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o,l=U1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(U1(e)){let{kernelName:p,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let A=Wh(p,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let _=g.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:x,dtype:N}=b;return this.makeTensorFromDataId(w,x,N)});if(r){let b=this.getTensorsForGradient(p,m,_);n=this.saveTensorsForBackwardMode(b)}return _}}else{let{forwardFunc:p}=e,m=f=>{!r||(n=f.map(A=>this.keep(this.clone(A))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,m));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,A),A}}let{inputs:u,attrs:c}=e,h=U1(e)?null:e.backwardsFunc,d;return 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*T1(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 bu||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*T1(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 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c=C.computePool3DInfo(s.shape,i,o,1,l,u),h=c.strideDepth,d=c.strideHeight,p=c.strideWidth,m=c.filterDepth,f=c.filterHeight,A=c.filterWidth,y=c.dilationDepth,g=c.dilationHeight,_=c.dilationWidth,b=c.effectiveFilterDepth,w=c.effectiveFilterHeight,x=c.effectiveFilterWidth,N=b-1-c.padInfo.front,T=x-1-c.padInfo.left,E=w-1-c.padInfo.top,M=Le(s.shape,"float32"),D=1/(m*f*A),L=n.bufferSync(a);for(let W=0;W=c.outDepth||Math.floor(Q)!==Q))for(let he=0;he=c.outHeight||Math.floor(oe)!==oe))for(let fe=0;fe=c.outWidth||Math.floor(pe)!==pe||(te+=L.get(W,Q,oe,pe,U))}}}M.set(te*D,W,H,X,G,U)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var _F={kernelName:dh,backendName:"cpu",kernelFunc:wF};function 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o=s.reduce((y,g)=>y*g),l=C.getReshaped(a.shape,s,o),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(c,i,s.length),p=At({inputs:{x:a},backend:n,attrs:{shape:l}}),m=ir({inputs:{x:p},backend:n,attrs:{perm:u}}),f=At({inputs:{x:m},backend:n,attrs:{shape:c}}),A=oi({inputs:{x:f},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),A}var SF={kernelName:tu,backendName:"cpu",kernelFunc:NF};function TF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,u=nm(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var EF={kernelName:ph,backendName:"cpu",kernelFunc:TF},CF=at(xa,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,r=new 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At({inputs:{x:f},backend:n,attrs:{shape:[-1,A]}})}),c=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=C.computeOutShape(u.map(f=>f.shape),1);let h=u[0].shape[0]===1,d=rm(c,i,t[0].dtype,h),p=C.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(p,t[0].dtype,d);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var OF={kernelName:Ki,backendName:"cpu",kernelFunc:fl};function Xx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r;_e([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,_=d.dataFormat==="channelsLast",b=new 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YF(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,[c,h,d,p]=a.shape,m=s.shape[0],[f,A]=o,y=Le([m,f,A,p],"float32"),g=n.data.get(s.dataId).values,_=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,w=v.computeStrides(a.shape),x=v.computeStrides(y.shape);for(let N=0;N=c)continue;let U=f>1?(D-E)*(h-1)/(f-1):0,H=A>1?(L-M)*(d-1)/(A-1):0;for(let X=0;X1?E*(h-1)+X*U:.5*(E+D)*(h-1);if(G<0||G>h-1){for(let ee=0;ee1?M*(d-1)+te*H:.5*(M+L)*(d-1);if(ie<0||ie>d-1){for(let fe=0;fe1?M*(d-1)+ee*H:.5*(M+L)*(d-1);if(Y<0||Y>d-1){for(let ie=0;iey+m-g-1:(y,g)=>y+g;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. 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WM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=Xx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=f;f=ju({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=f;f=cm(n,f,p,o,m),n.disposeIntermediateTensorInfo(A)}return f}var BM={kernelName:js,backendName:"cpu",kernelFunc:WM};function VM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=Kx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=f;f=ju({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=f;f=cm(n,f,p,o,m),n.disposeIntermediateTensorInfo(A)}return f}var UM={kernelName:Gs,backendName:"cpu",kernelFunc:VM};function HM(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=v.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,u,c,h]=C.prepareAndValidate(r,a);if(u===0)return n.makeTensorInfo(l,r.dtype,[]);let d=Le([u,c],r.dtype),p=n.data.get(a.dataId).values,m=n.data.get(r.dataId).values;for(let f=0;f=s/c)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;ge>=t?1:0),KM=Ht(fs,XM,null,"bool"),ZM={kernelName:fs,backendName:"cpu",kernelFunc:KM};function YM(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=At({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=Yx(o,!0,n),u=At({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var JM={kernelName:Nh,backendName:"cpu",kernelFunc:YM},QM=at(lo,e=>Number.isFinite(e)?1:0,"bool"),e$={kernelName:lo,backendName:"cpu",kernelFunc:QM},t$=at(uo,e=>Math.abs(e)===Infinity?1:0,"bool"),n$={kernelName:uo,backendName:"cpu",kernelFunc:t$},r$=at(co,e=>Number.isNaN(e)?1:0,"bool"),a$={kernelName:co,backendName:"cpu",kernelFunc:r$},s$=Ct((e,t)=>e<=t?1:0),i$=Ht(po,s$,null,"bool"),o$={kernelName:po,backendName:"cpu",kernelFunc:i$};function l$(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=vx(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var u$={kernelName:Th,backendName:"cpu",kernelFunc:l$},c$=at(fo,e=>Math.log1p(e)),h$={kernelName:fo,backendName:"cpu",kernelFunc:c$},d$=Ct((e,t)=>e&&t),p$=Ht(mo,d$,null,"bool"),f$={kernelName:mo,backendName:"cpu",kernelFunc:p$},m$=at(iu,e=>e?0:1,"bool"),A$={kernelName:iu,backendName:"cpu",kernelFunc:m$},y$=Ct((e,t)=>e||t),g$=Ht(ou,y$,null,"bool"),x$={kernelName:ou,backendName:"cpu",kernelFunc:g$};function w$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;_e(a,"LRN");let u=a.shape[3],c=u-1,h=n.data.get(a.dataId).values,d=v.sizeFromShape(a.shape),p=new Float32Array(d);function m(f){let A=f%u,y=f-A+Math.max(0,A-s),g=f-A+Math.min(A+s,c),_=0;for(;y<=g;y++){let b=h[y];_+=b*b}return _}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. 