv(flatIndex, 4, 1.);

            int r = flatIndex / texShape[1];
            int c = imod(flatIndex, texShape[1]);
            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
            values = ${n.texture2D}(A, uv);

            if (offset == 0) {
              result[${o}] = values[0];
            } else if (offset == 1) {
              result[${o}] = values[1];
            } else if (offset == 2) {
              result[${o}] = values[2];
            } else {
              result[${o}] = values[3];
            }
          }
        }
        `}this.userCode=`
        ${this.enableShapeUniforms?G0():U0(e)}

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

          vec4 result = vec4(0.);
          int flatIndex, r, c, offset;
          ivec3 localCoords;
          vec2 uv;
          vec4 values;

          ${a}

          ${n.output} = ${r};
        }
    `}},v_={};$e(v_,{bindVertexProgramAttributeStreams:()=>E_,createBufferFromOutputTexture:()=>F_,createFloat16MatrixTexture:()=>T_,createFloat16PackedMatrixTexture:()=>__,createFloat32MatrixTexture:()=>S_,createIndexBuffer:()=>I_,createPackedMatrixTexture:()=>C_,createUnsignedBytesMatrixTexture:()=>N_,createVertexBuffer:()=>k_,createVertexShader:()=>w_,downloadByteEncodedFloatMatrixFromOutputTexture:()=>R_,downloadFloat32MatrixFromBuffer:()=>D_,downloadMatrixFromPackedOutputTexture:()=>P_,downloadPackedMatrixFromBuffer:()=>M_,getInternalFormatForFloat16MatrixTexture:()=>q0,getInternalFormatForFloat16PackedMatrixTexture:()=>Y0,getInternalFormatForFloat32MatrixTexture:()=>j0,getInternalFormatForPackedMatrixTexture:()=>X0,getInternalFormatForUnsignedBytesMatrixTexture:()=>K0,uploadDenseMatrixToTexture:()=>$_,uploadPixelDataToTexture:()=>A_});function w_(e){let t=Cn(),n=`${t.version}
    precision highp float;
    ${t.attribute} vec3 clipSpacePos;
    ${t.attribute} vec2 uv;
    ${t.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;return qC(e,n)}function k_(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return ZC(e,t)}function I_(e){let t=new Uint16Array([0,1,2,2,1,3]);return JC(e,t)}function td(e,t,n,a,r,s){e_(t,n);let i=QC(e),o=e.TEXTURE_2D;return me(e,()=>e.bindTexture(o,i)),me(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),me(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),me(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),me(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),H().getNumber("WEBGL_VERSION")===1?me(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)):me(e,()=>e.texStorage2D(o,1,a,t,n)),me(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function j0(e){return e.internalFormatFloat}function S_(e,t,n,a){let[r,s]=ed(t,n);return td(e,r,s,j0(a),a.textureFormatFloat,e.FLOAT)}function q0(e){return e.internalFormatHalfFloat}function T_(e,t,n,a){let[r,s]=ed(t,n);return td(e,r,s,q0(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function K0(e){return e.downloadTextureFormat}function N_(e,t,n,a){let[r,s]=ed(t,n);return td(e,r,s,K0(a),e.RGBA,e.UNSIGNED_BYTE)}function X0(e){return e.internalFormatPackedFloat}function C_(e,t,n,a){let[r,s]=Ou(t,n);return td(e,r,s,X0(a),e.RGBA,e.FLOAT)}function Y0(e){return e.internalFormatPackedHalfFloat}function __(e,t,n,a){let[r,s]=Ou(t,n);return td(e,r,s,Y0(a),e.RGBA,a.textureTypeHalfFloat)}function E_(e,t,n){return me(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),ix(e,t,"clipSpacePos",n,3,20,0)&&ix(e,t,"uv",n,2,20,12)}function $_(e,t,n,a,r,s){me(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new 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me(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),me(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),me(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function D_(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function R_(e,t,n,a){let[r,s]=ed(t,n),i=4,o=new Uint8Array(OY(t*n,i));return me(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function M_(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(LY(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function P_(e,t,n){let a=new Float32Array(t*n*4);return me(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var ph=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let 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if(n="EXT_color_buffer_float",ca(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ca(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=k_(this.gl),this.indexBuffer=I_(this.gl),this.framebuffer=t_(this.gl),this.textureConfig=B0(this.gl,this.textureHalfFloatExtension)}get debug(){return H().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),__(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),C_(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(ox(this.gl,this.framebuffer),this.outputTexture=null),me(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>R_(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return M_(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return D_(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=F_(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(H().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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me(t,()=>t.attachShader(n,this.vertexShader)),me(t,()=>t.attachShader(n,e)),YC(t,n),this.debug&&ih(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=E_(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&me(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ih(this.gl,this.program),me(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?a_(this.gl,e,t):r_(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),me(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),s_(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Ou(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&ih(this.gl,this.program),Rp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),me(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),me(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return 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a=this.gl;oh(a,e,this.framebuffer),this.debug&&Rp(a),this.outputTexture=e,me(a,()=>a.viewport(0,0,t,n)),me(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),me(this.gl,()=>this.gl.scissor(e,t,n,a))}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 VZ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:UZ,bincountImpl:O_,bincountReduceImpl:GZ,castImpl:HZ,ceilImpl:jZ,concatImpl:qZ,equalImpl:KZ,expImpl:XZ,expm1Impl:YZ,floorImpl:ZZ,gatherNdImpl:JZ,gatherV2Impl:QZ,greaterImpl:e7,greaterEqualImpl:t7,lessImpl:n7,lessEqualImpl:a7,linSpaceImpl:r7,logImpl:s7,maxImpl:i7,maximumImpl:o7,minimumImpl:l7,multiplyImpl:u7,negImpl:p7,notEqualImpl:c7,prodImpl:d7,raggedGatherImpl:h7,raggedRangeImpl:m7,raggedTensorToTensorImpl:f7,rangeImpl:g7,rsqrtImpl:y7,scatterImpl:b7,sigmoidImpl:x7,simpleAbsImpl:L_,sliceImpl:v7,sparseFillEmptyRowsImpl:w7,sparseReshapeImpl:k7,sparseSegmentReductionImpl:z_,sqrtImpl:I7,stridedSliceImpl:S7,stringNGramsImpl:T7,stringSplitImpl:N7,stringToHashBucketFastImpl:C7,subImpl:_7,tileImpl:E7,topKImpl:$7,transposeImpl:Z0,uniqueImpl:A7}=B2;function W_(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function wn(e,t){return t===1?[e]:W_(e,t)}function F7(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var D7=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=_n(this.outputShape.length),this.rank===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{let t=wn("rc",this.rank),n=gt(this.rank),a=this.getOutOfBoundsCondition(t),r=this.getSetup(t),s=this.getOutput(t);this.userCode=`
        void main() {
          ${n} rc = getOutputCoords();

          if(${a}) {
            setOutput(vec4(0));
          } else {
            ${r}

            setOutput(vec4(${s}));
          }
        }
      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let a=0;a<=1;a++){let r=`${n===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let s=2;s<this.rank;s++)r=`${e[e.length-1-s]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],a=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
      int r = ${t[0]};
      int c = ${t[1]};
      int rp1 = r + 1;
      int cp1 = c + 1;

      bool cEdge = cp1 >= ${n};
      bool rEdge = rp1 >= ${a};
    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
            cEdge ? 0. : getA(${t[1]}),
            rEdge ? 0. : getA(${t[2]}),
            rEdge || cEdge ? 0. : getA(${t[3]})`}},B_=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
        ${r}
        ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
          int flatIndex = getFlatIndex(thisRC);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${a}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${a>0?"}":""}
      `}this.userCode=`
      ${R7(t,this.enableShapeUniforms)}
      ${this.enableShapeUniforms?G0():U0(e)}

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

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
        int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};

        ${n}

        setOutput(result);
      }
    `}};function R7(e,t){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t?YY(["r","c","d"],"inputShape"):vo(["r","c","d"],e)}
      return ivec3(r, c, d);
    }
  `}var M7=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=Kk(t,n),r=Xk(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=qk(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===on.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===on.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===on.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===on.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===on.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=Kk(n,a),s=Xk(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=qk(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=H().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function P7(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function qk(e,t,n,a,r){let s=O7(t,a),i;if(r){let[l,u]=Ou(e[0],e[1]);i=l*u}else{let[l,u]=ed(e[0],e[1]);i=l*u}let o=P7(n,s);return i*o}function O7(e,t){switch(e){case on.PACKED_2X2_FLOAT32:return X0(t);case on.PACKED_2X2_FLOAT16:return Y0(t);case on.UNPACKED_FLOAT32:return j0(t);case on.UNPACKED_FLOAT16:return q0(t);case on.PACKED_4X1_UNSIGNED_BYTE:return K0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function L7(e){return H().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?on.PACKED_2X2_FLOAT32:on.UNPACKED_FLOAT32:e?on.PACKED_2X2_FLOAT16:on.UNPACKED_FLOAT16}function Kk(e,t){if(e===pa.UPLOAD)return on.PACKED_2X2_FLOAT32;if(e===pa.RENDER||e==null)return L7(t);if(e===pa.DOWNLOAD||e===pa.PIXELS)return on.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Xk(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Sr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=`
      float unaryOperation(float x) {
        ${t}
      }

      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);

        setOutput(y);
      }
    `}},Da="if (isnan(x)) return x;",z7="return x;",Yk="return abs(x);",W7="return (x >= 0.0) ? x : (exp(x) - 1.0);",B7=Da+`
  return (x < 0.0) ? 0.0 : x;
`,V7=Da+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,jo="return x;",U7="return 1.0 / (1.0 + exp(-1.0 * x));",G7="return x;",H7=`
  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;
`,j7=`
  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;
`,q7=`
  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;
`,K7="return 1.0 / (1.0 + exp(-1.0 * x));",js=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);

        setOutput(y);
      }
    `}},X7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length);let t=e.length,n=wn("rc",t),a=gt(t),r=F7(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
      void main() {
        ${a} rc = getOutputCoords();
        vec4 packedInput = getA(${r});

        setOutput(getChannel(packedInput, ${i}));
      }
    `}},Y7=cr.whereImpl,Z7=1e-7,J7=1e-4,gb={};function Q7(e){return e in gb||(gb[e]={}),gb[e]}var eJ=H().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),tJ=600;function nJ(){return H().global.screen==null?1024:H().global.screen.height*H().global.screen.width*window.devicePixelRatio*tJ/1024/1024}var Rf=class extends rc{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!H().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof ph)t=e;else{let n=qa(H().getNumber("WEBGL_VERSION"),e);t=new ph(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qa(H().getNumber("WEBGL_VERSION"));t=new ph(n),this.binaryCache=Q7(H().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new M7(this.gpgpu),this.numMBBeforeWarning=nJ(),this.texData=new jh(this,Na())}nextDataId(){return Rf.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,a,r,s){let i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[a,r]},o.texShape=[a,r];let l=Mp(t),u=new jk(l,!1,s),p=this.runWebGLProgram(u,[i],n,[[a,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,n){if((H().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||H().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:pa.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(H().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:pa.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let d;o?d=new js(i,jo):d=new Sr(i,jo);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=N.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new js(a,jo):h=new Sr(a,jo);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(H().getBool("DEBUG")&&!H().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&H().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,u;if(s!=="complex64"&&H().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...eh(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=N.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;me(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Na().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new js(r,jo):c=new Sr(r,jo);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=Na().makeTensorFromTensorInfo(u),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return Pe(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!HC(n))throw H().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=v.sizeFromShape(t);if(H().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),c=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture.texture,...eh(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let s=H().getBool("WEBGL_PACK")&&a===!0,i=s?Mp(t):t,o=s?new zZ(i):new LZ(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=eJ){return H().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return Y7(e.shape,t)}packedUnaryOp(e,t,n){let a=new js(e.shape,t),r=this.compileAndRun(a,[e],n);return Na().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=L_(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(H().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Yk,e.dtype);let t=new Sr(e.shape,Yk),n=this.compileAndRun(t,[e]);return Na().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(s=>v.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){return Na().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new X7(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new D7(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[pi(e.shape),...ci(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[pi(t),...ci(t)],s=new B_(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=v.sizeFromShape(r),c=t[0]*t[1]*4;v.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Mp(r),o;a?o=new OZ(i):o=new PZ(i);let l=!0,u=[t!=null?t:eh(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Jp.DENSE){let g=s!=null?s:eh(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=H().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Qp(y.shape,g.shape)){let b=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),b.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=MZ(e,u,p),c=this.getAndSaveBinary(d,()=>DZ(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),H().get("ENGINE_COMPILE_ONLY")||RZ(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=H().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!H().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(H().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=P(()=>{if(!H().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=H().getBool("DEBUG");H().set("DEBUG",!1);let t=this.abs(xe(1e-8)).dataSync()[0];if(H().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Z7:J7}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=l_(n,o),t.texShape=p),r!=null){let d=Mp(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=Ou(p[0],p[1])),o?c=new BZ(d,f):c=new jk(d,f);let g=f?[m,h]:p,y=this.makeTensorInfo(g,a),b=this.texData.get(y.dataId);f?b.usage=pa.PIXELS:b.usage=pa.UPLOAD,b.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let x=[[m,h]],w=!0,I=this.runWebGLProgram(c,[y],a,x,w),T=this.texData.get(I.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,H().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=aJ(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Gv(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(V0(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=x_(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromTexture(e,t,n){let{texture:a,height:r,width:s,channels:i}=e,o=Na().backend;if(!o.gpgpu.gl.isTexture(a))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=o.writeTexture(a,t,n,r,s,i);return Na().makeTensorFromDataId(l,t,n,o)}};Rf.nextDataId=0;function aJ(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var rJ="4.0.0";function V_(){H().set("WEBGL_FORCE_F16_TEXTURES",!0)}Tc.isBrowser()&&Em("webgl",()=>new Rf,2);var sJ={forceHalfFloat:V_},J0=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,xl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},nd=`
  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;
`,ad=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=_n(r);let s="";if(a)if(r===0||v.sizeFromShape(this.outputShape)===1)s=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(s=`
          ${gt(r)} coords = getOutputCoords();
        `,r===1)this.enableShapeUniforms?s+=`
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `:s+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=wn("coords",r);this.enableShapeUniforms?s+=`
            bool nextRowOutOfBounds =
              (${i[r-2]} + 1) >= outShape[${r} - 2];
            bool nextColOutOfBounds =
              (${i[r-1]} + 1) >= outShape[${r} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `:s+=`
            bool nextRowOutOfBounds =
              (${i[r-2]} + 1) >= ${this.outputShape[r-2]};
            bool nextColOutOfBounds =
              (${i[r-1]} + 1) >= ${this.outputShape[r-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${s}

        setOutput(result);
      }
    `}};function na(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var iJ={kernelName:Di,backendName:"webgl",kernelFunc:na};function Is(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=na({inputs:{x:a},backend:n}),l=na({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var oJ={kernelName:Qh,backendName:"webgl",kernelFunc:Is},U_="return (a < 0.) ? b * a : a;",G_=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function lJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new ad(G_,r.shape,i.shape):new xl(U_,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var uJ={kernelName:Ri,backendName:"webgl",kernelFunc:lJ},H_="return (a < 0.) ? b * a : a;",j_=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function pJ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new ad(j_,a.shape,r.shape):new xl(H_,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var cJ={kernelName:qi,backendName:"webgl",kernelFunc:pJ},Uu="if (isnan(x)) return x;";function Ye({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=H().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new js(i.shape,t):p=new Sr(i.shape,e),o.runWebGLProgram(p,[i],l)}}function pn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,I]=x,T={dataId:w.dataId,dtype:w.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},E=new xl(e,l.shape,u.shape);return p.runWebGLProgram(E,[T,C],ma(w.dtype,I.dtype))}),b=Is({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),b}let d=s||ma(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(m):m,y=l.dtype==="string"?N.fromUint8ToStringArray(f):f,[b,x]=r(l.shape,u.shape,g,y,d),w=p.makeTensorInfo(x,d),I=p.texData.get(w.dataId);return I.values=b,w}let c=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new ad(t,l.shape,u.shape,n):h=new xl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function ec(e,t=!1){if(e==="linear")return t?G7:z7;if(e==="relu")return t?j7:B7;if(e==="elu")return t?H7:W7;if(e==="relu6")return t?q7:V7;if(e==="prelu")return t?j_:H_;if(e==="leakyrelu")return t?G_:U_;if(e==="sigmoid")return t?K7:U7;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var q_=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=_n(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${i}
        }`:l?f=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${i}
        }`:f=`vec4 activation(vec4 x) {
          ${i}
        }`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
      ${f}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${p}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${p}; i++) {
          int batchA = ${b};
          int batchB = ${x};
          vec4 a = getMatrixA(batchA, ${d});
          vec4 b = getMatrixB(batchB, ${c});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${h[0]} * ${m[0]});
          result += (${h[1]} * ${m[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${y}

        ${g}

        setOutput(result);
      }
    `}},Zk={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Jk=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${e}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}},Qk="return a * b;";function Q0(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=N.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new Jk(Zk.REAL,a.shape,r.shape),p=new Jk(Zk.IMAG,a.shape,r.shape),d=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Is({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,p]=u7(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new ad(Qk,a.shape,r.shape):i=new xl(Qk,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var dJ={kernelName:Ui,backendName:"webgl",kernelFunc:Q0};function hJ(e,t,n){let a=[pi(e.shape),...ci(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[pi(t),...ci(t)],i=new B_(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function de(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!Qp(r.shape,l)&&!(p.texture!==null&&Qp(p.shape,l))?hJ(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var mJ={kernelName:lu,backendName:"webgl",kernelFunc:de},eI=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${u}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};

        float sumValue = 0.0;

        for (int i = 0; i < ${i}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${l}
        }

        int inIdx = inOffset + ${i};
        if (${o===1}) {
          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);

          ${l}
        } else if (${o===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1), 0.0, 0.0);

          ${l}
        } else if (${o===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2), 0.0);

          ${l}
        }
        setOutput(sumValue);
      }
    `}},fJ=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,p=n%4,d=`
      if (${t==="sum"}) {
        sumValue += dot(values, ones);
      } else if (${t==="prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${o}(values, minMaxValue);
        if (${t==="min"} || ${t==="max"}) {
          minMaxValue = ${o}(values, minMaxValue);
          bvec4 isNaN = isnan(values);
          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
            minMaxValue = vec4(NAN);
          }
        }
      }
    `,c="vec4";t==="all"?(i="1.0",d=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,c="bvec4"):t==="any"&&(i="0.0",d=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,c="bvec4");let h="";r%n>0&&(h=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return initializationValue;
        }
      `),this.userCode=`
      const float initializationValue = ${i};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${h}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};

        vec4 minMaxValue = vec4(${i});
        float prodValue = 1.0;
        float sumValue = 0.0;
        float allValue = 1.0;
        float anyValue = 0.0;

        for (int i = 0; i < ${u}; i += 4) {
          int inIdx = inOffset + i;
          ${c} values = ${c}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${d}
        }

        int inIdx = inOffset + ${u};
        if (${p===1}) {
          ${c} values = ${c}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${d}
        } else if (${p===2}) {
          ${c} values = ${c}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${d}
        } else if (${p===3}) {
          ${c} values = ${c}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${d}
        }
        setOutput(${l});
      }
    `}};function gJ(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function ko(e,t,n,a){let r=gJ(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],p,d;n==="mean"?p=i===0?new eI({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new eI({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):p=new fJ({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),d=s,s=a.runWebGLProgram(p,[s],t),d.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(d)}return s}var yJ=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=gt(this.rank),r=bJ(t);this.userCode=`
    void main() {
      ${a} resRC = getOutputCoords();
      setOutput(getA(${r}));
    }
    `}};function bJ(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var xJ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=gt(this.rank),r=W_("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
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      vec4 result = vec4(0.);
      result[0] = ${l};
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      }
      --${r[this.rank-1]};
      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${l};
        if(${o}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function Mf(e,t,n){let a=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xJ(e.shape,t):new yJ(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function vJ(e,t,n,a){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Mf(e,l,a),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=N.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let m=v.sizeFromShape(c),f=v.sizeFromShape(e.shape)/m,g=de({inputs:{x:p},attrs:{shape:[f,m]},backend:a}),y=_m(e.dtype),b=ko(g,y,"sum",a),x=de({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),u&&a.disposeIntermediateTensorInfo(p),x}function Pf(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return vJ(r,s,i,n)}var wJ={kernelName:so,backendName:"webgl",kernelFunc:Pf};function In(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let p=0;p<l.length;p++)l[p]=r.shape[s[p]];let u;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=Z0(p,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let c=i.texData.get(u.dataId);c.values=d}else u=Mf(r,s,i);return u}var kJ={kernelName:Tr,backendName:"webgl",kernelFunc:In},K_=1e3;function Bh({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,d=n?e.shape[u-2]:e.shape[u-1],c=a?t.shape[p-1]:t.shape[p-2],h=n?e.shape[u-1]:e.shape[u-2],m=a?t.shape[p-2]:t.shape[p-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),b=v.sizeFromShape(g),x=Tu.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,m]);v.assert(d===c,()=>`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let w=n?[y,d,h]:[y,h,d],I=a?[b,m,c]:[b,c,m],T=de({inputs:{x:e},backend:r,attrs:{shape:w}}),C=de({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[T,C],$=Math.max(y,b),R=n?T.shape[1]:T.shape[2],F=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?ec(l,!0):null,U=F||S||M||B!=null,G;if((h===1||m===1)&&R>K_&&U===!1){let K=T,Z=C;n&&(K=In({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(Z=In({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=de({inputs:{x:K},backend:r,attrs:{shape:[$,R,1]}}),E.push(ae));let te=m===1?2:1,ie=Z;ee&&(ie=de({inputs:{x:Z},backend:r,attrs:{shape:[$,1,R]}}),E.push(ie));let oe=Q0({inputs:{a:ae,b:ie},backend:r});G=Pf({inputs:{x:oe},backend:r,attrs:{axis:te,keepDims:!0}}),E.push(oe)}else{let K=ma(e.dtype,t.dtype),Z=new q_(w,I,[$,h,m],n,a,F,B,S,M),J=[T,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));J.push(ee),E.push(ee)}G=r.runWebGLProgram(Z,J,K)}let q=de({inputs:{x:G},backend:r,attrs:{shape:x}});E.push(G);for(let K of E)r.disposeIntermediateTensorInfo(K);return q}function IJ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return Bh({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var SJ={kernelName:Js,backendName:"webgl",kernelFunc:IJ},tI="return abs(x);";function TJ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=L_(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new js(a.shape,tI):r=new Sr(a.shape,tI),n.runWebGLProgram(r,[a],a.dtype)}var NJ={kernelName:wl,backendName:"webgl",kernelFunc:TJ},CJ=Da+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,_J=Ye({opSnippet:CJ}),EJ={kernelName:kl,backendName:"webgl",kernelFunc:_J},$J=Da+`
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return log(x + sqrt(x * x - 1.0));`,AJ=Ye({opSnippet:$J}),FJ={kernelName:Il,backendName:"webgl",kernelFunc:AJ},nI="return a + b;",DJ=pn({opSnippet:nI,packedOpSnippet:nI,supportsComplex:!0,cpuKernelImpl:UZ}),RJ={kernelName:cs,backendName:"webgl",kernelFunc:DJ},MJ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

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

        vec4 result = ${a};
        setOutput(result);
      }
    `}};function ch(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return na({inputs:{x:a[0]},backend:n});if(a.length>H().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=ch({inputs:a.slice(0,o),backend:n}),u=ch({inputs:a.slice(o),backend:n});return ch({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ma(o,l)),s=a.map(o=>o.shape),i=H().getBool("WEBGL_PACK")?new PJ(a[0].shape,s):new MJ(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var OJ={kernelName:mi,backendName:"webgl",kernelFunc:ch};function LJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=de({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=ko(f,f.dtype,"all",n),y;if(i){let b=N.expandShapeToKeepDim(c,l);y=de({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=de({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var zJ={kernelName:Sl,backendName:"webgl",kernelFunc:LJ};function WJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=de({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=ko(f,f.dtype,"any",n),y;if(i){let b=N.expandShapeToKeepDim(c,l);y=de({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=de({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var BJ={kernelName:Tl,backendName:"webgl",kernelFunc:WJ},VJ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${a};

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

        for (int i = 0; i < ${a}; i++) {
          int inIdx = ${o};
          float candidate = getA(batch, inIdx);
          if (candidate ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}},UJ=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=gt(o),u=wn("coords",o),p,d;if(s===1){d=o+1;let C=gt(d);p=`
        ${C} sourceLocR = ${C}(${u.join()}, 0);
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        ${C} sourceLocG = ${C}(${u.join()}, 0);
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        ${C} sourceLocB = ${C}(${u.join()}, 0);
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        ${l} sourceLocR = coords;
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        ${l} sourceLocG = coords;
        ++${u[o-2]};
        ${l} sourceLocA = coords;
        --${u[o-1]};
        ${l} sourceLocB = coords;
        --${u[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,d),h="."+c[d-1],m=c.map(C=>"int "+C),f=wn("sourceLocR",d-1).concat("inIdx.r"),g=wn("sourceLocG",d-1).concat("inIdx.g"),y=wn("sourceLocB",d-1).concat("inIdx.b"),b=wn("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",w=a?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
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                             getBestIndicesAChannel(${b.join()})));`,I=`vec4(
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            hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,T=a?"":`
      float getBestIndicesAChannel(${m.join()}) {
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      }
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      void main() {
        ${l} coords = getOutputCoords();
        bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
        bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
        ${p}
        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
          sourceLocB${h}, sourceLocA${h}) * ${t};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${I};

        for (int i = 0; i < ${t}; i++) {
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          ${w}
          vec4 candidate = ${I};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
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          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
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                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
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          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function X_(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new VJ(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=X_(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function Y_(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=N.computeOptimalWindowSize(s),o=new UJ(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=Y_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function Z_(e,t,n,a){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!H().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=N.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(p),c=de({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=X_(e,c,a);s.push(h);let m=de({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return Y_(e,t,a)}function GJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=In({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=Z_(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var HJ={kernelName:fi,backendName:"webgl",kernelFunc:GJ};function jJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=In({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=Z_(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var qJ={kernelName:sc,backendName:"webgl",kernelFunc:jJ},KJ=Da+`
  if (abs(x) > 1.) {
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  return asin(x);
`,XJ=Ye({opSnippet:KJ}),YJ={kernelName:Nl,backendName:"webgl",kernelFunc:XJ},ZJ=Da+"return log(x + sqrt(x * x + 1.0));",JJ=Ye({opSnippet:ZJ}),QJ={kernelName:Cl,backendName:"webgl",kernelFunc:JJ},e9=Da+`
  return atan(x);
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  return atan(a, b);
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  `+nd+`
  return result;
`,s9=pn({opSnippet:a9,packedOpSnippet:r9}),i9={kernelName:$l,backendName:"webgl",kernelFunc:s9},o9=Da+`
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        const ivec2 pads = ivec2(${c}, ${h});

        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 >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${d};
                wC += ${u}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              float value = getX(batch, xR, xC, d);

              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${C} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${a?r?f:g:`wR * ${d} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let b="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let w=Math.floor(s/4)*4,I=s%4,T=`
      if (${m}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${b}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${i}, ${o});
      const ivec2 pads = ivec2(${c}, ${h});
      const float initializationValue = ${y};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];

        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${y});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${p};
            wR += ${l}) {
          int xR = xRCorner + wR;

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${w}; wC += 4) {
            int xC = xCCorner + wC * ${u};

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              getValue(batch, xR, xC + 2 * ${u}, d),
              getValue(batch, xR, xC + 3 * ${u}, d)
            );

            ${T}
          }

          int xC = xCCorner + ${w};
          if (${I===1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${T}
          } else if (${I===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              initializationValue,
              initializationValue
            );

            ${T}
          } else if (${I===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${u}, d),
              getValue(batch, xR, xC + 2 * ${u}, d),
              initializationValue
            );

            ${T}
          }
        }
        setOutput(${x});
      }
    `}},e1=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",x="0.0";if(b||(x="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${i}, ${o}, ${l});
        const ivec3 pads = ivec3(${f}, ${g}, ${y});

        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 < ${c};
              wD += ${u}) {
            int xD = xDCorner + wD;

            if (xD < 0 || xD >= ${e.inDepth}) {
              continue;
            }

            for (int wR = 0; wR < ${h};
                wR += ${p}) {
              int xR = xRCorner + wR;

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int wC = 0; wC < ${m};
                  wC += ${d}) {
                int xC = xCCorner + wC;

                if (xC < 0 || xC >= ${e.inWidth}) {
                  continue;
                }

                float value = getX(batch, xD, xR, xC, ch);

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${$} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} +
                      wR * ${m} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let w="max",I=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(I="avgValue / count");let T=Math.floor(s/4)*4,C=s%4,E=`
      if (${b}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${w}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${i}, ${o}, ${l});
      const ivec3 pads = ivec3(${f}, ${g}, ${y});
      const float initializationValue = ${x};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${x});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${c};
            wD += ${u}) {
          int xD = xDCorner + wD;

          if (xD < 0 || xD >= ${e.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${h};
            wR += ${p}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${T}; wC += 4) {
              int xC = xCCorner + wC * ${d};

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${d}, ch),
                getValue(batch, xD, xR, xC + 2 * ${d}, ch),
                getValue(batch, xD, xR, xC + 3 * ${d}, ch)
              );

              ${E}
            }

            int xC = xCCorner + ${T};
            if (${C===1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${E}
            } else if (${C===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${d}, ch),
                initializationValue,
                initializationValue
              );

              ${E}
            } else if (${C===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${d}, ch),
                getValue(batch, xD, xR, xC + 2 * ${d}, ch),
                initializationValue
              );

              ${E}
            }
          }
          setOutput(${I});
        }
      }
    `}};function p9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Lu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new tc(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var c9={kernelName:gi,backendName:"webgl",kernelFunc:p9};function d9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new e1(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var h9={kernelName:ic,backendName:"webgl",kernelFunc:d9},m9=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
      const ivec2 pads = ivec2(${u}, ${p});
      const float avgMultiplier = float(${d});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${o};
            wR += ${s}) {
          float dyR = float(dyRCorner + wR) / ${a}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${l};
            wC+= ${i}) {
            float dyC = float(dyCCorner + wC) / ${r}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}},f9=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
      const ivec3 pads = ivec3(${h}, ${m}, ${f});
      const float avgMultiplier = float(${g});

      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 += ${o}) {
          float dyD = float(dyDCorner + wD) / ${r}.0;

          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${d};
              wR += ${l}) {
            float dyR = float(dyRCorner + wR) / ${s}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${c};
                wC += ${u}) {
              float dyC = float(dyCCorner + wC) / ${i}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function g9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new f9(c);return n.runWebGLProgram(h,[r],i.dtype)}var y9={kernelName:Yh,backendName:"webgl",kernelFunc:g9};function b9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Lu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=new m9(p);return n.runWebGLProgram(d,[r],i.dtype)}var x9={kernelName:Xh,backendName:"webgl",kernelFunc:b9};function v9(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Bh({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var w9={kernelName:yi,backendName:"webgl",kernelFunc:v9},k9=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${i};
        float scale = ${o};
        float inv = scale * inversesqrt(variance + float(${s}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}},I9=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${i};
        vec4 scale = ${o};

        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();

        vec4 inv = scale * inversesqrt(variance + vec4(${s}));

        setOutput((x - mean) * inv + offset);
      }
    `}},S9=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=H().getBool("WEBGL_PACK_NORMALIZATION")?new I9(a.shape,r.shape,s.shape,p,d,l):new k9(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},T9={kernelName:Ai,backendName:"webgl",kernelFunc:S9},N9=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=C9(this.rank),a,r=e.map((s,i)=>`sourceLoc.${px[i]} = start[${i}] + coords.${px[i]};`);a=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${r.join(`
`)}
      `,this.userCode=`
      void main() {
        ${a}
        setOutput(getSource(${n}));
      }
    `}},px=["x","y","z","w","u","v"];function C9(e){if(e===1)return"sourceLoc";if(e<=6)return px.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var _9=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),n=wn("coords",this.rank),a=wn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
      result.x = ${s};
      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
        ++${a[this.rank-1]};
        result.y = ${s};
        --${a[this.rank-1]};
      }
    `,o=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
        ++${a[this.rank-2]};
        result.z = ${s};
        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
          ++${a[this.rank-1]};
          result.w = ${s};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((u,p)=>`start[${p}]`).join()});`:e.map((u,p)=>`${a[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${l}
        vec4 result = vec4(0.);
        ${i}
        ${o}
        setOutput(result);
      }
    `}};function E9(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=jt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Gu(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=jt.parseSliceParams(r,s,i);if(jt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=v7(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=jt.isSliceContinous(r.shape,o,l);if(u||!p){let d=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _9(l):new N9(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),E9(r,o,l,n)}var $9={kernelName:du,backendName:"webgl",kernelFunc:Gu},A9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,x)=>b*x),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=[],m=de({inputs:{x:r},backend:n,attrs:{shape:l}}),f=In({inputs:{x:m},backend:n,attrs:{perm:u}}),g=de({inputs:{x:f},backend:n,attrs:{shape:p}}),y=Gu({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},F9={kernelName:Al,backendName:"webgl",kernelFunc:A9};function D9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=O_(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var R9={kernelName:Zh,backendName:"webgl",kernelFunc:D9};function M9(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var P9={kernelName:Jh,backendName:"webgl",kernelFunc:M9},O9="return float(a != b);",J_=pn({opSnippet:O9,cpuKernelImpl:c7,dtype:"bool"}),L9={kernelName:tu,backendName:"webgl",kernelFunc:J_};function rd(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return na({inputs:{x:r.complexTensorInfos.real},backend:n})}var z9={kernelName:wm,backendName:"webgl",kernelFunc:rd},W9="return float(int(x));";function B9(e,t){let n=new Sr(e.shape,W9),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function cx(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return na({inputs:{x:r},backend:n});let i=It(r.shape),o=cx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Is({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=rd({inputs:{input:r},backend:n}),o=cx({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=na({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=HZ(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return B9(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=J_({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var V9={kernelName:bi,backendName:"webgl",kernelFunc:cx},aI="return ceil(x);",U9=Ye({opSnippet:aI,packedOpSnippet:aI,cpuKernelImpl:jZ}),G9={kernelName:xi,backendName:"webgl",kernelFunc:U9},H9=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`

      void main() {
        float value = getAAtOutCoords();
        if (isnan(value)) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, minVal, maxVal));
      }
    `}},j9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
      void main() {
        vec4 value = getAAtOutCoords();

        if (any(isnan(value))) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}};function q9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;H().getBool("WEBGL_PACK_CLIP")?o=new j9(r.shape):o=new H9(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var K9={kernelName:ds,backendName:"webgl",kernelFunc:q9},X9=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
      void main() {
        float re = abs(getRealAtOutCoords());
        float im = abs(getImagAtOutCoords());
        float mx = max(re, im);

        // sadly the length function in glsl is not underflow-safe
        // (at least not on Intel GPUs). So the safe solution is
        // to ensure underflow-safety in all cases.
        setOutput(
          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
        );
      }
    `}};function rI(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Y9(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new X9(a.shape),i=[rI(a,r.complexTensorInfos.real),rI(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var Z9={kernelName:oc,backendName:"webgl",kernelFunc:Y9},J9=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${n.join(`
        `)}
      }
    `}},Q9=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=gt(a),s=wn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],u=i.slice(-2),p=i.join(),d=`if (${l} < ${o[0]}) {
        return getChannel(
            getT0(${p}), vec2(${u.join()}));
        }`;for(let m=1;m<o.length;m++){let f=o[m-1];d+=`
        if (${l} < ${o[m]}  && ${l} >= ${o[m-1]}) {
          return getChannel(
            getT${m}(${nh(i,l,f)}),
            vec2(${nh(u,l,f)}));
        }`}let c=o.length,h=o[o.length-1];d+=`
        return getChannel(
          getT${c}(${nh(i,l,h)}),
          vec2(${nh(u,l,h)}));`,this.userCode=`
      float getValue(${i.map(m=>"int "+m)}) {
        ${d}
      }

      void main() {
        ${r} coords = getOutputCoords();
        vec4 result = vec4(getValue(${s}), 0., 0., 0.);