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c=C.computePool3DInfo(s.shape,i,o,1,l,u),h=n.bufferSync(s),d=mF(h,c),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,A=c.dilationDepth,y=c.dilationHeight,g=c.dilationWidth,_=c.effectiveFilterDepth,b=c.effectiveFilterHeight,w=c.effectiveFilterWidth,x=_-1-c.padInfo.front,N=w-1-c.padInfo.left,T=b-1-c.padInfo.top,E=Le(s.shape,"float32"),M=n.bufferSync(a);for(let D=0;D=c.outDepth||Math.floor(te)!==te))for(let ie=0;ie=c.outHeight||Math.floor(Q)!==Q))for(let he=0;he=c.outWidth||Math.floor(oe)!==oe)continue;let fe=_*b*w-1-d.get(D,te,Q,oe,L),pe=ae*b*w+ie*w+he,ve=fe===pe?1:0;ve!==0&&(Y+=M.get(D,te,Q,oe,L)*ve)}}}E.set(Y,D,W,U,H,L)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var C$={kernelName:Rh,backendName:"cpu",kernelFunc:E$};function R$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;_e([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,u,1,c,h),p=n.data.get(o.dataId).values,m=Le(d.outShape,o.dtype,Gx(p,o.shape,o.dtype,d).values),f=d.strideHeight,A=d.strideWidth,y=d.dilationHeight,g=d.dilationWidth,_=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=b-1-d.padInfo.left,x=_-1-d.padInfo.top,N=Le(o.shape,"float32"),T=n.data.get(a.dataId).values,E=Le(a.shape,"float32",T);for(let M=0;M=d.outHeight||Math.floor(ee)!==ee))for(let Y=0;Y=d.outWidth||Math.floor(ae)!==ae)continue;let te=_*b-1-m.get(M,ee,ae,D),ie=G*b+Y,Q=te===ie?1:0;Q!==0&&(X+=E.get(M,ee,ae,D)*Q)}}N.set(X,M,L,W,D)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var F$={kernelName:Ch,backendName:"cpu",kernelFunc:R$};function M$(e,t,n,r,a){let s=v.computeStrides(t),i=hm(e,t,n,s,a,"max"),o=Gx(e,t,n,a,!0,r);return[i.values,o.values]}var $$={kernelName:Fh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;_e(r,"MaxPoolWithArgmax");let u=l.data.get(r.dataId).values,c=C.computePool2DInfo(r.shape,a,s,[1,1],i),[h,d]=M$(u,r.shape,r.dtype,o,c),p=l.write(h,c.outShape,r.dtype),m=l.write(d,c.outShape,r.dtype);return[{dataId:p,shape:c.outShape,dtype:r.dtype},{dataId:m,shape:c.outShape,dtype:"int32"}]}};function Wd(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"sum");let o;a.dtype==="bool"?o=Fa({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=Dr({inputs:{x:a},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),c=C.getAxesPermutation(u,l),h=u,d=o;c!=null&&(d=ir({inputs:{x:o},backend:n,attrs:{perm:c}}),h=C.getInnerMostAxes(h.length,l)),C.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,m]=C.computeOutAndReduceShapes(d.shape,h),f=C.upcastType(d.dtype,"int32"),A=Pd(n,p,f),y=v.sizeFromShape(m),g=n.data.get(A.dataId).values,_=n.data.get(d.dataId).values;for(let b=0;bn.disposeIntermediateTensorInfo(f)),m}var z$={kernelName:_s,backendName:"cpu",kernelFunc:D$};function P$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"min");let o=v.parseAxisParam(s,a.shape),l=o,u=C.getAxesPermutation(l,a.shape.length),c=a;u!=null&&(c=ir({inputs:{x:a},backend:n,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",l,c.shape.length);let[h,d]=C.computeOutAndReduceShapes(c.shape,l),p=v.sizeFromShape(d),m=v.makeZerosTypedArray(v.sizeFromShape(h),c.dtype),f=n.data.get(c.dataId).values;for(let 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p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Er().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=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,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);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 J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().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:r,usage:a,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(r,n),this.textureManager.releaseTexture(t,r,a,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}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Er().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=NP){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Er().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new _P(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new iP(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[li(e.shape),...ui(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[li(t),...ui(t)],s=new t_(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=Hd(r),i;n?i=new LD(s):i=new PD(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Ku.DENSE){let f=Yu(e.outputShape);i.texShape=f.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(f.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(f.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=f.shape)}else if(!!A.isPacked!=!!e.packedInputs)f=A.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),A=this.texData.get(f.dataId);else if(A.isPacked&&!Xu(A.shape,f.shape)){let y=f,g=f.shape;f.shape=A.shape,f=this.packedReshape(f,g),o.push(f),A=this.texData.get(f.dataId),y.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=Ez(e,l,u),h=this.getAndSaveBinary(c,()=>Sz(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),Tz(this.gpgpu,h,l,u,r),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=v.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(be(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?vP:kP}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=bw(n,o),t.texShape=c),a!=null){let h=Hd(n),d,p=c[1],m=c[0],f=a instanceof Uint8Array;o?([p,m]=Al(c[0],c[1]),d=new UD(h,[m,p],f)):d=new VD(h,[m,p],f);let A=this.makeTensorInfo([m,p],r);f?this.texData.get(A.dataId).usage=Xn.PIXELS:this.texData.get(A.dataId).usage=Xn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,m,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),_=this.texData.get(g.