        ${s[a-1]} = ${s[a-1]} + 1;
        if (${s[a-1]} < ${n[a-1]}) {
          result.g = getValue(${s});
        }

        ${s[a-2]} = ${s[a-2]} + 1;
        if (${s[a-2]} < ${n[a-2]}) {
          result.a = getValue(${s});
        }

        ${s[a-1]} = ${s[a-1]} - 1;
        if (${s[a-2]} < ${n[a-2]} &&
            ${s[a-1]} < ${n[a-1]}) {
          result.b = getValue(${s});
        }
        setOutput(result);
      }
    `}};function nh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Of(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return na({inputs:{x:r.complexTensorInfos.imag},backend:n})}var eQ={kernelName:cm,backendName:"webgl",kernelFunc:Of};function Pp(e,t,n){let a=e[0].dtype;if(a==="complex64"){let d=e.map(g=>rd({inputs:{input:g},backend:n})),c=e.map(g=>Of({inputs:{input:g},backend:n})),h=Pp(d,t,n),m=Pp(c,t,n),f=Is({inputs:{real:h,imag:m},backend:n});return d.forEach(g=>n.disposeIntermediateTensorInfo(g)),c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let d=e.map(b=>{let x=[-1,v.sizeFromShape(b.shape.slice(t))];return de({inputs:{x:b},backend:n,attrs:{shape:x}})}),c=d.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),h=N.computeOutShape(d.map(b=>b.shape),1),m=d[0].shape[0]===1,f=qZ(c,h,a,m),g=N.computeOutShape(e.map(b=>b.shape),t),y=n.makeTensorInfo(g,a,f);return d.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}let s=H().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(e.length>s){let d=[];for(let h=0;h<e.length;h+=s){let m=e.slice(h,h+s);d.push(Pp(m,t,n))}let c=Pp(d,t,n);for(let h of d)n.disposeIntermediateTensorInfo(h);return c}if(H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new Q9(e.map(c=>c.shape),t);return n.runWebGLProgram(d,e,a)}let{tensors2D:i,outShape:o}=tQ(e,t,n),l=new J9(i.map(d=>d.shape)),u=n.runWebGLProgram(l,i,a);i.forEach(d=>n.disposeIntermediateTensorInfo(d));let p=de({inputs:{x:u},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(u),p}function tQ(e,t,n){let a=N.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>de({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function Q_(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?na({inputs:{x:l[0]},backend:n}):Pp(l,s,n)}var nQ={kernelName:Fl,backendName:"webgl",kernelFunc:Q_},eE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,b=f?3:1,x="",w="";n&&(a?x=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?x=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:x=`
          float activation(float x) {
            ${n}
          }
        `,w="result = activation(result);");let I=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${x}

      const ivec2 strides = ivec2(${o}, ${l});
      const ivec2 pads = ivec2(${s}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${b}];

        ivec2 xRCCorner =
            ivec2(coords[${g}], coords[${y}]) * 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 < ${d}; wR++) {
          int xR = xRCorner + wR * ${u};

          if (xR < 0 || xR >= ${e.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${c}; wC++) {
            int xC = xCCorner + wC * ${p};

            if (xC < 0 || xC >= ${e.inWidth}) {
              continue;
            }

            for (int d1 = 0; d1 < ${h}; 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 (${f}) {
                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 (${m===1}) {

              if (${f}) {
                dotProd +=
                    getX(batch, xR, xC, ${h}) *
                    getW(wR, wC, ${h}, d2);
              } else {
                dotProd +=
                    getX(batch, ${h}, xR, xC) *
                    getW(wR, wC, ${h}, d2);
              }

            } else if (${m===2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${h}, d2),
                getW(wR, wC, ${h} + 1, d2)
              );

              if (${f}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${h}),
                  getX(batch, xR, xC, ${h} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${h}, xR, xC),
                  getX(batch, ${h} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            } else if (${m===3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${h}, d2),
                getW(wR, wC, ${h} + 1, d2),
                getW(wR, wC, ${h} + 2, d2)
              );

              if (${f}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${h}),
                  getX(batch, xR, xC, ${h} + 1),
                  getX(batch, xR, xC, ${h} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${h}, xR, xC),
                  getX(batch, ${h} + 1, xR, xC),
                  getX(batch, ${h} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${I}
        ${w}
        setOutput(result);
      }
    `}},aQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${r}, ${s}, ${i});
      const ivec3 pads = ivec3(${t}, ${n}, ${a});

      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 * ${o};

          if (xF < 0 || xF >= ${e.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${d}; wR++) {
            int xR = xRCorner + wR * ${l};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${c}; wC++) {
              int xC = xCCorner + wC * ${u};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              for (int d1 = 0; d1 < ${h}; 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 (${m===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${h}) *
                  getW(wF, wR, wC, ${h}, d2);
              } else if (${m===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${h}),
                  getX(batch, xF, xR, xC, ${h} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${h}, d2),
                  getW(wF, wR, wC, ${h} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${m===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${h}),
                  getX(batch, xF, xR, xC, ${h} + 1),
                  getX(batch, xF, xR, xC, ${h} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${h}, d2),
                  getW(wF, wR, wC, ${h} + 1, d2),
                  getW(wF, wR, wC, ${h} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},tE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=_n(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f<u;f++)d+=`
           vec4 xTexelC${f*2};
           int xTexelC${f*2}Ready;
           vec4 xTexelC${f*2+1};
           int xTexelC${f*2+1}Ready;
           vec4 xC${f};`;d+=`
     for (int r = 0; r < ${l}; r++) {
      for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {
       `;for(let f=0;f<u;f++)d+=`
           xTexelC${f*2} = vec4(0.0);
           xTexelC${f*2}Ready = 0;
           xTexelC${f*2+1} = vec4(0.0);
           xTexelC${f*2+1}Ready = 0;
           xC${f} = vec4(0.0);`;d+=`
         xR = xRCorner + r * dilations[0];
         if (xR >=0 && xR < inDims[0]) {
       `;for(let f=0;f<(p+1)/2;f++){let g=f*2;if(d+=`
           xC = xCCorner + ${g*o};
           `,i===1){if(g<u&&(s%2===1?(d+=`
                 xCOffset = xC + 1;
                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) {
                   xTexelC${g} = 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${g}.zw = vec2(0.0);
                   }
                   xTexelC${g}Ready = 1;
                 }
               `,o===1&&g>0?d+=`
                 xC${g} = vec4(xTexelC${g-2}.zw, xTexelC${g}.xy);
                 `:d+=`
                   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${g} = vec4(previous.zw, xTexelC${g}.xy);
                   } else {
                     xC${g} = vec4(0.0, 0.0, xTexelC${g}.xy);
                   }
                   `):d+=`
                 if (xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) {
                   xTexelC${g} = getX(batch, xR, xC, d1);
                   if (xC + 1 >= inDims[1]) {
                     xTexelC${g}.zw = vec2(0.0);
                   }
                   xTexelC${g}Ready = 1;
                 }

                 xC${g} = xTexelC${g};
                 `,g+1<u)){let y=s%2===0?v.nearestLargerEven(o):o;o%2===0&&s%2===1||o%2!==0&&s%2!==1?(d+=`
                   xCOffset = xC + imod(pads[1], 2) + ${y};

                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) {
                     xTexelC${g+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${g+1}.zw = vec2(0.0);
                     }
                     xTexelC${g+1}Ready = 1;
                   }
                   `,o>1?d+=`
                     xCOffset -= 2;
                     if (xCOffset >= 0 && xCOffset < inDims[1]) {
                      previous = getX(batch, xR, xCOffset, d1);
                      xC${g+1} = vec4(previous.zw, xTexelC${g+1}.xy);
                     } else {
                      xC${g+1} = vec4(0.0, 0.0, xTexelC${g+1}.xy);
                     }
                     `:d+=`
                     xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.xy);
                     `):y===1?d+=`
                     xC${g+1} = xTexelC${g};
                     `:d+=`
                     xCOffset = xC + ${y};

                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) {
                       xTexelC${g+1} = getX(batch, xR, xCOffset, d1);
                       if (xCOffset + 1 >= inDims[1]) {
                         xTexelC${g+1}.zw = vec2(0.0);
                       }
                       xTexelC${g+1}Ready = 1;
                     }

                     xC${g+1} = xTexelC${g+1};
                     `}}else g<u&&(s%2===1?(d+=`
                 xCOffset = xC + 1 - strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) {
                   xTexelC${g} = 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${g}.zw = vec2(0.0);
                   }
                   xTexelC${g}Ready = 1;
                 }

                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${g+1}Ready == 0) {
                   xTexelC${g+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${g+1}.zw = vec2(0.0);
                   }
                   xTexelC${g+1}Ready = 1;
                 }

                 xC${g} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw);
               `,g+1<u&&(d+=`
                   final = vec4(0.0);
                   xCOffset = xC + 1 + strides[1];
                   if(xCOffset >= 0 && xCOffset < inDims[1]) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
                   xC${g+1} = vec4(xTexelC${g+1}.xy, final.xy);
                 `)):(d+=`
                 if(xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) {
                   xTexelC${g} = getX(batch, xR, xC, d1);
                   if (xC + 1 >= inDims[1]) {
                     xTexelC${g}.zw = vec2(0.0);
                   }
                   xTexelC${g}Ready = 1;
                 }

                 xCOffset = xC + strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) {
                   xTexelC${g+1} = getX(batch, xR, xCOffset, d1);
                   if (xCOffset + 1 >= inDims[1]) {
                     xTexelC${g+1}.zw = vec2(0.);
                   }
                   xTexelC${g+1}Ready = 1;
                 }

                 xC${g} = vec4(
                   xTexelC${g}.xy, xTexelC${g+1}.xy);
               `,g+1<u&&(d+=`
                   xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw);
                 `)));g<u&&(d+=`
             wTexel = getW(r, ${g}, d1, d2);
             dotProd += xC${g}.xxzz * vec4(wTexel.xy, wTexel.xy);
             if(d1 + 1 < ${e.inChannels}) {
               dotProd += xC${g}.yyww * vec4(wTexel.zw, wTexel.zw);
             }
           `,g+1<u&&(d+=`
               wTexel = getW(r, ${g+1}, d1, d2);
               dotProd += xC${g+1}.xxzz * vec4(wTexel.xy, wTexel.xy);
               if(d1 + 1 < ${e.inChannels}) {
                 dotProd += xC${g+1}.yyww * vec4(wTexel.zw, wTexel.zw);
               }
             `))}d+=`
     }
   `,d+=`
     }
   `,d+=`
     }
   `;let c="",h="";n&&(a?c=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?c=`vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
           ${n}
         }`:c=`vec4 activation(vec4 x) {
           ${n}
         }`,h="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${c}

       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;

         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
         vec4 dotProd = vec4(0.000000000000001);

         ${d}

         vec4 result = dotProd - vec4(0.000000000000001);
         ${m}
         ${h}
         setOutput(result);
       }
     `}},rQ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length);let{dataFormat:n}=t,a=Cn(),r=n==="channelsLast",s=r?1:2,i=r?2:3,o=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,l="";for(let u=0;u<=1;u++)for(let p=0;p<=1;p++)l+=`
          blockIndex = rc.z + ${p};
          pos = rc.y + ${u};

          ${o}
            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);

            if(d0 < inputShape[${s}] && d0 >= 0) {
              // Use custom imod instead mod. On Intel GPU, mod may generate
              // unexpected value.
              // https://github.com/tensorflow/tfjs/issues/5447
              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
                  inChannels);

              if(d1 < inputShape[${i}] && d1 >= 0) {

                ch = imod(pos, inChannels);

                if (${r}) {
                  innerDims = vec2(d1, ch);
                  result[${u*2+p}] = getChannel(
                    getA(rc.x, d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${u*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}

        ${a.output} = result;
      }
    `}};function Vh(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function nE({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(s!=null){let b=Vh(s.shape,h);b!=null&&(s=de({inputs:{x:s},backend:a,attrs:{shape:b}}),y.push(s))}if(r!=null){let b=Vh(r.shape,h);b!=null&&(r=de({inputs:{x:r},backend:a,attrs:{shape:b}}),y.push(r))}if(!((d===1||c===1)&&p>K_)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},w=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Qp(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=de({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(I);let T=Bh({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=w,C.shape=n.outShape,g=na({inputs:{x:T},backend:a}),g.shape=n.outShape,y.push(T)}else{let b=n.outHeight*n.outWidth,x=de({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,b,n.inChannels]:[n.batchSize,n.inChannels,b]}}),w=de({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Bh({a:h?x:w,b:h?w:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=de({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),y.push(x),y.push(w),y.push(I)}for(let b of y)a.disposeIntermediateTensorInfo(b);return g}function aE({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,y=[n.batchSize,f,g],b=!0,x=!1,w=[];if(s!=null){let K=Vh(s.shape,m);K!=null&&(s=de({inputs:{x:s},backend:a,attrs:{shape:K}}),w.push(s))}if(r!=null){let K=Vh(r.shape,m);K!=null&&(r=de({inputs:{x:r},backend:a,attrs:{shape:K}}),w.push(r))}let I=de({inputs:{x:t},backend:a,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});w.push(I);let T=new rQ(y,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=a.runWebGLProgram(T,[e],"float32",C),$=de({inputs:{x:E},backend:a,attrs:{shape:y}});w.push(E),w.push($);let R=r!=null,F=s!=null,S=o==="leakyrelu",M=o?ec(o,!0):null,B=new q_(m?$.shape:I.shape,m?I.shape:$.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],b,x,R,M,F,S),U=m?[$,I]:[I,$];if(r&&U.push(r),F&&U.push(s),S){let K=a.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));U.push(K),w.push(K)}let G=a.runWebGLProgram(B,U,"float32"),q=de({inputs:{x:G},backend:a,attrs:{shape:n.outShape}});w.push(G);for(let K of w)a.disposeIntermediateTensorInfo(K);return q}function sQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=nE({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&H().getBool("WEBGL_EXP_CONV")){let f=new tE(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(H().getBool("WEBGL_CONV_IM2COL"))h=aE({x:r,filter:s,convInfo:c,backend:n});else{let f=new eE(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=de({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var iQ={kernelName:vi,backendName:"webgl",kernelFunc:sQ},oQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${a};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${r};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              if (${s}) {
                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);
      }
    `}},lQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${o});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${p}];

        ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / ${a}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${t} - 1 - wR;

          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${r}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${n} - 1 - wC;

            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {

              if (${s}) {
                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);
      }
    `}},uQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yF = 0; yF < ${e.outDepth}; yF++) {
            int xF = wF + yF * ${t} - ${r};

            if (xF < 0 || xF >= ${e.inDepth}) {
              continue;
            }

            for (int yR = 0; yR < ${e.outHeight}; yR++) {
              int xR = wR + yR * ${n} - ${s};

              if (xR < 0 || xR >= ${e.inHeight}) {
                continue;
              }

              for (int yC = 0; yC < ${e.outWidth}; yC++) {
                int xC = wC + yC * ${a} - ${i};

                if (xC < 0 || xC >= ${e.inWidth}) {
                  continue;
                }

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},pQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${o}, ${l}, ${u});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.u;


        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyFCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        float dotProd = 0.0;
        for (int wF = 0; wF < ${t}; wF++) {
          float dyF = float(dyFCorner + wF) / ${r}.0;

          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
            continue;
          }
          int idyF = int(dyF);

          int wFPerm = ${t} - 1 - wF;

          for (int wR = 0; wR < ${n}; wR++) {
            float dyR = float(dyRCorner + wR) / ${s}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
              fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            int wRPerm = ${n} - 1 - wR;

            for (int wC = 0; wC < ${a}; wC++) {
              float dyC = float(dyCCorner + wC) / ${i}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              int wCPerm = ${a} - 1 - wC;

              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function cQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new oQ(c);return n.runWebGLProgram(h,[r,s],"float32")}var dQ={kernelName:em,backendName:"webgl",kernelFunc:cQ};function hQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(u),c=N.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d),h=new lQ(c);return n.runWebGLProgram(h,[r,s],"float32")}var mQ={kernelName:wi,backendName:"webgl",kernelFunc:hQ};function fQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new aQ(u);return n.runWebGLProgram(p,[r,s],"float32")}var gQ={kernelName:lc,backendName:"webgl",kernelFunc:fQ};function yQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=N.computeConv3DInfo(r.shape,l,i,1,o),p=new uQ(u);return n.runWebGLProgram(p,[r,s],"float32")}var bQ={kernelName:tm,backendName:"webgl",kernelFunc:yQ};function xQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=N.computeConv3DInfo(l,s.shape,o,1,i),p=new pQ(u);return n.runWebGLProgram(p,[r,s],"float32")}var vQ={kernelName:nm,backendName:"webgl",kernelFunc:xQ},wQ=Uu+`
  return cos(x);
`,kQ=Ye({opSnippet:wQ}),IQ={kernelName:ki,backendName:"webgl",kernelFunc:kQ},SQ=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,TQ=Ye({opSnippet:SQ}),NQ={kernelName:Ii,backendName:"webgl",kernelFunc:TQ},CQ=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,w]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
      const float height_ratio = float(${f});
      const float width_ratio = float(${b});
      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 >= ${s}) {
          return;
        }

        float height_scale = ${g};
        float width_scale = ${x};

        float in_y = ${y};
        if( in_y < 0.0 || in_y > ${h} ) {
          setOutput(float(${r}));
          return;
        }
        float in_x = ${w};
        if( in_x < 0.0 || in_x > ${m} ) {
          setOutput(float(${r}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${c} == 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);
        }
      }
    `}},_Q=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new CQ(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},EQ={kernelName:Rl,backendName:"webgl",kernelFunc:_Q},nc;(function(e){e.Prod="*",e.Sum="+"})(nc||(nc={}));var sI=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===nc.Prod?"1.0":"0.0",i=n?s:`getX(${iI(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=`
      void main() {
        ${gt(r)} coords = getOutputCoords();
        int end = ${oI(r,"coords",this.op)};
        float val = ${i};
        int pow2 = int(pow(2.0, index));
        if (${l}) {
          int idx = ${u};
          ${oI(r,"coords",this.op)} = idx;
          val ${this.op}= getX(${iI(r,"coords",this.op)});
        }
        setOutput(val);
      }
    `}};function iI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function oI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function rE(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=In({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=na({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new sI(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new sI(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=In({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function $Q(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return rE(nc.Prod,r,n,s,i,o)}var AQ={kernelName:Dl,backendName:"webgl",kernelFunc:$Q};function FQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return rE(nc.Sum,r,n,s,i,o)}var DQ={kernelName:Si,backendName:"webgl",kernelFunc:FQ};function RQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=O_(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=GZ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var MQ={kernelName:am,backendName:"webgl",kernelFunc:RQ},PQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

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

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function OQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new PQ(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var LQ={kernelName:Ml,backendName:"webgl",kernelFunc:OQ},sE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=_n(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?l=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:l=`
          float activation(float x) {
            ${n}
          }
        `,u="result = activation(result);");let p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&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 / ${o};
        int q = d2 - d1 * ${o};

        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 < ${s}; wR++) {
          int xR = xRCorner + wR * dilations[0];

          if (xR < 0 || xR >= inDims[0]) {
            continue;
          }

          for (int wC = 0; wC < ${i}; 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}
        ${u}
        setOutput(result);
      }
    `}},iE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=_n(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<p;g++)c+=`
          vec4 xTexelC${g*2};
          int xTexelC${g*2}Ready;
          vec4 xTexelC${g*2+1};
          int xTexelC${g*2+1}Ready;
          vec4 xC${g};`;c+=`
    for (int r = 0; r < ${u}; r++) {
      `;for(let g=0;g<p;g++)c+=`
          xTexelC${g*2} = vec4(0.0);
          xTexelC${g*2}Ready = 0;
          xTexelC${g*2+1} = vec4(0.0);
          xTexelC${g*2+1}Ready = 0;
          xC${g} = vec4(0.0);`;c+=`
        xR = xRCorner + r * dilations[0];
        if (xR >=0 && xR < inDims[0]) {
      `;for(let g=0;g<(d+1)/2;g++){let y=g*2;if(c+=`
          xC = xCCorner + ${y*l};
          `,o===1){if(y<p&&(i%2===1?(c+=`
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = 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${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }
              `,l===1&&y>0?c+=`
                xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
                `:c+=`
                  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${y} = vec4(previous.zw, xTexelC${y}.xy);
                  } else {
                    xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
                  }
                  `):c+=`
                if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }

                xC${y} = xTexelC${y};
                `,y+1<p)){let b=i%2===0?v.nearestLargerEven(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(c+=`
                  xCOffset = xC + imod(pads[1], 2) + ${b};

                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                    xTexelC${y+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${y+1}.zw = vec2(0.0);
                    }
                    xTexelC${y+1}Ready = 1;
                  }
                  `,l>1?c+=`
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < inDims[1]) {
                     previous = getX(batch, xR, xCOffset, d1);
                     xC${y+1} = vec4(previous.zw, xTexelC${y+1}.xy);
                    } else {
                     xC${y+1} = vec4(0.0, 0.0, xTexelC${y+1}.xy);
                    }
                    `:c+=`
                    xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
                    `):b===1?c+=`
                    xC${y+1} = xTexelC${y};
                    `:c+=`
                    xCOffset = xC + ${b};

                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                      xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
                      if (xCOffset + 1 >= inDims[1]) {
                        xTexelC${y+1}.zw = vec2(0.0);
                      }
                      xTexelC${y+1}Ready = 1;
                    }

                    xC${y+1} = xTexelC${y+1};
                    `}}else y<p&&(i%2===1?(c+=`
                xCOffset = xC + 1 - strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = 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${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }

                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
                  xTexelC${y+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${y+1}.zw = vec2(0.0);
                  }
                  xTexelC${y+1}Ready = 1;
                }

                xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
              `,y+1<p&&(c+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + strides[1];
                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
                `)):(c+=`
                if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
                  xTexelC${y} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${y}.zw = vec2(0.0);
                  }
                  xTexelC${y}Ready = 1;
                }

                xCOffset = xC + strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
                  xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${y+1}.zw = vec2(0.);
                  }
                  xTexelC${y+1}Ready = 1;
                }

                xC${y} = vec4(
                  xTexelC${y}.xy, xTexelC${y+1}.xy);
              `,y+1<p&&(c+=`
                  xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
                `)));y<p&&(c+=`
            wTexel = getW(r, ${y}, d1, q);
            dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
          `,y+1<p&&(c+=`
              wTexel = getW(r, ${y+1}, d1, q);
              dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
            `))}c+=`
    }
  `,c+=`
      }
    `;let h="",m="";n&&(a?h=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:r?h=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:h=`vec4 activation(vec4 x) {
          ${n}
        }`,m="result = activation(result);");let f=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${h}

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${s};
        int q = d2 - d1 * ${s};
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
        vec4 dotProd = vec4(0.000000000000001);

        ${c}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${f}
        ${m}
        setOutput(result);
      }
    `}};function zQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,p=l;p==null&&(p=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=N.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;H().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new iE(d):c=new sE(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var WQ={kernelName:Ti,backendName:"webgl",kernelFunc:zQ},BQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${s} + dm;

        float dotProd = 0.0;

        // TO DO: Vec4 over the batch size
        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${a};

            if (xR < 0 || xR >= ${e.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${r};

              if (xC < 0 || xC >= ${e.inWidth}) {
                continue;
              }

              float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);
            }
          }
        }
        setOutput(dotProd);
      }
    `}},VQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
      const ivec2 pads = ivec2(${s}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
        ivec2 dyCorner = coords.yz - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        float dotProd = 0.0;

        for (int wR = 0; wR < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / ${a}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${t} - 1 - wR;

          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${r}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${n} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${o}; dm++) {
              int d2 = d1 * ${o} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function UQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=N.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new BQ(d);return n.runWebGLProgram(c,[r,s],"float32")}var GQ={kernelName:rm,backendName:"webgl",kernelFunc:UQ};function HQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=N.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new VQ(d);return n.runWebGLProgram(c,[r,s],"float32")}var jQ={kernelName:sm,backendName:"webgl",kernelFunc:HQ},qQ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function KQ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=v.sizeFromShape(a.shape),i=de({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new qQ(s),l=n.runWebGLProgram(o,[i],i.dtype),u=de({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var XQ={kernelName:im,backendName:"webgl",kernelFunc:KQ},YQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=`
      const ivec2 strides = ivec2(${r}, ${s});
      const ivec2 pads = ivec2(${p}, ${d});
      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 < ${i}; h++) {
          int hIn = hBeg + h * ${l};

          if (hIn >= 0 && hIn < ${t}) {
            for (int w = 0; w < ${o}; w++) {
              int wIn = wBeg + w * ${u};

              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 ZQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new YQ(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=de({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var JQ={kernelName:uc,backendName:"webgl",kernelFunc:ZQ};function QQ(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(r,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=N.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f<d;++f){for(let g of p[f]){let{permutationIndices:y,expandDims:b}=N.getEinsumPermutation(h,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=In({inputs:{x:s[g]},backend:n,attrs:{perm:y}}),m.push(x));let w=x.shape.slice();for(let I=0;I<b.length;++I)w.splice(b[I],0,1);v.arraysEqual(x.shape,w)||(x=de({inputs:{x},backend:n,attrs:{shape:w}}),m.push(x)),c===null?c=x:(c=Q0({inputs:{a:x,b:c},backend:n}),m.push(c))}f<d-1&&(u[f]>=0&&(c=Pf({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var eee={kernelName:om,backendName:"webgl",kernelFunc:QQ},tee="return (x >= 0.0) ? x : (exp(x) - 1.0);",nee=`
  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;
`,aee=Ye({opSnippet:tee,packedOpSnippet:nee}),ree={kernelName:Ci,backendName:"webgl",kernelFunc:aee},see="return (b >= 1.0) ? a : a * (b + 1.0);",iee=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,oee=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new ad(iee,a.shape,r.shape):new xl(see,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},lee={kernelName:lm,backendName:"webgl",kernelFunc:oee},uee=`
  return vec4(equal(a, b));
`,pee="return float(a == b);",cee=pn({opSnippet:pee,packedOpSnippet:uee,dtype:"bool",cpuKernelImpl:KZ}),dee={kernelName:Ol,backendName:"webgl",kernelFunc:cee},hee=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${N.ERF_P};
  float a1 = ${N.ERF_A1};
  float a2 = ${N.ERF_A2};
  float a3 = ${N.ERF_A3};
  float a4 = ${N.ERF_A4};
  float a5 = ${N.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));
`,mee=Ye({opSnippet:hee}),fee={kernelName:Pl,backendName:"webgl",kernelFunc:mee},gee=Uu+`
  return exp(x);
`,yee=`
  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;
`,oE=Ye({opSnippet:gee,packedOpSnippet:yee,cpuKernelImpl:XZ,dtype:"float32"}),bee={kernelName:_i,backendName:"webgl",kernelFunc:oE};function dx(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),de({inputs:{x:s},backend:a,attrs:{shape:o}})}var xee={kernelName:Ll,backendName:"webgl",kernelFunc:dx},lI="return exp(x) - 1.0;",vee=Ye({opSnippet:lI,packedOpSnippet:lI,cpuKernelImpl:YZ}),wee={kernelName:zl,backendName:"webgl",kernelFunc:vee},uI=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
      const float exponentMultiplier = ${r};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${i}
      }

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${a});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${a}; 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) / ${s};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function lE(e,t,n){let a=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=de({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new uI("real",l,t),p=new uI("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Is({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=de({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function kee(e){let{inputs:t,backend:n}=e,{input:a}=t;return lE(a,!1,n)}var Iee={kernelName:um,backendName:"webgl",kernelFunc:kee},See=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}};function sd(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new See(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Tee={kernelName:pc,backendName:"webgl",kernelFunc:sd},Nee=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${t} - x - 1;
          float outputValue;
          if(coordX >= 0 && coordX < ${t}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}},Cee={kernelName:Wl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Nee(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},pI="return floor(x);",_ee=Ye({opSnippet:pI,packedOpSnippet:pI,cpuKernelImpl:ZZ}),Eee={kernelName:Ei,backendName:"webgl",kernelFunc:_ee},$ee=`
  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;
  }
`,Aee=`
  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);
`,Fee=pn({opSnippet:$ee,packedOpSnippet:Aee,dtype:"int32"}),Dee={kernelName:$i,backendName:"webgl",kernelFunc:Fee},Ree=class{constructor(e){this.variableNames=["A"];let t=Cn(),[n,a]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);

        vec4 values = ${t.texture2D}(A, uv);
        float value;
        if (depth == 0) {
          value = values.r;
        } else if (depth == 1) {
          value = values.g;
        } else if (depth == 2) {
          value = values.b;
        } else if (depth == 3) {
          value = values.a;
        }

        setOutput(floor(value * 255.0 + 0.5));
      }
    `}},Mee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Cn(),[n,a]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];

        vec4 result = vec4(0.);

        for(int row=0; row<=1; row++) {
          for(int col=0; col<=1; col++) {
            texC = coords[1] + row;
            depth = coords[2] + col;

            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${a}.0, ${n}.0);
            vec4 values = ${t.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }

        ${t.output} = result;
      }
    `}},Pee={kernelName:yh,backendName:"webgl",kernelFunc:Oee},qo,yb=H().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function Oee(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],d=[u,l,s];if(o||i){let f=H().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(qo==null||f!==yb)&&(yb=f,qo=document.createElement("canvas").getContext("2d",{willReadFrequently:yb})),qo.canvas.width=l,qo.canvas.height=u,qo.drawImage(r,0,0,l,u),r=qo.canvas}let c=n.makeTensorInfo(p,"int32");n.texData.get(c.dataId).usage=pa.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=H().getBool("WEBGL_PACK")?new Mee(d):new Ree(d),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function Lee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=N.convertConv2DDataFormat(p),g=N.computeConv2DInfo(r.shape,s.shape,l,d,u,c,!1,f),y,b=[],x=i!=null,w=o!=null,I=h==="leakyrelu",T=()=>{let E=[r,s],$=(R,F)=>{if(F==="NCHW"&&R.shape.length===1&&R.shape[0]!==1){let S=de({inputs:{x:R},backend:n,attrs:{shape:[R.shape[0],1,1]}});return b.push(S),S}return R};if(x&&E.push($(i,p)),w&&E.push($(o,p)),I){let R=n.makeTensorInfo([],"float32",v.createScalarValue(m,"float32"));E.push(R),b.push(R)}return E};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"))y=nE({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(g.strideWidth<=2&&f==="channelsLast"&&H().getBool("WEBGL_EXP_CONV")){let E=h?ec(h,!0):null,$=new tE(g,x,E,w,I),R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=T();y=n.runWebGLProgram($,F,"float32",R)}else if(H().getBool("WEBGL_CONV_IM2COL"))y=aE({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let E=h?ec(h,!1):null,$=new eE(g,x,E,w,I),R=T();y=n.runWebGLProgram($,R,"float32")}let C=de({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(E=>n.disposeIntermediateTensorInfo(E)),C}var zee={kernelName:Qs,backendName:"webgl",kernelFunc:Lee};function Wee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:d,activation:c,leakyreluAlpha:h}=a,m=[],f=p;f==null&&(f=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=N.computeConv2DInfo(r.shape,s.shape,l,f,u,d,!0),y=H().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=c?ec(c,y):null,x=[r,s],w=i!=null,I=o!=null,T=c==="leakyrelu";if(w&&x.push(i),I&&x.push(o),T){let R=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));x.push(R),m.push(R)}let C;y?C=new iE(g,w,b,I,T):C=new sE(g,w,b,I,T);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=n.runWebGLProgram(C,x,"float32",E);return m.forEach(R=>n.disposeIntermediateTensorInfo(R)),$}var Bee={kernelName:ei,backendName:"webgl",kernelFunc:Wee},Vee=class{constructor(e,t,n,a){this.sliceDim=e,this.strides=t,this.paramsShape=a,this.variableNames=["x","indices"],this.outputShape=n;let r=gt(n.length),s=`
    int index;`;for(let i=0;i<this.sliceDim;i++)s+=`
          index = round(getIndices(coords[0], ${i}));
          out_of_bounds = out_of_bounds || index < 0;
          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[i]};
          flattenIndex += index * ${this.strides[i]};`;this.userCode=`
         void main() {
          ${r} coords = getOutputCoords();
          int flattenIndex = 0;
          bool out_of_bounds = false;

          ${s}

          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
        }
      `}};function Uee(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(a.shape),[l,u,p,d]=N.prepareAndValidate(a,r),c=de({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),h=de({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape)/p,p]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let y=n.readSync(r.dataId),b=n.bufferSync(a),x=JZ(y,b,a.dtype,u,i,p,d,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new Vee(i,d,[u,p],a.shape),f=n.runWebGLProgram(m,[h,c],h.dtype),g=de({inputs:{x:f},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),g}var Gee={kernelName:Vl,backendName:"webgl",kernelFunc:Uee},Hee=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=gt(this.rank),a=jee(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${a}));
      }
    `}};function jee(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("index"):a.push(`${n[r]}`);return a.join()}function uE(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=v.parseAxisParam(i,r.shape)[0];if(H().get("DEBUG")){let b=n.readSync(s.dataId),x=r.shape[l];for(let w=0;w<b.length;++w){let I=b[w];v.assert(I<=x-1&&I>=0,()=>`GatherV2: the index value ${I} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=v.sizeFromShape(s.shape),d=[],c=de({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=de({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,p/u.batchSize]}});d.push(c),d.push(h);let m=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),x=n.bufferSync(c),w=QZ(x,b,m);return d.forEach(I=>n.disposeIntermediateTensorInfo(I)),n.makeTensorInfo(u.outputShape,w.dtype,w.values)}let f=new Hee(c.shape,m),g=n.runWebGLProgram(f,[c,h],c.dtype);d.push(g);let y=de({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var qee={kernelName:Bl,backendName:"webgl",kernelFunc:uE},Kee="return float(a > b);",Xee=`
  return vec4(greaterThan(a, b));
`,Yee=pn({opSnippet:Kee,packedOpSnippet:Xee,cpuKernelImpl:e7,dtype:"bool"}),Zee={kernelName:Ul,backendName:"webgl",kernelFunc:Yee},Jee="return float(a >= b);",Qee=`
  return vec4(greaterThanEqual(a, b));
`,ete=pn({opSnippet:Jee,packedOpSnippet:Qee,dtype:"bool",cpuKernelImpl:t7}),tte={kernelName:Fi,backendName:"webgl",kernelFunc:ete};function nte(e){let{inputs:t,backend:n}=e,{input:a}=t;return lE(a,!0,n)}var ate={kernelName:pm,backendName:"webgl",kernelFunc:nte},rte="return float(!isnan(x) && !isinf(x));",ste=Ye({opSnippet:rte,dtype:"bool"}),ite={kernelName:Gl,backendName:"webgl",kernelFunc:ste},ote="return float(isinf(x));",lte=Ye({opSnippet:ote,dtype:"bool"}),ute={kernelName:Hl,backendName:"webgl",kernelFunc:lte},pte="return float(isnan(x));",cte=Ye({opSnippet:pte,dtype:"bool"}),dte={kernelName:jl,backendName:"webgl",kernelFunc:cte},hte="return float(a < b);",mte=`
  return vec4(lessThan(a, b));
`,fte=pn({opSnippet:hte,packedOpSnippet:mte,cpuKernelImpl:n7,dtype:"bool"}),gte={kernelName:ql,backendName:"webgl",kernelFunc:fte},yte="return float(a <= b);",bte=`
  return vec4(lessThanEqual(a, b));
`,xte=pn({opSnippet:yte,packedOpSnippet:bte,cpuKernelImpl:a7,dtype:"bool"}),vte={kernelName:Kl,backendName:"webgl",kernelFunc:xte};function wte(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=r7(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var kte={kernelName:dm,backendName:"webgl",kernelFunc:wte},Ite=Uu+`
  return x < 0.0 ? 0./0. : log(x);
`,Ste=`
  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;
`,Tte=Ye({opSnippet:Ite,packedOpSnippet:Ste,cpuKernelImpl:s7}),Nte={kernelName:Mi,backendName:"webgl",kernelFunc:Tte},Cte=Uu+`
  return log(1.0 + x);
`,_te=Ye({opSnippet:Cte}),Ete={kernelName:Xl,backendName:"webgl",kernelFunc:_te},$te="return float(a >= 1.0 && b >= 1.0);",Ate=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,Fte=pn({opSnippet:$te,packedOpSnippet:Ate,dtype:"bool"}),Dte={kernelName:Yl,backendName:"webgl",kernelFunc:Fte},Rte="return float(!(x >= 1.0));",Mte=Ye({opSnippet:Rte}),Pte={kernelName:Zl,backendName:"webgl",kernelFunc:Mte},Ote="return float(a >= 1.0 || b >= 1.0);",Lte=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,zte=pn({opSnippet:Ote,packedOpSnippet:Lte,dtype:"bool"}),Wte={kernelName:Jl,backendName:"webgl",kernelFunc:zte},Bte=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${s}; j <= ${s}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${i}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${o};
        setOutput(val);
      }
    `}},Vte=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,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 - ${s};
        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 = - ${s}; j <= ${s}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));