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=EP(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};bl.nextDataId=0;function EP(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 r=0;rnew bl,2);var CP={forceHalfFloat:o_},l_=` if (isnan(a)) return a; if (isnan(b)) return b; `,vl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOperation(float a, float b) { ${e} } void main() { float a = getAAtOutCoords(); float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } `}},Yd=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; `,Qu=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(s=` ${ot(a)} coords = getOutputCoords(); `,a===1)s+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let i=cn("coords",a);s+=` bool nextRowOutOfBounds = (${i[a-2]} + 1) >= ${this.outputShape[a-2]}; bool nextColOutOfBounds = (${i[a-1]} + 1) >= ${this.outputShape[a-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 $n(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var RP={kernelName:ms,backendName:"webgl",kernelFunc:$n};function $a(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=$n({inputs:{x:r},backend:n}),l=$n({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var FP={kernelName:fh,backendName:"webgl",kernelFunc:$a},u_="return (a < 0.) ? b * a : a;",c_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function MP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Qu(c_,a.shape,i.shape):new vl(u_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var $P={kernelName:As,backendName:"webgl",kernelFunc:MP},h_="return (a < 0.) ? b * a : a;",d_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function OP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Qu(d_,r.shape,a.shape):new vl(h_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var DP={kernelName:Ts,backendName:"webgl",kernelFunc:OP},p_="if (isnan(x)) return x;",zP=` if (isnan(a)) return a; if (isnan(b)) return b; `,PP=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; `;function qe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new _l(i.shape,t):c=new Ma(i.shape,e),o.runWebGLProgram(c,[i],l)}}function tn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let m=c.texData.get(l.dataId),f=c.texData.get(u.dataId),[A,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(_=>{let[b,w]=_,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:w.dataId,dtype:w.dtype,shape:u.shape},T=new vl(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,N],tr(b.dtype,w.dtype))}),g=$a({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||tr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let m=c.texData.get(l.dataId),f=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,m.values,f.values,h),g=c.makeTensorInfo(y,h),_=c.texData.get(g.dataId);return _.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new Qu(t,l.shape,u.shape,n):p=new vl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function Jd(e,t=!1){if(e==="linear")return t?yP:pP;if(e==="relu")return t?xP:mP;if(e==="elu")return t?gP:fP;if(e==="relu6")return t?wP:AP;if(e==="prelu")return t?d_:h_;if(e==="leakyrelu")return t?c_:u_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var f_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",A="";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) { ${i} }`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",_="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. 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int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${r}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); for (int i = 0; i < ${r}; i++) { int inIdx = ${o}; float candidate = getA(batch, inIdx); if (candidate ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}},AL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ot(o),u=cn("coords",o),c,h;if(s===1){h=o+1;let N=ot(h);c=` ${N} sourceLocR = ${N}(${u.join()}, 0); ++${u[o-1]}; ${N} sourceLocG = ${N}(${u.join()}, 0); ++${u[o-2]}; ${N} sourceLocA = ${N}(${u.join()}, 0); --${u[o-1]}; ${N} sourceLocB = ${N}(${u.join()}, 0); --${u[o-2]};`}else h=o,c=` ${l} sourceLocR = coords; ++${u[o-1]}; ${l} sourceLocG = coords; ++${u[o-2]}; ${l} sourceLocA = coords; --${u[o-1]}; ${l} sourceLocB = coords; --${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],m=d.map(N=>"int "+N),f=cn("sourceLocR",h-1).concat("inIdx.r"),A=cn("sourceLocG",h-1).concat("inIdx.g"),y=cn("sourceLocB",h-1).concat("inIdx.b"),g=cn("sourceLocA",h-1).concat("inIdx.a"),_=n==="max"?"greaterThan":"lessThan",b=r?"":` inIdx = round(vec4(getBestIndicesAChannel(${f.join()}), getBestIndicesAChannel(${A.join()}), getBestIndicesAChannel(${y.join()}), getBestIndicesAChannel(${g.join()})));`,w=`vec4( getAChannel(${f.join()}), hasNextCol ? getAChannel(${A.join()}) : 0., hasNextRow ? getAChannel(${y.join()}) : 0., hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,x=r?"":` float getBestIndicesAChannel(${m.join()}) { return getChannel(getBestIndicesA(${d.join()}), vec2(${d.slice(-2).join()})); }`;this.userCode=` float getAChannel(${m.join()}) { return getChannel(getA(${d.join()}), vec2(${d.slice(-2).join()})); } ${x} void main() { ${l} coords = getOutputCoords(); bool hasNextCol = ${u[o-1]} < ${i[o-1]-1}; bool hasNextRow = ${u[o-2]} < ${i[o-2]-1}; ${c} ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p}, sourceLocB${p}, sourceLocA${p}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${w}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; ${b} vec4 candidate = ${w}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${_}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); bestValue = vec4(replace.x ? candidate.x : bestValue.x, replace.y ? candidate.y : bestValue.y, replace.z ? candidate.z : bestValue.z, replace.w ? candidate.w : bestValue.w); bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace)); srcIdx++; } setOutput(bestIndex); } `}};function v_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new mL(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=v_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function k_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new AL(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=k_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function I_(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=ye({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=v_(e,u,r);s.push(c);let h=ye({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return k_(e,t,r)}function