          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 * ${o};
        setOutput(result);
      }
    `}},Ute=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=H().getBool("WEBGL_PACK_NORMALIZATION")?new Vte(r.shape,s,i,o,l):new Bte(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},Gte={kernelName:cc,backendName:"webgl",kernelFunc:Ute},Hte=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];

        float result = 0.0;
        for (int d = 0; d < ${this.depth}; ++d) {
          int depthBegin = int(max(0.0, float(d - ${t})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${t} + 1)));

          const int MIN_DEPTH_BEGIN = 0;
          const int MAX_DEPTH_END = ${this.depth};

          float norm = 0.0;
          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            }
            else {
              break;
            }
          }

          norm = float(${a}) * norm + float(${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(${a})
                * float(${r})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${r});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}},jte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new Hte(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},qte={kernelName:hm,backendName:"webgl",kernelFunc:jte};function Kte(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=de({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=ko(i,e.dtype,"max",a),l=de({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function pE(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=r.shape[p[T]];let w=Z0(b,r.shape,r.dtype,p,x);h=n.makeTensorInfo(x,r.dtype);let I=n.texData.get(h.dataId);I.values=w}else h=Mf(r,p,n);u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("max",u,o);let[m,f]=N.computeOutAndReduceShapes(h.shape,u),g=m;i&&(g=N.expandShapeToKeepDim(m,l));let y;if(c){let b=n.texData.get(h.dataId).values,x=i7(b,v.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(y.dataId);w.values=x}else y=Kte(h,f,g,n);return d&&n.disposeIntermediateTensorInfo(h),y}var Xte={kernelName:Pi,backendName:"webgl",kernelFunc:pE},Yte=J0+`
  return max(a, b);
`,Zte=`
  vec4 result = vec4(max(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+nd+`
  return result;
`,Jte=pn({opSnippet:Yte,packedOpSnippet:Zte,cpuKernelImpl:o7}),Qte={kernelName:Oi,backendName:"webgl",kernelFunc:Jte};function ene(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Lu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new tc(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var tne={kernelName:Li,backendName:"webgl",kernelFunc:ene};function nne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new e1(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var ane={kernelName:dc,backendName:"webgl",kernelFunc:nne},rne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${o});

      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 < ${r};
          wR += ${a}) {
          float dyR = float(dyRCorner + wR) / ${t}.0;

          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${s}; wC++) {
            float dyC = float(dyCCorner + wC) / ${n}.0;

            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));

            // Get the current value, check it against the value from the
            // position matrix.
            int curPosValue = wR * ${s} + wC;
            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `}},sne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=`
      const ivec3 pads = ivec3(${p}, ${d}, ${c});

      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 < ${o};
           wD += ${r}) {
          float dyD = float(dyDCorner + wD) / ${t}.0;

          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${l};
              wR += ${s}) {
            float dyR = float(dyRCorner + wR) / ${n}.0;

            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${u};
                wC += ${i}) {
              float dyC = float(dyCCorner + wC) / ${a}.0;

              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${h} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

              // Get the current value, check it against the value from the
              // position matrix.
              int curPosValue =
                  wD * ${l} * ${u} +
                  wR * ${u} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function ine(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new e1(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new sne(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var one={kernelName:fm,backendName:"webgl",kernelFunc:ine};function lne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Lu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=N.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new tc(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new rne(c),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var une={kernelName:mm,backendName:"webgl",kernelFunc:lne};function pne(e,t,n,a){let r=new tc(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new tc(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var cne={kernelName:gm,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=N.computePool2DInfo(a.shape,r,s,u,i),[d,c]=pne(a,o,p,l);return[d,c]}};function dne(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=de({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=ko(i,"float32","mean",a),l=de({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var hne={kernelName:zi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let C=0;C<w.length;C++)w[C]=a.shape[p[C]];let I=Z0(x,a.shape,a.dtype,p,w);m=i.makeTensorInfo(w,a.dtype);let T=i.texData.get(m.dataId);T.values=I}else m=Mf(a,p,i);h.push(m),u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("sum",u,o);let[f,g]=N.computeOutAndReduceShapes(m.shape,u),y=f;r&&(y=N.expandShapeToKeepDim(f,l));let b=dne(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return b}};function mne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,r.shape.length)),N.assertAxesAreInnerMostDims("min",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=de({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=ko(f,f.dtype,"min",n),y;if(i){let b=N.expandShapeToKeepDim(c,l);y=de({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=de({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var fne={kernelName:Wi,backendName:"webgl",kernelFunc:mne},gne=J0+`
  return min(a, b);
`,yne=`
  vec4 result = vec4(min(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+nd+`
  return result;
`,bne=pn({opSnippet:gne,packedOpSnippet:yne,cpuKernelImpl:l7}),xne={kernelName:Bi,backendName:"webgl",kernelFunc:bne},vne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let a=e.length,r=gt(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
        int start = ${s};
        int end = ${i};

        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=`
      ${r} start = ${r}(${s});
      ${r} end = ${r}(${i});

      void main() {
        ${r} outC = getOutputCoords();
        for (int i = 0; i < ${a}; 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};
          }
        }
        ${r} coords = outC - start;
        setOutput(getX(${o}));
      }
    `}},wne=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=gt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=wn("rc",a),l=wn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,c="";if(a===1){let h=`
        ${r} source = rc;
        if (source < start) {
          source = start * 2 - source - ${d};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${d};
        }
        source -= start;
      `;c=`
        ${r} rc = outputLoc;
        ${h}
        result[0] = getChannel(getX(${l.join()}), ${p});
        ${o[a-1]} += 1;
        if(${u}) {
          ${h}
          result[1] = getChannel(getX(${l.join()}), ${p});
        }
      `}else{let h=`
        ${r} source = rc;
        ${r} lt = ${r}(lessThan(source, start));
        ${r} gte = ${r}(greaterThanEqual(source, end));
        ${r} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${d}) +
                gte * ((end - 1) * 2 - source + ${d});
        source -= start;
      `;c=`
        ${r} rc = outputLoc;
        ${h}
        result[0] = getChannel(getX(${l.join()}), ${p});
        ${o[a-1]} += 1;
        if(${u}) {
          ${h}
          result[1] = getChannel(getX(${l.join()}), ${p});
        }
        rc = outputLoc;
        ${o[a-2]} += 1;
        if(${o[a-2]} < ${this.outputShape[a-2]}) {
          ${h}
          result[2] = getChannel(getX(${l.join()}), ${p});
          ${o[a-1]} += 1;
          if(${u}) {
            ${h}
            result[3] = getChannel(getX(${l.join()}), ${p});
          }
        }
      `}this.userCode=`
      const ${r} start = ${r}(${s});
      const ${r} end = ${r}(${i});

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${c}
        setOutput(result);
      }
    `}},kne=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wne(a.shape,r,s):new vne(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Ine={kernelName:Vi,backendName:"webgl",kernelFunc:kne},Sne=`if (b == 0.0) return NAN;
  return mod(a, b);`,Tne=`
  vec4 result = mod(a, b);
  bvec4 isNaN = equal(b, vec4(0.0));
  `+nd+`
  return result;
`,Nne=pn({opSnippet:Sne,packedOpSnippet:Tne}),Cne={kernelName:Ql,backendName:"webgl",kernelFunc:Nne},_ne=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,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 < ${t-1}; i++) {
          cdf += getProbs(batch, i);

          if (r < cdf) {
            setOutput(float(i));
            return;
          }
        }

        // If no other event happened, last event happened.
        setOutput(float(${t-1}));
      }
    `}},Ene=`
if (a == b) {
  return 1.0;
};
return a / b;`,$ne=`
  // 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;
`,cE=pn({opSnippet:Ene,packedOpSnippet:$ne,checkOutOfBounds:!0}),Ane={kernelName:Ni,backendName:"webgl",kernelFunc:cE},cI="return a - b;",dE=pn({opSnippet:cI,packedOpSnippet:cI,supportsComplex:!0,cpuKernelImpl:_7}),Fne={kernelName:lo,backendName:"webgl",kernelFunc:dE};function hE(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=v.parseAxisParam([s],r.shape),o=pE({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=de({inputs:{x:o},backend:n,attrs:{shape:l}}),p=dE({inputs:{a:r,b:u},backend:n}),d=oE({inputs:{x:p},backend:n}),c=Pf({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=de({inputs:{x:c},backend:n,attrs:{shape:l}}),m=cE({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Dne={kernelName:io,backendName:"webgl",kernelFunc:hE};function Rne(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:hE({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new _ne(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Mne={kernelName:ym,backendName:"webgl",kernelFunc:Rne},Pne=Da+`
  return -x;
`,One=`
  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 Lne(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=p7(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new js(a.shape,One):r=new Sr(a.shape,Pne),n.runWebGLProgram(r,[a],a.dtype)}var zne={kernelName:eu,backendName:"webgl",kernelFunc:Lne},Wne=cr.nonMaxSuppressionV3Impl;function Bne(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=Wne(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Vne={kernelName:nu,backendName:"webgl",kernelFunc:Bne},Une=cr.nonMaxSuppressionV4Impl;function Gne(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=Une(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Hne={kernelName:au,backendName:"webgl",kernelFunc:Gne},jne=cr.nonMaxSuppressionV5Impl;function qne(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=jne(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Kne={kernelName:ru,backendName:"webgl",kernelFunc:qne},Xne=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${a}), float(${n}),
                      float(index == coords.y)));
      }
    `}},Yne=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=v.sizeFromShape(r.shape),p=new Xne(u,i,o,l),d=de({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=de({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},Zne={kernelName:Gi,backendName:"webgl",kernelFunc:Yne};function Uh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=rd({inputs:{input:a},backend:n}),s=Uh({inputs:{x:r},backend:n}),i=Of({inputs:{input:a},backend:n}),o=Uh({inputs:{x:i},backend:n}),l=Is({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return sd({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var Jne={kernelName:Iu,backendName:"webgl",kernelFunc:Uh};function mE(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=rd({inputs:{input:a},backend:n}),s=mE({inputs:{x:r},backend:n}),i=Of({inputs:{input:a},backend:n}),o=Uh({inputs:{x:i},backend:n}),l=Is({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return sd({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var Qne={kernelName:su,backendName:"webgl",kernelFunc:mE};function eae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return dx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=dx({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=Q_({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var tae={kernelName:iu,backendName:"webgl",kernelFunc:eae},nae=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=gt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
        int start = ${s};
        int end = ${i};

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(value);
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;return}this.userCode=`
      ${r} start = ${r}(${s});
      ${r} end = ${r}(${i});

      void main() {
        ${r} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${r} coords = outC - start;
          setOutput(getX(${o}));
        }
      }
    `}},aae=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=gt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=wn("rc",a),l=wn("source",a),u=`${o[a-1]} < ${this.outputShape[a-1]}`,p=a===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
       if(${u}) {
      `,a===1?"":`}
       rc = outputLoc;
       ${o[a-2]} += 1;
       if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":`  ${o[a-1]} += 1;
         if(${u}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
        ${d[m]}
        if (${c}) {
          result[${m}] = float(value);
        } else {
          ${r} source = rc - start;
          result[${m}] = getChannel(getX(${l.join()}), ${p});
        }
      `;h+=a===1?"} ":"}}",this.userCode=`
      const ${r} start = ${r}(${s});
      const ${r} end = ${r}(${i});

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${h}
        setOutput(result);
      }
    `}},fE=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return sd({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aae(r.shape,s,i):new nae(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},rae={kernelName:Hi,backendName:"webgl",kernelFunc:fE},sae=`
  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);
`,iae=`
  // 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);
  `+nd+`
  return result;
`,oae=pn({opSnippet:sae,packedOpSnippet:iae}),lae={kernelName:ji,backendName:"webgl",kernelFunc:oae};function uae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,d=N.getAxesPermutation(p,o),c=r;d!=null&&(c=In({inputs:{x:r},backend:n,attrs:{perm:d}}),p=N.getInnerMostAxes(p.length,o),l.push(c)),N.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:y}=d7(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=v.sizeFromShape(f),y=de({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),b=_m(r.dtype),x=ko(y,b,"prod",n);h=de({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=N.expandShapeToKeepDim(h.shape,u);h=de({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var pae={kernelName:Ki,backendName:"webgl",kernelFunc:uae};function cae(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(y=>n.readSync(y.dataId)),u=r.map(y=>y.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=h7(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(y=>n.makeTensorInfo([y.length],"int32",y)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var dae={kernelName:bm,backendName:"webgl",kernelFunc:cae};function hae(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=m7(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var mae={kernelName:xm,backendName:"webgl",kernelFunc:hae};function fae(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=f7(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var gae={kernelName:vm,backendName:"webgl",kernelFunc:fae},gE=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=g7(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},yae={kernelName:hc,backendName:"webgl",kernelFunc:gE},bae="return 1.0 / x;",xae=Ye({opSnippet:bae}),vae={kernelName:ou,backendName:"webgl",kernelFunc:xae},wae=Da+`
  return (x < 0.0) ? 0.0 : x;
`,kae=`
  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;
`,Iae=Ye({opSnippet:wae,packedOpSnippet:kae}),Sae={kernelName:Xi,backendName:"webgl",kernelFunc:Iae},Tae=Da+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Nae=`
  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;
`,Cae=Ye({opSnippet:Tae,packedOpSnippet:Nae}),_ae={kernelName:Ji,backendName:"webgl",kernelFunc:Cae},Eae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/p[0]},
          ${u[1]/p[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${d};

        // 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);
      }
    `}},$ae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${u[0]/p[0]},
          ${u[1]/p[1]},
          ${u[1]/p[1]});
      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
                                     ${o}.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 = ${d};

        // 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 Aae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new $ae(r.shape,l,u,s,i):new Eae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Fae={kernelName:Zi,backendName:"webgl",kernelFunc:Aae},Dae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*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(${u});
        const float widthScale = float(${p});

        const float invHeightScale = float(${d});
        const float invWidthScale = float(${c});

        const int winHeight = int(${h});
        const int winWidth = int(${m});

        // 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 >= ${s}) {
            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 >= ${i}) {
              continue;
            }

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${a-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), ${r-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 Rae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Dae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Mae={kernelName:Im,backendName:"webgl",kernelFunc:Rae},Pae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/p[0]},
          ${u[1]/p[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${c};

        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `}},Oae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${u[0]/p[0]},
          ${u[1]/p[1]},
          ${u[1]/p[1]});
      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
                                     ${o}.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 = ${c};

        // Compute the coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));

        // 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 Lae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Oae(r.shape,l,u,s,i):new Pae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var zae={kernelName:Yi,backendName:"webgl",kernelFunc:Lae},Wae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*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(${u});
        const float widthScale = float(${p});

        const float invHeightScale = float(${d});
        const float invWidthScale = float(${c});

        const int winHeight = int(${h});
        const int winWidth = int(${m});