yL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=_n({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=I_(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var gL={kernelName:Qa,backendName:"webgl",kernelFunc:yL};function xL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=_n({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=I_(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var wL={kernelName:Ql,backendName:"webgl",kernelFunc:xL},_L=Ar+` if (abs(x) > 1.) { return NAN; } return asin(x); `,bL=qe({opSnippet:_L}),vL={kernelName:Hi,backendName:"webgl",kernelFunc:bL},kL=Ar+"return log(x + sqrt(x * x + 1.0));",IL=qe({opSnippet:kL}),NL={kernelName:ji,backendName:"webgl",kernelFunc:IL},SL=Ar+` return atan(x); `,TL=qe({opSnippet:SL}),EL={kernelName:Gi,backendName:"webgl",kernelFunc:TL},CL=zP+` return atan(a, b); `,RL=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+PP+` return result; `,FL=tn({opSnippet:CL,packedOpSnippet:RL}),ML={kernelName:Xi,backendName:"webgl",kernelFunc:FL},$L=Ar+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OL=qe({opSnippet:$L}),DL={kernelName:qi,backendName:"webgl",kernelFunc:OL},ec=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${p}); 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 < ${c}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${h}; 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 ${N} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${r?a?f:A:`wR * ${h} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let g="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let b=Math.floor(s/4)*4,w=s%4,x=` if (${m}) { avgValue += dot(values, ones); } else { minMaxValue = ${g}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${p}); 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 < ${c}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${b}; 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) ); ${x} } int xC = xCCorner + ${b}; if (${w===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${x} } else if (${w===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), initializationValue, initializationValue ); ${x} } else if (${w===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), getValue(batch, xR, xC + 2 * ${u}, d), initializationValue ); ${x} } } setOutput(${_}); } `}},Rm=class{constructor(e,t,n,r=!1,a=!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,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",_="0.0";if(g||(_="-1.0 / 1e-20"),n){let E=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${A}, ${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 < ${d}; wD += ${u}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${p}; wR += ${c}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${m}; wC += ${h}) { 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 ${E} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${p} * ${m} + wR * ${m} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let x=Math.floor(s/4)*4,N=s%4,T=` if (${g}) { avgValue += dot(values, ones); } else { minMaxValue = ${b}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${A}, ${y}); const float initializationValue = ${_}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${_}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${d}; wD += ${u}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${p}; wR += ${c}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${x}; wC += 4) { int xC = xCCorner + wC * ${h}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${h}, ch), getValue(batch, xD, xR, xC + 2 * ${h}, ch), getValue(batch, xD, xR, xC + 3 * ${h}, ch) ); ${T} } int xC = xCCorner + ${x}; if (${N===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${T} } else if (${N===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${h}, ch), initializationValue, initializationValue ); ${T} } else if (${N===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${h}, ch), getValue(batch, xD, xR, xC + 2 * ${h}, ch), initializationValue ); ${T} } } setOutput(${w}); } } `}};function zL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ml(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return $n({inputs:{x:a},backend:n});let h=new ec(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var PL={kernelName:es,backendName:"webgl",kernelFunc:zL};function LL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new Rm(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var WL={kernelName:eu,backendName:"webgl",kernelFunc:LL},BL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${c}); const float avgMultiplier = float(${h}); 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) / ${r}.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) / ${a}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},VL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,m=h-1-e.padInfo.top,f=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=` const ivec3 pads = ivec3(${p}, ${m}, ${f}); const float avgMultiplier = float(${A}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${c}; wD += ${o}) { float dyD = float(dyDCorner + wD) / ${a}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${h}; wR += ${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 < ${d}; 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 UL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new VL(d);return n.runWebGLProgram(p,[a],i.dtype)}var HL={kernelName:dh,backendName:"webgl",kernelFunc:UL};function jL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;ml([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=C.computePool2DInfo(i.shape,o,l,1,u),h=new BL(c);return n.runWebGLProgram(h,[a],i.dtype)}var GL={kernelName:hh,backendName:"webgl",kernelFunc:jL};function qL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return ep({a,b:s,transposeA:i,transposeB:o,backend:n})}var XL={kernelName:ts,backendName:"webgl",kernelFunc:qL},KL=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),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))); } `}},ZL=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),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); } `}},YL=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.