        // 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 >= ${s}) {
            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 >= ${i}) {
              continue;
            }

            float sourceFracRow =
              float(${o[0]}) *
                (float(dyR) / float(${l[0]}));

            float sourceFracCol =
                float(${o[1]}) *
                  (float(dyC) / float(${l[1]}));

            int sourceNearestRow = int(min(
                float(int(${a}) - 1),
                ${n} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${r}) - 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 Bae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Wae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Vae={kernelName:km,backendName:"webgl",kernelFunc:Bae},Uae=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=gt(n);this.userCode=`
      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${r}));
      }
    `}},Gae=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=wn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=gt(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${r}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${i} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${o(a.slice())};
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            result.g = ${l(a.slice())};
          }
          if(${s}) {
            result.b = ${u(a.slice())};
            if(${r}) {
              result.a = ${p(a.slice())};
            }
          }
          setOutput(result);
        }
    `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((y,b)=>c(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function Hae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return na({inputs:{x:r},backend:n});let l=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Gae(r.shape,o):new Uae(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var jae={kernelName:Qi,backendName:"webgl",kernelFunc:Hae},qae=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
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          int y = coords[1];
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  float base = floor(x);
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  } else {
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      return base;
    } else {
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    }
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        void main() {
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            for (int j = 0; j < ${t}; j++) {
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            }
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              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function tre(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=N.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=de({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=de({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new yE(l,o,h.shape.length,m.shape.length,p,c),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=de({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var nre={kernelName:uu,backendName:"webgl",kernelFunc:tre},are=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=H().getNumber("WEBGL_VERSION")===2?r:s,o=a==="left"?"<":"<=";this.userCode=`
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         int right = numInputs;
         int mid;
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             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 rre(e){let{inputs:t,backend:n,attrs:a}=e,{sortedSequence:r,values:s}=t,{side:i}=a,o=new are(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return n.runWebGLProgram(o,[r,s],"int32",l)}var sre={kernelName:Sm,backendName:"webgl",kernelFunc:rre},ire=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);a=o.join(),r=l.join()}let s=gt(n);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        float cVal = getC(${a});
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        } else {
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        }
      }
    `}};function ore(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new ire(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ma(r.dtype,s.dtype))}var lre={kernelName:pu,backendName:"webgl",kernelFunc:ore},ure=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
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  float scale = ${N.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,pre=Ye({opSnippet:ure}),cre={kernelName:cu,backendName:"webgl",kernelFunc:pre},dre=Uu+`
  return 1.0 / (1.0 + exp(-1.0 * x));
`,hre=`
  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;
`,mre=Ye({opSnippet:dre,packedOpSnippet:hre,cpuKernelImpl:x7}),fre={kernelName:ao,backendName:"webgl",kernelFunc:mre},gre=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,yre=Ye({opSnippet:gre}),bre={kernelName:mu,backendName:"webgl",kernelFunc:yre},xre=Uu+`
  return sin(x);
`,vre=Ye({opSnippet:xre}),wre={kernelName:no,backendName:"webgl",kernelFunc:vre},kre=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,Ire=Ye({opSnippet:kre}),Sre={kernelName:hu,backendName:"webgl",kernelFunc:Ire},Tre=`
  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;
`,Nre=Ye({opSnippet:Tre}),Cre={kernelName:fu,backendName:"webgl",kernelFunc:Nre},_re=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let u=[],p=fE({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(p.shape,s,o,!1),c=N.getPermuted(d.length,s.length,!1),h=N.getReshapedPermuted(p.shape,s,o,!1),m=de({inputs:{x:p},backend:n,attrs:{shape:d}}),f=In({inputs:{x:m},backend:n,attrs:{perm:c}}),g=de({inputs:{x:f},backend:n,attrs:{shape:h}});return u.push(p),u.push(m),u.push(f),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},Ere={kernelName:gu,backendName:"webgl",kernelFunc:_re};function $re(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
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         ${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
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              ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=z_(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(p,a.dtype,u)}var Mre={kernelName:fc,backendName:"webgl",kernelFunc:Rre};function Pre(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=z_(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var Ore={kernelName:gc,backendName:"webgl",kernelFunc:Pre};function Lre(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=N.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let y=n.bufferSync(r),b=n.bufferSync(s),x=v.decodeString(n.readSync(i.dataId)[0]),w=b7(y,b,o,c,p,u,l,d,x,h);return n.makeTensorInfo(o,w.dtype,w.values)}let m=new yE(u,l,r.shape.length,s.shape.length,d,[c,1],h),f=n.runWebGLProgram(m,[s,r,i],s.dtype),g=de({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),g}var zre={kernelName:Tm,backendName:"webgl",kernelFunc:Lre};function Wre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=v.parseAxisParam(i,r.shape)[0],l=N.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=Gu({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var Bre={kernelName:yu,backendName:"webgl",kernelFunc:Wre},dI="return sqrt(x);",Vre=Ye({opSnippet:dI,packedOpSnippet:dI,cpuKernelImpl:I7}),Ure={kernelName:ro,backendName:"webgl",kernelFunc:Vre},Gre="return x * x;",Hre=Ye({opSnippet:Gre}),jre={kernelName:yc,backendName:"webgl",kernelFunc:Hre},hI="return (a - b) * (a - b);",qre=pn({opSnippet:hI,packedOpSnippet:hI}),Kre={kernelName:oo,backendName:"webgl",kernelFunc:qre};function Xre({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Da+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,s=new Sr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Yre={kernelName:ms,backendName:"webgl",kernelFunc:Xre},Zre=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=gt(n.length),s=gt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
      ${r} begin = ${r}(${e});
      ${r} strides = ${r}(${t});

      void main() {
        ${s} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `}};function Jre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:w}=jt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=de({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=jt.computeOutShape(b,x,w),E=Gu({inputs:{x:r},backend:n,attrs:{begin:b,size:C}});I=de({inputs:{x:E},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),E=Pe(r.shape,r.dtype,C),$=S7(h,E,w,b);I=n.makeTensorInfo(m,r.dtype,$.values)}else{let C=new Zre(b,w,h);I=n.runWebGLProgram(C,[r],r.dtype)}let T=de({inputs:{x:I},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(I),T}var Qre={kernelName:xu,backendName:"webgl",kernelFunc:Jre};function ese(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.readSync(p.dataId),h=n.readSync(d.dataId),[m,f]=T7(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var tse={kernelName:bc,backendName:"webgl",kernelFunc:ese};function nse(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{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 o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=N7(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var ase={kernelName:xc,backendName:"webgl",kernelFunc:nse};function rse(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=C7(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var sse={kernelName:vc,backendName:"webgl",kernelFunc:rse},ise="return tan(x);",ose=Ye({opSnippet:ise}),lse={kernelName:uo,backendName:"webgl",kernelFunc:ose},use=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,pse=Ye({opSnippet:use}),cse={kernelName:po,backendName:"webgl",kernelFunc:pse},dse=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=gt(this.rank),r=hse(e);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `}};function hse(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function bE(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>v.decodeString(d)):o,u=Pe(r.shape,r.dtype,l),p=E7(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new dse(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var mse={kernelName:hs,backendName:"webgl",kernelFunc:bE},fse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // We compare elements pair-wise within a group of size 2 * inc.
         // The comparing rule for each group alternates between ascending
         // and descending. Within each group, we compare each pair at
         // positions i and i+inc. To decide whether an element at position i
         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
         // inc, it is in the first half of the group, we denote it as x0,
         // otherwise we denote it as x1.
         // For example, as shown in the Bitonic top K paper referenced above,
         // Figure5(a) shows that element[1] is in the
         // second half of the group when group size is 2, but it is in the
         // first half of the group when group size is 4.

         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
         int i = isFirstInPair ? elemIdx : elemIdx - inc;

         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
         float x1 = i1 < n ? getX(batch, i1) : negativeInf;

         // Denotes which direction indices are in (ascending or descending).
         bool reverse = imod(elemIdx, 2 * dir) >= dir;
         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
         if (reverse == isGreater) { // Elements in opposite order of direction
           int iTemp = i0;
           i0 = i1;
           i1 = iTemp;
         }
         if (isFirstInPair) {
            setOutput(float(i0));
         } else {
            setOutput(float(i1));
         }
       }
     `}},gse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
    void main() {
         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // The output size is half of the previous size.
         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
         // we only need to output the indices at positions |, the indices at
         // positions _ can be thrown away, see Figure5(b) After Phase 2
         // (Merge phase) in the Bitonic Top K paper referenced above.
         // For example, the paper shows we only need to output the orange bars.
         // The output sequence should look like this | | | | | | | |.
         // Because the sequence is halved, to map the output index back
         // to the previous sequence to find the corresponding value,
         // we need to double the index. When we double the index,
         // we basically interpolate a position, so 2i looks like
         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
         // of each 2k positions by - elemIdx % k. E.g. for output at
         // index 4,5,6,7, we want to get the corresponding element at
         // original index 8,9,10,11, for output at index 8,9,10,11,
         // we want to get the corresponding element at original index
         // 16,17,18,19, so on and so forth.

         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));

         float x0 = getX(batch, i0);
         float x1 = i1 < n ? getX(batch, i1) : x0;

         setOutput(x0 >= x1 ? float(i0) : float(i1));
       }
     `}};function Os(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function mI(e){let t=1;for(;t<e;)t*=2;return t}function yse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=H().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=H().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,p=u[u.length-1];if(n.shouldExecuteOnCPU([r])||p<o||s>l){let $=n.readSync(r.dataId),[R,F]=$7($,u,r.dtype,s,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,sd({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=de({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Os(n,h);let g=mI(s),y=mI(p),b=null,x=()=>b===null?[f,f]:[f,b],w=($,R,F)=>{let S=x(),M=new fse(F),B=[[p],[b===null?1:0],[Number.NEGATIVE_INFINITY],[$],[R]],U=b;b=n.runWebGLProgram(M,S,"int32",B),Os(n,U)};for(let $=1;$<g;$*=2){let R=$*2;for(let F=$;F>=1;F/=2)w(R,F,[m,y])}for(let $=y;$>g;$/=2){let R=x(),F=new gse([m,$/2]),S=[[p],[b===null?1:0],[g]],M=b;b=n.runWebGLProgram(F,R,"int32",S),Os(n,M);let B=g/2,U=B*2;for(let G=B;G>=1;G/=2)w(U,G,b.shape)}let I=b;b=Gu({inputs:{x:b},backend:n,attrs:{begin:0,size:[m,s]}}),Os(n,I);let T=uE({inputs:{x:f,indices:b},backend:n,attrs:{axis:1,batchDims:1}});Os(n,f);let C=u.slice(0,-1);C.push(s),I=b,b=de({inputs:{x:b},attrs:{shape:C},backend:n}),Os(n,I);let E=T;return T=de({inputs:{x:T},attrs:{shape:C},backend:n}),Os(n,E),[T,b]}var bse={kernelName:vu,backendName:"webgl",kernelFunc:yse},xse=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${o} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 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 (${o} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${r});
              }
              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(${r});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${t}));
                float mapY = mapCoord(inY, float(${e}));

                if (${i} == 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 vse(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],y=new xse(d,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var wse={kernelName:wu,backendName:"webgl",kernelFunc:vse};function kse(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Lu(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=A7(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var Ise={kernelName:Nm,backendName:"webgl",kernelFunc:kse};function Sse(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let f=0;f<o;f++)f!==s&&(u[p++]=i.shape[f]);let d=[],c=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){c[s]=f;let g=Gu({inputs:{x:i},backend:n,attrs:{begin:c,size:h}}),y=de({inputs:{x:g},backend:n,attrs:{shape:u}});m[f]=y,d.push(g)}return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var Tse={kernelName:ku,backendName:"webgl",kernelFunc:Sse},Nse=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,p=n%4,d=`
        sumValue += dot(values, segFilter);
    `,c="";r%n>0&&(c=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return initializationValue;
        }
      `);let h="";r%n>0&&(h=`
        if (inIdx < 0 || inIdx >= ${r}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${o};

      float getValue(int batch, int inIdx) {
        ${c}
        return getX(batch, inIdx);
      }

      float getSegmentIdAtIndex(int inIdx) {
        ${h}
        return getSegmentIds(inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = int(floor(float(outIdx) / float(
          ${s})) * float(${n}));
        int currentSeg = int(mod(float(outIdx), float(${s})));

        float sumValue = 0.0;

        for (int i = 0; i < ${u}; 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
          );

          ${d}
        }

        int inIdx = inOffset + ${u};
        if (${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
          );

          ${d}
        } else if (${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
          );

          ${d}
        } else if (${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
          );