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=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new ZL(r.shape,a.shape,s.shape,c,h,l):new KL(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},JL={kernelName:ps,backendName:"webgl",kernelFunc:YL},eW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=`uniform int start[${this.rank}];`,r=QL(this.rank),a,s=e.map((i,o)=>`sourceLoc.${Fm[o]} = start[${o}] + coords.${Fm[o]};`);a=` ${t} sourceLoc; 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uniform float maxVal; void main() { float value = getAAtOutCoords(); if (isnan(value)) { setOutput(value); return; } setOutput(clamp(value, minVal, maxVal)); } `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},yW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` uniform float minVal; uniform float maxVal; void main() { vec4 value = getAAtOutCoords(); if (any(isnan(value))) { setOutput(value); return; } setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function gW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new yW(a.shape):o=new AW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var xW={kernelName:xa,backendName:"webgl",kernelFunc:gW},wW=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 T_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function _W(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new wW(r.shape),i=[T_(r,a.complexTensorInfos.real),T_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var bW={kernelName:nu,backendName:"webgl",kernelFunc:_W},vW=class{constructor(e){this.outputShape=[],this.outputShape=C.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${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m= ${o[m-1]}) { return getChannel( getT${m}(${np(i,l,f)}), vec2(${np(u,l,f)})); }`}let d=o.length,p=o[o.length-1];h+=` return getChannel( getT${d}(${np(i,l,p)}), vec2(${np(u,l,p)}));`,this.userCode=` float getValue(${i.map(m=>"int "+m)}) { ${h} } void main() { ${a} coords = getOutputCoords(); vec4 result = vec4(getValue(${s}), 0., 0., 0.); ${s[r-1]} = ${s[r-1]} + 1; if (${s[r-1]} < ${n[r-1]}) { result.g = getValue(${s}); } ${s[r-2]} = ${s[r-2]} + 1; if (${s[r-2]} < ${n[r-2]}) { result.a = getValue(${s}); } ${s[r-1]} = ${s[r-1]} - 1; if (${s[r-2]} < ${n[r-2]} && ${s[r-1]} < ${n[r-1]}) { result.b = getValue(${s}); } setOutput(result); } `}};function np(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function rp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return $n({inputs:{x:a.complexTensorInfos.imag},backend:n})}var IW={kernelName:Sh,backendName:"webgl",kernelFunc:rp};function kl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(m=>nc({inputs:{input:m},backend:n})),c=e.map(m=>rp({inputs:{input:m},backend:n})),h=kl(u,t,n),d=kl(c,t,n),p=$a({inputs:{real:h,imag:d},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),c.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=E_(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=Mz(h,c,r,d),m=C.computeOutShape(e.map(A=>A.shape),t),f=n.makeTensorInfo(m,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),f}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=kl(e.slice(0,u),t,n),h=kl(e.slice(u),t,n),d=kl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new kW(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=E_(e,t,n),i=new vW(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=ye({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function E_(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ye({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function C_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return $n({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return C.assertParamsConsistent(l,s),kl(o,s,n)}var NW={kernelName:Ki,backendName:"webgl",kernelFunc:C_},R_=class{constructor(e,t=!1,n=null,r=!1,a=!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,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",A=f?1:2,y=f?2:3,g=f?3:1,_="",b="";n&&(r?_=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?_=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:_=` float activation(float x) { ${n} } `,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${_} const ivec2 strides = ivec2(${o}, ${l}); const ivec2 pads = ivec2(${s}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${g}]; ivec2 xRCCorner = ivec2(coords[${A}], coords[${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 < ${h}; wR++) { int xR = xRCorner + wR * ${u}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${d}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${p}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${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, ${p}) * getW(wR, wC, ${p}, d2); } else { dotProd += getX(batch, ${p}, xR, xC) * getW(wR, wC, ${p}, d2); } } else if (${m===2}) { vec2 wValues = vec2( getW(wR, wC, ${p}, d2), getW(wR, wC, ${p} + 1, d2) ); if (${f}) { vec2 xValues = vec2( getX(batch, xR, xC, ${p}), getX(batch, xR, xC, ${p} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${p}, xR, xC), getX(batch, ${p} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${m===3}) { vec3 wValues = vec3( getW(wR, wC, ${p}, d2), getW(wR, wC, ${p} + 1, d2), getW(wR, wC, ${p} + 2, d2) ); if (${f}) { vec3 xValues = vec3( getX(batch, xR, xC, ${p}), getX(batch, xR, xC, ${p} + 1), getX(batch, xR, xC, ${p} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( getX(batch, ${p}, xR, xC), getX(batch, ${p} + 1, xR, xC), getX(batch, ${p} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${w} ${b} setOutput(result); } `}},SW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${a}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${n}, ${r}); 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 < ${c}; wF++) { int xF = xFCorner + wF * ${o}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR++) { int xR = xRCorner + wR * ${l}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${d}; wC++) { int xC = xCCorner + wC * ${u}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${p}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${m===1}) { dotProd += getX(batch, xF, xR, xC, ${p}) * getW(wF, wR, wC, ${p}, d2); } else if (${m===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${p}), getX(batch, xF, xR, xC, ${p} + 1) ); vec2 wValues = vec2( getW(wF, wR, wC, ${p}, d2), getW(wF, wR, wC, ${p} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${m===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${p}), getX(batch, xF, xR, xC, ${p} + 1), getX(batch, xF, xR, xC, ${p} + 2) ); vec3 wValues = vec3( getW(wF, wR, wC, ${p}, d2), getW(wF, wR, wC, ${p} + 1, d2), getW(wF, wR, wC, ${p} + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutput(dotProd); } `}},TW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,m=a*r,f=un(),A=h==="channelsLast",y=A?0:1,g=A?1:2,_="";for(let b=0;b<=1;b++)for(let w=0;w<=1;w++)_+=` blockIndex = rc.y + ${w}; pos = rc.x + ${b}; if(blockIndex < ${e[1]} && pos < ${e[0]}) { offsetY = int(blockIndex / (${l})) * ${i} - ${p}; d0 = offsetY + ${c} * (pos / ${m}); if(d0 < ${t[y]} && d0 >= 0) { offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.); d1 = offsetX + ${u} * (int(mod(float(pos), ${m}.) / ${a}.)); if(d1 < ${t[g]} && d1 >= 0) { ch = int(mod(float(pos), ${a}.)); if (${A}) { innerDims = vec2(d1, ch); result[${b*2+w}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${b*2+w}] = getChannel( getA(ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec2 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${_} ${f.output} = result; } `}};function F_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",m=!1,f=!1,A,y=[],g=(h===1||d===1)&&c>w_,_=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=ye({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=ye({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=ep({a:w,b:x,transposeA:m,transposeB:f,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=ye({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Xu(u.shape,w.shape),()=>`packed reshape ${u.shape} to ${w.shape} isn't free`);let N=ye({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=ep({a:w,b:N,backend:r,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,E.shape=n.outShape,A=$n({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function M_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,m=p==="channelsLast",f=l*u*c,A=d*h,y=[f,A],g=!0,_=!1,b=[],w=ye({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=ye({inputs:{x:t},backend:r,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w),b.push(x);let N=new TW(y,w.shape,n),T=r.runWebGLProgram(N,[w],"float32"),E=ye({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let M=a!=null,D=s!=null,L=o==="leakyrelu",W=o?Jd(o,!0):null,U=new f_(E.shape,x.shape,[1,A,n.outChannels],g,_,M,W,D,L),H=[E,x];if(a&&H.push(a),D&&H.push(s),L){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));H.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,H,"float32"),G=m?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=ye({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function EW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=F_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=M_({x:a,filter:s,convInfo:d,backend:n});else{let f=new R_(d);p=n.runWebGLProgram(f,[a,s],"float32")}let m=ye({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),m}var CW={kernelName:as,backendName:"webgl",kernelFunc:EW},RW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=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} - ${r}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${a}; 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); } `}},FW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=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,c=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${c}]; 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) / ${r}.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) / ${a}.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); } `}},MW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=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} - ${a}; 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 * ${r} - ${i}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},$W=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-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) / ${a}.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 < ${r}; 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 = ${r} - 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 OW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new RW(d);return n.runWebGLProgram(p,[a,s],"float32")}var DW={kernelName:mh,backendName:"webgl",kernelFunc:OW};function zW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new FW(d);return n.runWebGLProgram(p,[a,s],"float32")}var PW={kernelName:ss,backendName:"webgl",kernelFunc:zW};function LW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new SW(u);return n.runWebGLProgram(c,[a,s],"float32")}var WW={kernelName:ru,backendName:"webgl",kernelFunc:LW};function BW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=C.computeConv3DInfo(a.shape,l,i,1,o),c=new MW(u);return n.runWebGLProgram(c,[a,s],"float32")}var VW={kernelName:Ah,backendName:"webgl",kernelFunc:BW};function UW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=C.computeConv3DInfo(l,s.shape,o,1,i),c=new $W(u);return n.runWebGLProgram(c,[a,s],"float32")}var HW={kernelName:yh,backendName:"webgl",kernelFunc:UW},jW=p_+` return cos(x); `,GW=qe({opSnippet:jW}),qW={kernelName:is,backendName:"webgl",kernelFunc:GW},XW=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,KW=qe({opSnippet:XW}),ZW={kernelName:Zi,backendName:"webgl",kernelFunc:KW},YW=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,m]=[`${i-1}.0`,`${o-1}.0`],[f,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,_,b]=h>1?