          ${d}
        }
        setOutput(${l});
      }
    `}};function Cse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,p=N.getAxesPermutation([u],o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=N.getInnerMostAxes(1,o)[0]);let c=N.segment_util.computeOutShape(d.shape,u,i),h=v.sizeFromShape([d.shape[u]]),m=de({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=_m(r.dtype),g=(w,I,T,C,E)=>{let $=w.shape[0],R=w.shape[1],F=N.segment_util.segOpComputeOptimalWindowSize(R,E),S={windowSize:F,inSize:R,batchSize:$,numSegments:E},M=new Nse(S,I),B=n.compileAndRun(M,[w,T],C);if(l.push(B),B.shape[1]===E)return B;let U=gE({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),G=bE({inputs:{x:U},backend:n,attrs:{reps:[R/F]}});return l.push(U),l.push(G),g(B,I,G,C,E)},y=g(m,"unsortedSegmentSum",s,f,i),b=de({inputs:{x:y},backend:n,attrs:{shape:c}}),x=b;if(p!=null){l.push(b);let w=N.getUndoAxesPermutation(p);x=In({inputs:{x},backend:n,attrs:{perm:w}})}return l.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}var _se={kernelName:wc,backendName:"webgl",kernelFunc:Cse},Ese=[SJ,NJ,EJ,FJ,RJ,OJ,zJ,BJ,HJ,qJ,YJ,QJ,n9,i9,u9,c9,h9,y9,x9,w9,T9,F9,R9,P9,V9,G9,K9,oJ,Z9,nQ,iQ,dQ,mQ,gQ,bQ,vQ,IQ,NQ,EQ,AQ,DQ,MQ,LQ,WQ,GQ,jQ,XQ,JQ,eee,ree,lee,dee,fee,bee,xee,wee,Iee,Tee,Cee,Eee,Dee,Pee,zee,Bee,Gee,qee,Zee,tte,iJ,ate,eQ,ite,ute,dte,uJ,gte,vte,kte,Nte,Ete,Dte,Pte,Wte,Gte,qte,Xte,Qte,tne,ane,one,une,cne,hne,fne,xne,Ine,Cne,Mne,dJ,zne,Vne,Hne,Kne,L9,Zne,Qne,tae,rae,lae,cJ,pae,dae,mae,gae,yae,z9,Ane,vae,Sae,_ae,mJ,Fae,Mae,zae,Vae,jae,Kae,Zae,ere,nre,sre,lre,cre,fre,bre,wre,Sre,$9,Dne,Cre,Ere,Are,Dre,Mre,Ore,zre,Bre,Ure,jre,Kre,Yre,Qre,tse,ase,sse,Fne,wJ,lse,cse,mse,bse,wse,kJ,Ise,Tse,_se,Jne];for(let e of Ese)kc(e);var Et;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Et||(Et={}));var ac;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(ac||(ac={}));var xE;function $se(e){xE=e.wasm.cwrap(Js,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Ase(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);m=E.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=ac[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=u?s.shape[1]:s.shape[2],x=Tu.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)),w=n.makeOutput([...x,y,b],r.dtype),I=n.dataIdMap.get(w.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),C=new Uint8Array(new Int32Array(s.shape).buffer);return xE(c,T,r.shape.length,h,C,s.shape.length,l,u,g,m,f,d||0,I),w}var Fse={kernelName:Js,backendName:"wasm",setupFunc:$se,kernelFunc:Ase};function an(e,t){let n;function a(s){n=s.wasm.cwrap(e,null,["number","number","number"])}function r(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),p=i.dataIdMap.get(u.dataId).id;return v.sizeFromShape(u.shape)===0||n(l,Et[o.dtype],p),u}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:r}}var Dse=an(wl);function cn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,d=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(p.dataId).id,h=n!=null?n:u.dtype,m=N.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(p.shape).buffer),b=o.dataIdMap.get(f.dataId).id;return a(d,g,u.shape.length,c,y,p.shape.length,Et[u.dtype],b),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var Rse=!0,Mse=cn(cs,Rse),vE;function Pse(e){vE=e.wasm.cwrap(mi,null,["array","number","number","number"])}function Ose(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return vE(s,r.length,Et[a.dtype],i),a}var Lse={kernelName:mi,backendName:"wasm",setupFunc:Pse,kernelFunc:Ose};function Lf(e){let{inputs:{x:t},backend:n}=e;if(t.dtype==="string")return kn(n.readSync(t.dataId),t.shape,t.dtype);let a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var zse={kernelName:Di,backendName:"wasm",kernelFunc:Lf},wE;function Wse(e){wE=e.wasm.cwrap(Tr,null,["number","array","number","number","number","array","number"])}function ls(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Vse(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Bse(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=Lf({inputs:t,backend:n});return m.shape=o,m}let u=n.makeOutput(o,l.dtype),p=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(u.dataId).id,c=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return wE(p,h,l.shape.length,Et[l.dtype],d,c,s.length),u}function Bse(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function Vse(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Use={kernelName:Tr,backendName:"wasm",kernelFunc:ls,setupFunc:Wse};function Ss(e,t,n){let a=e.shape,r=e.shape.length,s=v.parseAxisParam(t,a),i=s,o=N.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let c=0;c<p.length;c++)p[c]=a[o[c]];i=N.getInnerMostAxes(i.length,r),l=ls({inputs:{x:e},attrs:{perm:o},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var kE;function Gse(e){kE=e.wasm.cwrap(Sl,null,["number, number, number"])}function Hse(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ss(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;N.assertAxesAreInnerMostDims("all",p,h);let[m,f]=N.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;kE(o,g,b)}if(c&&t.disposeData(u.dataId),s){let b=N.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var jse={kernelName:Sl,backendName:"wasm",setupFunc:Gse,kernelFunc:Hse},IE;function qse(e){IE=e.wasm.cwrap(Tl,null,["number, number, number"])}function Kse(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ss(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;N.assertAxesAreInnerMostDims("any",p,h);let[m,f]=N.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;IE(o,g,b)}if(c&&t.disposeData(u.dataId),s){let b=N.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var Xse={kernelName:Tl,backendName:"wasm",setupFunc:qse,kernelFunc:Kse},SE;function Yse(e){SE=e.wasm.cwrap(fi,null,["number","number","number","number","number"])}function Zse(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:p,inputWasTransposed:d}=Ss(s,r,t);if(d){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let c=l.shape.slice(0,-1),h=t.makeOutput(c,"int32"),m=t.dataIdMap.get(h.dataId).id,f=v.sizeFromShape(h.shape),g=l.shape[p[0]];return SE(o,Et[l.dtype],f,g,m),d&&t.disposeData(u.dataId),h}var Jse={kernelName:fi,backendName:"wasm",kernelFunc:Zse,setupFunc:Yse},TE;function Qse(e){TE=e.wasm.cwrap(gi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function eie(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.strideHeight,b=p.strideWidth,x=p.inChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);if(p.dilationWidth!==1||p.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${p.dilationHeight}, ${p.dilationWidth}].`);let w=a.makeOutput(p.outShape,"float32"),I=a.dataIdMap.get(w.dataId).id;return TE(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,y,b,x,I),w}var tie={kernelName:gi,backendName:"wasm",setupFunc:Qse,kernelFunc:eie};function Wn(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=v.sizeFromShape(a.shape),i=v.inferFromImplicitShape(r,s);return v.assert(s===v.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var nie={kernelName:lu,backendName:"wasm",kernelFunc:Wn},NE;function aie(e){NE=e.wasm.cwrap(yi,null,["number","array","number","number","array","number","number","number","number"])}function rie(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,p=i?r.shape[l-2]:r.shape[l-1],d=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=v.sizeFromShape(m),y=v.sizeFromShape(f),b=Tu.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)).concat([c,h]);v.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,p,c]:[g,c,p],w=o?[y,h,d]:[y,d,h],I=Wn({inputs:{x:r},backend:n,attrs:{shape:x}}),T=Wn({inputs:{x:s},backend:n,attrs:{shape:w}}),C=n.dataIdMap.get(I.dataId).id,E=n.dataIdMap.get(T.dataId).id,$=i?I.shape[2]:I.shape[1],R=o?T.shape[1]:T.shape[2],F=Math.max(g,y),S=n.makeOutput([F,$,R],I.dtype),M=n.dataIdMap.get(S.dataId).id,B=new Uint8Array(new Int32Array(I.shape).buffer),U=new Uint8Array(new Int32Array(T.shape).buffer);return NE(C,B,I.shape.length,E,U,T.shape.length,i,o,M),n.disposeData(I.dataId),n.disposeData(T.dataId),S.shape=b,S}var sie={kernelName:yi,backendName:"wasm",setupFunc:aie,kernelFunc:rie};function di(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=jt.parseSliceParams(t,n,a),o=jt.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),u=r.makeOutput(i,t.dtype),p=v.computeStrides(t.shape),d=r.dataIdMap.get(u.dataId);if(o){let m=jt.computeFlatOffset(s,p);return t.dtype==="string"?d.stringBytes=l.slice(m,m+v.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+v.sizeFromShape(i))),u}if(t.dtype==="string"){let m=Oh(l,s,i,t.shape,t.dtype);return d.stringBytes=m,u}let c=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)iie(l,p[0],c,s,i);else if(h===3)oie(l,p[0],p[1],c,s,i);else if(h===4)lie(l,p[0],p[1],p[2],c,s,i);else{let m=Oh(l,s,i,t.shape,t.dtype);c.set(m)}return u}function iie(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;u<l;u++){let p=u*t+o;n.set(e.subarray(p,p+r[1]),s),s+=r[1]}}function oie(e,t,n,a,r,s){let i=0,o=r[0],l=r[1],u=r[2],p=o+s[0],d=l+s[1];for(let c=o;c<p;c++)for(let h=l;h<d;h++){let m=c*t+h*n+u;a.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function lie(e,t,n,a,r,s,i){let o=0,l=s[0],u=s[1],p=s[2],d=l+i[0],c=u+i[1],h=p+i[2],m=s[3];for(let f=l;f<d;f++)for(let g=u;g<c;g++)for(let y=p;y<h;y++){let b=f*t+g*n+y*a+m;r.set(e.subarray(b,b+i[3]),o),o+=i[3]}}var uie={kernelName:du,backendName:"wasm",kernelFunc:di};function pie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a,o=s.reduce((y,b)=>y*b),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=Wn({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ls({inputs:{x:h},backend:n,attrs:{perm:u}}),f=Wn({inputs:{x:m},backend:n,attrs:{shape:p}}),g=di({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeData(h.dataId),n.disposeData(m.dataId),n.disposeData(h.dataId),g}var cie={kernelName:Al,backendName:"wasm",kernelFunc:pie};function Hu(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var die={kernelName:bi,backendName:"wasm",kernelFunc:Hu},hie=an(xi),CE;function mie(e){CE=e.wasm.cwrap(ds,null,["number","number","number","number"])}function fie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return CE(o,s,i,u),l}var gie={kernelName:ds,backendName:"wasm",setupFunc:mie,kernelFunc:fie};function _E(e){let{inputs:t,backend:n}=e,a=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);N.assertParamsConsistent(r,a);let s=N.computeOutShape(t.map(h=>h.shape),a),i=t.filter(h=>v.sizeFromShape(h.shape)>0);if(i.length===1)return Lf({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(v.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let w=[-1,v.sizeFromShape(x.shape.slice(a))];return Wn({inputs:{x},backend:n,attrs:{shape:w}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));s=N.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=E0(m,s,t[0].dtype,f),y=N.computeOutShape(i.map(x=>x.shape),a);o.shape=y;let b=n.dataIdMap.get(o.dataId);return b.stringBytes=N.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=v.sizeFromShape(i[0].shape.slice(0,a)),u=0,p=i.map(h=>{let m=v.sizeFromShape(h.shape.slice(a));return u+=m,m}),d=i.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let m=h*u;for(let f=0;f<d.length;f++){let g=p[f],y=h*g,b=d[f].subarray(y,y+g);c.set(b,m),m+=g}}return o}var yie={kernelName:Fl,backendName:"wasm",kernelFunc:_E},EE;function bie(e){EE=e.wasm.cwrap(vi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xie(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d,dataFormat:c}=n,h=N.convertConv2DDataFormat(c),m=N.computeConv2DInfo(r.shape,s.shape,l,u,p,d,!1,h),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,b=m.padInfo.right,x=m.padInfo.bottom,w=m.padInfo.left,I=m.dilationHeight,T=m.dilationWidth,C=m.strideHeight,E=m.strideWidth,$=m.inChannels,R=m.outChannels,F=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(m.outShape,"float32"),M=a.dataIdMap.get(S.dataId).id;return EE(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,x,w,F,I,T,C,E,$,R,M),S}var vie={kernelName:vi,backendName:"wasm",setupFunc:bie,kernelFunc:xie},$E;function wie(e){$E=e.wasm.cwrap(wi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kie(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:p}=a,d=1,c=N.convertConv2DDataFormat(l),h=N.computeConv2DInfo(p,s.shape,i,d,o,u,!1,c),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:x,outChannels:w,outHeight:I,outWidth:T,strideHeight:C,strideWidth:E}=h,$=f-1-h.padInfo.top,R=g-1-h.padInfo.left,F=h.dataFormat==="channelsLast",S=v.computeStrides(h.inShape),M=v.computeStrides(r.shape),[B,U,G]=v.computeStrides(s.shape),q=S[0],K=F?S[1]:S[2],Z=F?S[2]:1,J=F?1:S[1],ee=M[0],ae=F?M[1]:M[2],te=F?M[2]:1,ie=F?1:M[1],oe=t.makeOutput(h.inShape,"float32"),ye=t.dataIdMap.get(oe.dataId).id,ue=t.dataIdMap.get(r.dataId).id,be=t.dataIdMap.get(s.dataId).id;return $E(ue,be,m,f,g,b,x,y,I,T,w,C,E,$,R,B,U,G,q,K,Z,J,ee,ae,te,ie,ye),oe}var Iie={kernelName:wi,backendName:"wasm",setupFunc:wie,kernelFunc:kie},Sie=an(ki),Tie=an(Ii),hx;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(hx||(hx={}));var AE;function Nie(e){AE=e.wasm.cwrap(Rl,null,["number","number","number","number","array","number","number","number","number","number"])}function Cie(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:u}=n,p=l.shape[0],[d,c]=i,h=[p,d,c,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=Hu({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(h,"float32"),w=t.dataIdMap.get(x.dataId).id,I=new Uint8Array(new Int32Array(o.shape).buffer);return AE(g,y,b,p,I,d,c,hx[r],s,w),f!=null&&t.disposeData(f.dataId),x}var _ie={kernelName:Rl,backendName:"wasm",setupFunc:Nie,kernelFunc:Cie},FE;function Eie(e){FE=e.wasm.cwrap(Dl,null,["number","number","number","number","number","number"])}function $ie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=ls({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumprod",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;FE(m,i?1:0,o?1:0,h,f,Et[r.dtype]);let g=c;if(u!==null){let y=N.getUndoAxesPermutation(u);g=ls({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Aie={kernelName:Dl,backendName:"wasm",setupFunc:Eie,kernelFunc:$ie},DE;function Fie(e){DE=e.wasm.cwrap(Si,null,["number","number","number","number","number","number"])}function Die(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=ls({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;DE(m,i?1:0,o?1:0,h,f,Et[r.dtype]);let g=c;if(u!==null){let y=N.getUndoAxesPermutation(u);g=ls({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Rie={kernelName:Si,backendName:"wasm",setupFunc:Fie,kernelFunc:Die},RE;function Mie(e){RE=e.wasm.cwrap(Ml,null,["number","number","number","array","number","array","array","number","number"])}function Pie(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return RE(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,w),f}var Oie={kernelName:Ml,backendName:"wasm",setupFunc:Mie,kernelFunc:Pie},ME;function Lie(e){ME=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zie(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=N.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,I=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,E=h.inChannels,$=h.outChannels,R=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let F=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get(F.dataId).id;return ME(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,R,w,I,T,C,E,$,S),F}var Wie={kernelName:Ti,backendName:"wasm",setupFunc:Lie,kernelFunc:zie},Bie=an(Ci),Vie=!1,Uie=cn(Ol,Vie,"bool"),Gie=an(_i,"float32");function mx(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Wn({inputs:{x:r},backend:a,attrs:{shape:o}})}var Hie={kernelName:Ll,backendName:"wasm",kernelFunc:mx};function PE(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var jie={kernelName:pc,backendName:"wasm",kernelFunc:PE},OE;function qie(e){OE=e.wasm.cwrap(Wl,null,["number","number","number","number","number","number"])}function Kie(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return OE(s,o,l,u,p,i),r}var Xie={kernelName:Wl,backendName:"wasm",kernelFunc:Kie,setupFunc:qie},Yie=an(Ei),Zie=!1,Jie=cn($i,Zie),LE;function Qie(e){LE=e.wasm.cwrap(Ai,null,["number","number","number","number","number","number","number"])}function eoe(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return LE(p,d,c,h,m,r,g),f}var toe={kernelName:Ai,backendName:"wasm",setupFunc:Qie,kernelFunc:eoe},zE;function noe(e){zE=e.wasm.cwrap(Qs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function aoe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=ac[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,F=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,G=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return zE(y,q,K,Z,b,I,T,w,C,E,$,R,G,F,S,M,B,U,x,g,ae,m||0,ee),J}var roe={kernelName:Qs,backendName:"wasm",setupFunc:noe,kernelFunc:aoe},WE;function soe(e){WE=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ioe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=ac[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,F=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,G=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return WE(y,q,K,Z,b,I,T,w,C,E,$,R,G,F,S,M,B,U,x,g,ae,m||0,ee),J}var ooe={kernelName:ei,backendName:"wasm",setupFunc:soe,kernelFunc:ioe},BE;function loe(e){BE=e.wasm.cwrap(Vl,null,["number","number","number","number","number","number","array","number"])}function uoe(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Mx.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return BE(c,Et[a.dtype],h,i,d,o,m,f),u}var poe={kernelName:Vl,backendName:"wasm",setupFunc:loe,kernelFunc:uoe},VE;function coe(e){VE=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function doe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C<u.length;++C){let E=u[C];v.assert(E<=p-1&&E>=0,()=>`GatherV2: the index value ${E} is not in [0, ${p-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Wn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=Wn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let y=c.shape.length-1,b=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,w=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return VE(b,Et[r.dtype],I,y,x,d.batchSize,T,w),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var hoe={kernelName:Bl,backendName:"wasm",setupFunc:coe,kernelFunc:doe},moe=!1,foe=cn(Ul,moe,"bool"),goe=!1,yoe=cn(Fi,goe,"bool"),UE;function boe(e){UE=e.wasm.cwrap(Ri,null,["number","number","number","number"])}function xoe(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;UE(r,Et[t.dtype],n,i)}return s}var voe={kernelName:Ri,backendName:"wasm",setupFunc:boe,kernelFunc:xoe},woe=!1,koe=cn(ql,woe,"bool"),Ioe=!1,Soe=cn(Kl,Ioe,"bool"),Toe=an(Mi),Noe=!1,Coe=cn(Yl,Noe,"bool"),_oe=an(Zl),Eoe=!1,$oe=cn(Jl,Eoe,"bool"),Aoe=!1,Foe=cn(MI,Aoe,"bool"),GE;function Doe(e){GE=e.wasm.cwrap(Pi,null,["number","number","number","number"])}function Roe(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ss(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;N.assertAxesAreInnerMostDims("max",p,h);let[m,f]=N.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;GE(o,Et[i.dtype],g,b)}if(c&&t.disposeData(u.dataId),s){let b=N.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var Moe={kernelName:Pi,backendName:"wasm",setupFunc:Doe,kernelFunc:Roe},Poe=!1,Ooe=cn(Oi,Poe),HE;function Loe(e){HE=e.wasm.cwrap(Li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zoe(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,b=p.dilationWidth,x=p.strideHeight,w=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),E=a.dataIdMap.get(C.dataId).id;return HE(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,y,b,x,w,I,T,E),C}var Woe={kernelName:Li,backendName:"wasm",setupFunc:Loe,kernelFunc:zoe},jE;function Boe(e){jE=e.wasm.cwrap(zi,null,["number, number, number"])}function Voe(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ss(i,r,t),m=d;if(h){let w=t.dataIdMap.get(p.dataId).id;w!==o&&(u=p,l=w,m=N.getInnerMostAxes(m.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=N.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Hu({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let w=t.dataIdMap.get(x.dataId).id;jE(l,y,w)}if(h&&t.disposeData(p.dataId),s){let w=N.expandShapeToKeepDim(x.shape,c);x.shape=w}return u.dtype!=="float32"&&t.disposeData(b.dataId),x}var Uoe={kernelName:zi,backendName:"wasm",setupFunc:Boe,kernelFunc:Voe},qE;function Goe(e){qE=e.wasm.cwrap(Wi,null,["number","number","number","number"])}function Hoe(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ss(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;N.assertAxesAreInnerMostDims("min",d,m);let[f,g]=N.computeOutAndReduceShapes(u.shape,d),y=v.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;qE(l,Et[i.dtype],y,x)}if(h&&t.disposeData(p.dataId),s){let x=N.expandShapeToKeepDim(b.shape,c);b.shape=x}return b}var joe={kernelName:Wi,backendName:"wasm",setupFunc:Goe,kernelFunc:Hoe},qoe=!1,Koe=cn(Bi,qoe),fx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(fx||(fx={}));var KE;function Xoe(e){KE=e.wasm.cwrap(Vi,null,["number","array","number","number","array","array","number","number"])}function Yoe(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(d).buffer);return KE(i,u,t.shape.length,Et[t.dtype],c,h,fx[r],l),o}var Zoe={kernelName:Vi,backendName:"wasm",kernelFunc:Yoe,setupFunc:Xoe},Joe=!0,Qoe=cn(Ui,Joe),ele=an(eu);function t1(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var XE;function tle(e){XE=e.wasm.cwrap(nu,"number",["number","number","number","number","number"])}function nle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=XE(u,p,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=t1(t,d);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var ale={kernelName:nu,backendName:"wasm",setupFunc:tle,kernelFunc:nle},YE;function rle(e){YE=e.wasm.cwrap(au,"number",["number","number","number","number","number","bool"])}function sle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=YE(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=t1(t,c);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var ile={kernelName:au,backendName:"wasm",setupFunc:rle,kernelFunc:sle},ZE;function ole(e){ZE=e.wasm.cwrap(ru,"number",["number","number","number","number","number","number"])}function lle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=ZE(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=t1(t,c);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var ule={kernelName:ru,backendName:"wasm",setupFunc:ole,kernelFunc:lle},ple=!1,cle=cn(tu,ple,"bool"),JE;function dle(e){JE=e.wasm.cwrap(Gi,null,["number","number","number","number","number"])}function hle(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=n.makeOutput([...r.shape,i],s),p=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(r.dataId).id;return JE(d,i,o,l,p),u}var mle={kernelName:Gi,backendName:"wasm",setupFunc:dle,kernelFunc:hle};function fle(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var gle={kernelName:su,backendName:"wasm",kernelFunc:fle};function yle(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return mx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching 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Ile(e){t$=e.wasm.cwrap(qi,null,["number","number","number"])}function Sle(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=s,l=a,u=l;l.dtype!=="float32"&&(u=Hu({backend:n,inputs:{x:a},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(u.dataId).id);let p=n.makeOutput(a.shape,"float32"),d=n.dataIdMap.get(p.dataId).id;return t$(o,i,d),l.dtype!=="float32"&&n.disposeData(u.dataId),p}var Tle={kernelName:qi,backendName:"wasm",setupFunc:Ile,kernelFunc:Sle},n$;function Nle(e){n$=e.wasm.cwrap(Ki,null,["number","number","number","number"])}function Cle(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ss(i,r,t),m=d;if(h){let 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Lr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Rr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(xa(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof et.getEnv().Canvas?a:Hf(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return hr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return s1({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,P(()=>{let a=hr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Te){let o=xa(i)?i:Zt(i);return o=_$(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=za.resizeBilinear(o,[t,t],!1,!1)),o.as3D(t,t,3)}if(i instanceof et.getEnv().Canvas)return co.fromPixels(R$(i,t,n));throw new 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n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function O$(e,t){let{manifestUri:n,modelBaseUri:a}=jf(e,t),r=await M$(n);return Ut.loadWeights(r,a)}function _ve(e,t,n=!1){let{width:a,height:r}=n?Qu(t):t;return e.width=a,e.height=r,{width:a,height:r}}var dn=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return 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dn{constructor(n,a){super(n);this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof Lr?this.faceFeatureExtractor.forwardInput(n):n;return md(r.as2D(r.shape[0],-1),a.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return W$(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=Jf(n);return this.faceFeatureExtractor.loadFromWeightMap(a),B$(r)}extractParams(n){let a=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*a+r,i=n.slice(0,n.length-s),o=n.slice(n.length-s);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(o)}};var V$=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Cs=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);V$.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return V$.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Qf=class extends sp{constructor(t=new rp){super("FaceExpressionNet",t)}forwardInput(t){return P(()=>Ka(this.runNet(t)))}async forward(t){return this.forwardInput(await vt(t))}async predictExpressions(t){let n=await vt(t),a=await this.forwardInput(n),r=await Promise.all(ct(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Cs(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 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m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:c,middle_flow:h,exit_flow:g}}}function Mpe(e,t){let n=ra(e,t),a=Yf(n),r=ap(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:p}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function q$(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Mpe(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),p={conv_in:o,reduction_block_0:l,reduction_block_1:u},d={};hr(t,0,1).forEach(f=>{d[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return $n(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function K$(e,t,n){return Y(At(e,t.filters,n,"same"),t.bias)}function m1(e,t,n=!0){let a=n?Xe(e):e;return a=Gn(a,t.separable_conv0,[1,1]),a=Gn(Xe(a),t.separable_conv1,[1,1]),a=Dt(a,[3,3],[2,2],"same"),a=Y(a,K$(e,t.expansion_conv,[2,2])),a}function Ppe(e,t){let n=Gn(Xe(e),t.separable_conv0,[1,1]);return n=Gn(Xe(n),t.separable_conv1,[1,1]),n=Gn(Xe(n),t.separable_conv2,[1,1]),n=Y(n,e),n}var ng=class extends dn{constructor(n){super("TinyXception");this._numMainBlocks=n}forwardInput(n){let{params:a}=this;if(!a)throw new Error("TinyXception - load model before inference");return P(()=>{let r=le(n.toBatchTensor(112,!0),"float32"),i=mr(r,[122.782,117.001,104.298]).div(255),o=Xe(K$(i,a.entry_flow.conv_in,[2,2]));return o=m1(o,a.entry_flow.reduction_block_0,!1),o=m1(o,a.entry_flow.reduction_block_1),hr(this._numMainBlocks,0,1).forEach(l=>{o=Ppe(o,a.middle_flow[`main_block_${l}`])}),o=m1(o,a.exit_flow.reduction_block),o=Xe(Gn(o,a.exit_flow.separable_conv,[1,1])),o})}async forward(n){return this.forwardInput(await vt(n))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(n){return q$(n,this._numMainBlocks)}extractParams(n){return j$(n,this._numMainBlocks)}};function X$(e){let t=[],{extractWeights:n,getRemainingWeights:a}=An(e),r=Kf(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function Y$(e){let t=[],n=ra(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return $n(e,t),{params:r,paramMappings:t}}var f1=(n=>(n.FEMALE="female",n.MALE="male",n))(f1||{});var ag=class extends dn{constructor(n=new ng(2)){super("AgeGenderNet");this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof Lr?this.faceFeatureExtractor.forwardInput(n):n,s=ga(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=md(s,a.fc.age).as1D(),o=md(s,a.fc.gender);return{age:i,gender:o}})}forwardInput(n){return P(()=>{let{age:a,gender:r}=this.runNet(n);return{age:a,gender:Ka(r)}})}async forward(n){return this.forwardInput(await vt(n))}async predictAgeAndGender(n){let a=await vt(n),r=await 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og=e=>typeof e=="number";function yA(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!og(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>og(t.x)&&og(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(og)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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bA(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=An(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=Kpe(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,y,b,x]=a,w=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),T=u(h,m,"conv2"),C=u(m,f,"conv3"),E=u(f,g,"conv4"),$=u(g,y,"conv5"),R=b?u(y,b,"conv6"):void 0,F=x?u(b,x,"conv7"):void 0,S=o(x||b||y,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:E,conv5:$,conv6:R,conv7:F,conv8:S}}else{let[d,c,h,m,f,g,y,b,x]=a,w=l(d,c,"conv0"),I=l(c,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),E=l(f,g,"conv4"),$=l(g,y,"conv5"),R=l(y,b,"conv6"),F=l(b,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:E,conv5:$,conv6:R,conv7:F,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Xpe(e,t){let n=ra(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=ap(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function xA(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Xpe(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return $n(e,n),{params:i,paramMappings:n}}var fr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var w1=class extends dn{constructor(n){super("TinyYolov2");yA(n),this._config=n}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(n,a){let r=zr(n,a.conv0);return r=Dt(r,[2,2],[2,2],"same"),r=zr(r,a.conv1),r=Dt(r,[2,2],[2,2],"same"),r=zr(r,a.conv2),r=Dt(r,[2,2],[2,2],"same"),r=zr(r,a.conv3),r=Dt(r,[2,2],[2,2],"same"),r=zr(r,a.conv4),r=Dt(r,[2,2],[2,2],"same"),r=zr(r,a.conv5),r=Dt(r,[2,2],[1,1],"same"),r=zr(r,a.conv6),r=zr(r,a.conv7),No(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?pp(No(n,a.conv0,"valid",!1)):Wr(n,a.conv0);return r=Dt(r,[2,2],[2,2],"same"),r=Wr(r,a.conv1),r=Dt(r,[2,2],[2,2],"same"),r=Wr(r,a.conv2),r=Dt(r,[2,2],[2,2],"same"),r=Wr(r,a.conv3),r=Dt(r,[2,2],[2,2],"same"),r=Wr(r,a.conv4),r=Dt(r,[2,2],[2,2],"same"),r=Wr(r,a.conv5),r=Dt(r,[2,2],[1,1],"same"),r=a.conv6?Wr(r,a.conv6):r,r=a.conv7?Wr(r,a.conv7):r,No(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return P(()=>{let s=le(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?mr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await vt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new fr(a),i=await vt(n),o=await this.forwardInput(i,r),l=P(()=>ct(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(y=>y.box),c=p.map(y=>y.score),h=p.map(y=>y.classScore),m=p.map(y=>this.config.classes[y.label]);return C$(d.map(y=>y.rescale(r)),c,this.config.iouThreshold,!0).map(y=>new Ts(c[y],h[y],m[y],d[y],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return xA(n,this.config)}extractParams(n){let a=this.config.filterSizes||w1.DEFAULT_FILTER_SIZES,r=a?a.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return bA(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let b=n.reshape([p,p,d,this.boxEncodingSize]),x=b.slice([0,0,0,0],[p,p,d,4]),w=b.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Ka(b.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):xe(0);return[x,w,I]}),f=[],g=await h.array(),y=await c.array();for(let b=0;b<p;b++)for(let x=0;x<p;x++)for(let w=0;w<d;w++){let I=Bf(g[b][x][w][0]);if(!r||I>r){let T=(x+Bf(y[b][x][w][0]))/p*l,C=(b+Bf(y[b][x][w][1]))/p*u,E=Math.exp(y[b][x][w][2])*this.config.anchors[w].x/p*l,$=Math.exp(y[b][x][w][3])*this.config.anchors[w].y/p*u,R=T-E/2,F=C-$/2,S={row:b,col:x,anchor:w},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new qu(R,F,R+E,F+$),score:I,classScore:I*M,label:B,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},Eo=w1;Eo.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var cp=class extends Eo{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:cA,classes:["face"],...t?{anchors:hA,meanRgb:mA}:{anchors:dA,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new xt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?gA:fA}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function HSe(e,t=!0){let n=new cp(t);return n.extractWeights(e),n}var lg=class extends fr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Pa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function $o(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>ip(l)?r(l):l.detection),i=a||(t instanceof Te?await cd(t,s):await pd(t,s)),o=await n(i);return i.forEach(l=>l instanceof Te&&l.dispose()),o}async function dp(e,t,n,a,r){return $o([e],t,async s=>n(s[0]),a,r)}var vA=.4,wA=[new Me(1.603231,2.094468),new Me(6.041143,7.080126),new Me(2.882459,3.518061),new Me(4.266906,5.178857),new Me(9.041765,10.66308)],kA=[117.001,114.697,97.404];var hp=class extends Eo{constructor(){let t={withSeparableConvs:!0,iouThreshold:vA,classes:["face"],anchors:wA,meanRgb:kA,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new xt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var tt={ssdMobilenetv1:new _o,tinyFaceDetector:new hp,tinyYolov2:new cp,faceLandmark68Net:new lp,faceLandmark68TinyNet:new sg,faceRecognitionNet:new up,faceExpressionNet:new Qf,ageGenderNet:new ag},Ype=(e,t)=>tt.ssdMobilenetv1.locateFaces(e,t),wTe=(e,t)=>tt.tinyFaceDetector.locateFaces(e,t),kTe=(e,t)=>tt.tinyYolov2.locateFaces(e,t),Zpe=e=>tt.faceLandmark68Net.detectLandmarks(e),ITe=e=>tt.faceLandmark68TinyNet.detectLandmarks(e),STe=e=>tt.faceRecognitionNet.computeFaceDescriptor(e),TTe=e=>tt.faceExpressionNet.predictExpressions(e),NTe=e=>tt.ageGenderNet.predictAgeAndGender(e),Jpe=e=>tt.ssdMobilenetv1.load(e),CTe=e=>tt.tinyFaceDetector.load(e),_Te=e=>tt.tinyYolov2.load(e),ETe=e=>tt.faceLandmark68Net.load(e),$Te=e=>tt.faceLandmark68TinyNet.load(e),ATe=e=>tt.faceRecognitionNet.load(e),FTe=e=>tt.faceExpressionNet.load(e),DTe=e=>tt.ageGenderNet.load(e),RTe=Jpe,MTe=Ype,PTe=Zpe;var ug=class extends Pa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Ao=class extends ug{async run(){let t=await this.parentTask,n=await $o(t,this.input,async a=>Promise.all(a.map(r=>tt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>h1(a,n[r]))}withAgeAndGender(){return new Do(this,this.input)}},Fo=class extends ug{async run(){let t=await this.parentTask;if(!t)return;let n=await dp(t,this.input,a=>tt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return h1(t,n)}withAgeAndGender(){return new Ro(this,this.input)}},_s=class extends Ao{withAgeAndGender(){return new $s(this,this.input)}withFaceDescriptors(){return new Fs(this,this.input)}},Es=class extends Fo{withAgeAndGender(){return new As(this,this.input)}withFaceDescriptor(){return new Ds(this,this.input)}};var pg=class extends Pa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Do=class extends pg{async run(){let t=await this.parentTask,n=await $o(t,this.input,async a=>Promise.all(a.map(r=>tt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return x1(v1(a,i,o),s)})}withFaceExpressions(){return new Ao(this,this.input)}},Ro=class extends pg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await dp(t,this.input,s=>tt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return x1(v1(t,a,r),n)}withFaceExpressions(){return new Fo(this,this.input)}},$s=class extends Do{withFaceExpressions(){return new _s(this,this.input)}withFaceDescriptors(){return new Fs(this,this.input)}},As=class extends Ro{withFaceExpressions(){return new Es(this,this.input)}withFaceDescriptor(){return new Ds(this,this.input)}};var cg=class extends Pa{constructor(n,a){super();this.parentTask=n;this.input=a}},Fs=class extends cg{async run(){let t=await this.parentTask;return(await $o(t,this.input,a=>Promise.all(a.map(r=>tt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>b1(t[r],a))}withFaceExpressions(){return new _s(this,this.input)}withAgeAndGender(){return new $s(this,this.input)}},Ds=class extends cg{async run(){let t=await this.parentTask;if(!t)return;let n=await dp(t,this.input,a=>tt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return b1(t,n)}withFaceExpressions(){return new Es(this,this.input)}withAgeAndGender(){return new As(this,this.input)}};var dg=class extends Pa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?tt.faceLandmark68TinyNet:tt.faceLandmark68Net}},hg=class extends dg{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Te?await cd(this.input,n):await pd(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Te&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>fd(i,r[o]))}withFaceExpressions(){return new _s(this,this.input)}withAgeAndGender(){return new $s(this,this.input)}withFaceDescriptors(){return new Fs(this,this.input)}},mg=class extends dg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Te?await cd(this.input,[n]):await pd(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Te&&s.dispose()),fd(t,r)}withFaceExpressions(){return new Es(this,this.input)}withAgeAndGender(){return new As(this,this.input)}withFaceDescriptor(){return new Ds(this,this.input)}};var fg=class extends Pa{constructor(n,a=new Ma){super();this.input=n;this.options=a}},yd=class extends fg{async run(){let{input:t,options:n}=this,a;if(n instanceof lg)a=tt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Ma)a=tt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof fr)a=tt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>Yu({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new hg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ao(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Do(this.runAndExtendWithFaceDetections(),this.input)}},gg=class extends fg{async run(){let t=await new yd(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Yu({},n):void 0)})}withFaceLandmarks(t=!1){return new mg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Fo(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ro(this.runAndExtendWithFaceDetection(),this.input)}};function DNe(e,t=new Ma){return new gg(e,t)}function k1(e,t=new Ma){return new yd(e,t)}async function Qpe(e,t){return k1(e,new Ma(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function zNe(e,t={}){return k1(e,new fr(t)).withFaceLandmarks().withFaceDescriptors()}var WNe=Qpe;function IA(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s*s,0))}var yg=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Mr)return i;if(i instanceof Float32Array)return new Mr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Mr(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>IA(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new id(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this._distanceThreshold?n:new id("unknown",n.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Mr.fromJSON(a));return new yg(n,t.distanceThreshold)}};function r2e(e){let t=new hp;return t.extractWeights(e),t}function ece(e,t){let{width:n,height:a}=new En(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>ece(r,{width:n,height:a}));if(ip(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return fd(Yu(e,r),s)}return Pr(e)?Yu(e,e.detection.forSize(n,a)):e instanceof va||e instanceof xt?e.forSize(n,a):e}var f2e=H$;export{ag as AgeGenderNet,qu as BoundingBox,ut as Box,Pa as ComposableTask,Fs as ComputeAllFaceDescriptorsTask,cg as ComputeFaceDescriptorsTaskBase,Ds as ComputeSingleFaceDescriptorTask,hg as DetectAllFaceLandmarksTask,yd as DetectAllFacesTask,dg as DetectFaceLandmarksTaskBase,fg as DetectFacesTaskBase,mg as DetectSingleFaceLandmarksTask,gg as DetectSingleFaceTask,En as Dimensions,V$ as FACE_EXPRESSION_LABELS,xt as FaceDetection,pA as FaceDetectionNet,Qf as FaceExpressionNet,Cs as FaceExpressions,lp as FaceLandmark68Net,sg as FaceLandmark68TinyNet,Q$ as FaceLandmarkNet,va as FaceLandmarks,E$ as FaceLandmarks5,Xu as FaceLandmarks68,id as FaceMatch,yg as FaceMatcher,up as FaceRecognitionNet,f1 as Gender,od as LabeledBox,Mr as LabeledFaceDescriptors,Lr as NetInput,dn as NeuralNetwork,Ts as ObjectDetection,Me as Point,$$ as PredictedBox,Ku as Rect,_o as SsdMobilenetv1,Ma as SsdMobilenetv1Options,hp as TinyFaceDetector,lg as TinyFaceDetectorOptions,cp as TinyYolov2,fr as TinyYolov2Options,WNe as allFaces,Qpe as allFacesSsdMobilenetv1,zNe as allFacesTinyYolov2,A$ as awaitMediaLoaded,F$ as bufferToImage,STe as computeFaceDescriptor,ep as createCanvas,Hf as createCanvasFromMedia,eSe as createFaceDetectionNet,eIe as createFaceRecognitionNet,qpe as createSsdMobilenetv1,r2e as createTinyFaceDetector,HSe as createTinyYolov2,k1 as detectAllFaces,Zpe as detectFaceLandmarks,ITe as detectFaceLandmarksTiny,PTe as detectLandmarks,DNe as detectSingleFace,G$ as draw,et as env,IA as euclideanDistance,x1 as extendWithAge,b1 as extendWithFaceDescriptor,Yu as extendWithFaceDetection,h1 as extendWithFaceExpressions,fd as extendWithFaceLandmarks,v1 as extendWithGender,cd as extractFaceTensors,pd as extractFaces,pve as fetchImage,M$ as fetchJson,fve as fetchNetWeights,Ns as fetchOrThrow,wve as fetchVideo,aa as getContext2dOrThrow,Qu as getMediaDimensions,D$ as imageTensorToCanvas,R$ as imageToSquare,Tye as inverseSigmoid,T$ as iou,d1 as isMediaElement,Gf as isMediaLoaded,rIe as isWithAge,Pr as isWithFaceDetection,U$ as isWithFaceExpressions,ip as isWithFaceLandmarks,lIe as isWithGender,DTe as loadAgeGenderModel,RTe as loadFaceDetectionModel,FTe as loadFaceExpressionModel,ETe as loadFaceLandmarkModel,$Te as loadFaceLandmarkTinyModel,ATe as loadFaceRecognitionModel,Jpe as loadSsdMobilenetv1Model,CTe as loadTinyFaceDetectorModel,_Te as loadTinyYolov2Model,O$ as loadWeightMap,MTe as locateFaces,_ve as matchDimensions,N$ as minBbox,tt as nets,C$ as nonMaxSuppression,mr as normalize,_$ as padToSquare,NTe as predictAgeAndGender,TTe as recognizeFaceExpressions,ece as resizeResults,Zu as resolveInput,Iye as shuffleArray,Bf as sigmoid,Ype as ssdMobilenetv1,Oe as tf,wTe as tinyFaceDetector,kTe as tinyYolov2,vt as toNetInput,S$ as utils,yA as validateConfig,f2e as version};
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