[`${(o-1)/(h-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(${g}); 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 = ${A}; float width_scale = ${_}; float in_y = ${y}; if( in_y < 0.0 || in_y > ${p} ) { setOutput(float(${a})); return; } float in_x = ${b}; if( in_x < 0.0 || in_x > ${m} ) { setOutput(float(${a})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${d} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}},JW=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new YW(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},QW={kernelName:Yi,backendName:"webgl",kernelFunc:JW},D_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${$_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=` uniform float index; void main() { ${ot(r)} coords = getOutputCoords(); int end = ${O_(r,"coords")}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${o}; ${O_(r,"coords")} = idx; val += getX(${$_(r,"coords")}); } setOutput(val); } `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function $_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function O_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function eB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=C.getAxesPermutation([s],l),c=a;u!=null&&(c=_n({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=$n({inputs:{x:c},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new D_(c.shape,!1,o),A=f.getCustomSetupFunc(m),y=p;p=n.runWebGLProgram(f,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let m=new D_(c.shape,i,o),f=p;p=n.runWebGLProgram(m,[p],p.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=C.getUndoAxesPermutation(u),f=_n({inputs:{x:p},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),f}return p}var tB={kernelName:os,backendName:"webgl",kernelFunc:eB};function nB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=Jw(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=Rz(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var rB={kernelName:gh,backendName:"webgl",kernelFunc:nB},aB=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 sB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),m=i==="NHWC"?[o,h,d,p]:[o,p,h,d],f=new aB(m,s,i);return n.runWebGLProgram(f,[a],a.dtype)}var iB={kernelName:Ji,backendName:"webgl",kernelFunc:sB},z_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?A=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:A=` float activation(float x) { ${n} } `,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${A} const ivec2 strides = ivec2(${u}, ${c}); const ivec2 pads = ivec2(${o}, ${l}); void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${f}; int q = d2 - d1 * ${f}; 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 < ${p}; wR++) { int xR = xRCorner + wR * ${h}; if (xR < 0 || xR >= ${s}) { continue; } for (int wC = 0; wC < ${m}; wC++) { int xC = xCCorner + wC * ${d}; if (xC < 0 || xC >= ${i}) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${g} ${y} setOutput(result); } `}},P_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,f=m,A="int xR; int xC; int xCOffset;";for(let b=0;b= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if(xCOffset + 1 >= ${i}) { xTexelR${b}C${x}.zw = vec2(0.); } } else { xTexelR${b}C${x} = vec4(0.); } xCOffset = xC + 1 - 2; if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) { vec4 previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if(xCOffset + 1 >= ${i}) { previous.zw = vec2(0.); } xR${b}C${x} = vec4(previous.zw, xTexelR${b}C${x}.xy); } else { xR${b}C${x} = vec4(0, 0, xTexelR${b}C${x}.xy); } `:A+=` if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) { xTexelR${b}C${x} = getX(batch, xR, xC, d1); } else { xTexelR${b}C${x} = vec4(0.); } xR${b}C${x} = xTexelR${b}C${x}; `,x+1= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1); } `,d>1&&(A+=` xCOffset -= 2; if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1); } else { xTexelR${b}C${x} = vec4(0.); } `),A+=` xR${b}C${x+1} = vec4( xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.xy); `):A+=` xCOffset = xC + ${N}; if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1); } xR${b}C${x+1} = xTexelR${b}C${x+2}; `}}else x= 0 && xR < ${s}) { `,l%2==1?(A+=` xCOffset = xC + 1 - ${c}; if(xCOffset >= 0 && xCOffset < ${i}) { xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1); } else { xTexelR${b}C${x} = vec4(0.); } if(xC + 1 >= 0 && xC + 1 < ${i}) { xTexelR${b}C${x+2} = getX(batch, xR, xC + 1, d1); } else { xTexelR${b}C${x+2} = vec4(0.); } xR${b}C${x} = vec4( xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw); `,x+1= 0 && xCOffset < ${i}) { final = getX(batch, xR, xCOffset, d1); } xR${b}C${x+1} = vec4(xTexelR${b}C${x+2}.xy, final.xy); `)):(A+=` if(xC >= 0 && xC < ${i}) { xTexelR${b}C${x} = getX(batch, xR, xC, d1); } else { xTexelR${b}C${x} = vec4(0.); } xCOffset = xC + ${c}; if(xCOffset >= 0 && xCOffset < ${i}) { xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1); } else { xTexelR${b}C${x+2} = vec4(0.); } xR${b}C${x} = vec4( xTexelR${b}C${x}.xy, xTexelR${b}C${x+2}.xy); `,x+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new P_(h):d=new z_(h),n.runWebGLProgram(d,[a,s],"float32")}var lB={kernelName:ls,backendName:"webgl",kernelFunc:oB},uB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=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} - ${r}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${a}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},cB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${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) / ${r}.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) / ${a}.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 * ${o} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function hB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=C.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new uB(h);return n.runWebGLProgram(d,[a,s],"float32")}var dB={kernelName:xh,backendName:"webgl",kernelFunc:hB};function pB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=C.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new cB(h);return n.runWebGLProgram(d,[a,s],"float32")}var fB={kernelName:wh,backendName:"webgl",kernelFunc:pB},mB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function AB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=ye({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new mB(s),l=n.runWebGLProgram(o,[i],i.dtype),u=ye({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var yB={kernelName:_h,backendName:"webgl",kernelFunc:AB},gB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=` const ivec2 strides = ivec2(${a}, ${s}); const ivec2 pads = ivec2(${c}, ${h}); 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 xB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new gB(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=ye({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var wB={kernelName:au,backendName:"webgl",kernelFunc:xB},_B="return (x >= 0.0) ? x : (exp(x) - 1.0);",bB=` 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; `,vB=qe({opSnippet:_B,packedOpSnippet:bB}),kB={kernelName:Qi,backendName:"webgl",kernelFunc:vB},IB="return (b >= 1.0) ? a : a * (b + 1.0);",NB=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); 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} `}},jB={kernelName:ao,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new HB(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},H_="return floor(x);",GB=qe({opSnippet:H_,packedOpSnippet:H_,cpuKernelImpl:Dz}),qB={kernelName:hs,backendName:"webgl",kernelFunc:GB},XB=` 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; } `,KB=` 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); `,ZB=tn({opSnippet:XB,packedOpSnippet:KB,dtype:"int32"}),YB={kernelName:ds,backendName:"webgl",kernelFunc:ZB},JB=class{constructor(e){this.variableNames=["A"];let t=un(),[n,r]=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(${r}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}},QB=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=un(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.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; } `}},tV={kernelName:Lh,backendName:"webgl",kernelFunc:eV},Il;function eV(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[u,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[c,u],d=[c,u,s];(o||i||l)&&(Il==null&&(Il=document.createElement("canvas").getContext("2d")),Il.canvas.width=u,Il.canvas.height=c,Il.drawImage(a,0,0,u,c),a=Il.canvas);let p=n.makeTensorInfo(h,"int32");n.texData.get(p.dataId).usage=Xn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);let m=J().getBool("WEBGL_PACK")?new QB(d):new JB(d),f=n.runWebGLProgram(m,[p],"int32");return n.disposeData(p.dataId),f}function nV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=C.convertConv2DDataFormat(c),A=C.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,f),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=F_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=M_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else{let b=i!=null,w=o!=null,x=p==="leakyrelu",N=p?Jd(p,!1):null,T=new R_(A,b,N,w,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let M=n.makeTensorInfo([],"float32",v.createScalarValue(m,"float32"));E.push(M),g.push(M)}y=n.runWebGLProgram(T,E,"float32")}let _=ye({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),_}var rV={kernelName:js,backendName:"webgl",kernelFunc:nV};function aV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,m=[],f=c;f==null&&(f=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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NAN : result.r; result.g = isNaN.g == 1.0 ? NAN : result.g; result.b = isNaN.b == 1.0 ? NAN : result.b; result.a = isNaN.a == 1.0 ? NAN : result.a; return result; `,HV=qe({opSnippet:VV,packedOpSnippet:UV,cpuKernelImpl:Bz}),jV={kernelName:ys,backendName:"webgl",kernelFunc:HV},GV="return log(1.0 + x);",qV=qe({opSnippet:GV}),XV={kernelName:fo,backendName:"webgl",kernelFunc:qV},KV="return float(a >= 1.0 && b >= 1.0);",ZV=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,YV=tn({opSnippet:KV,packedOpSnippet:ZV,dtype:"bool"}),JV={kernelName:mo,backendName:"webgl",kernelFunc:YV},QV="return float(!(x >= 1.0));",eU=qe({opSnippet:QV}),tU={kernelName:iu,backendName:"webgl",kernelFunc:eU},nU="return float(a >= 1.0 || b >= 1.0);",rU=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,aU=tn({opSnippet:nU,packedOpSnippet:rU,dtype:"bool"}),sU={kernelName:ou,backendName:"webgl",kernelFunc:aU},iU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; for (int j = -${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 * ${o}; setOutput(val); } `}},oU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,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); } `}},lU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new oU(a.shape,s,i,o,l):new iU(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},uU={kernelName:lu,backendName:"webgl",kernelFunc:lU},cU=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,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 = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${r}) * norm + float(${n}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${r}) * float(${a}) * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${a}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}},hU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new cU(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},dU={kernelName:Eh,backendName:"webgl",kernelFunc:hU};function pU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=ye({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=pi(i,e.dtype,"max",r),l=ye({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function j_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,_=new Array(o);for(let x=0;x<_.length;x++)_[x]=a.shape[c[x]];let b=Tm(g,a.shape,a.dtype,c,_);p=n.makeTensorInfo(_,a.dtype);let w=n.texData.get(p.dataId);w.values=b}else p=Qd(a,c,n);u=C.getInnerMostAxes(u.length,o)}C.assertAxesAreInnerMostDims("max",u,o);let[m,f]=C.computeOutAndReduceShapes(p.shape,u),A=m;i&&(A=C.expandShapeToKeepDim(m,l));let y;if(d){let g=n.texData.get(p.dataId).values,_=Vz(g,v.sizeFromShape(f),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=_}else y=pU(p,f,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var fU={kernelName:gs,backendName:"webgl",kernelFunc:j_},mU=l_+` return max(a, b); `,AU=` vec4 result = vec4(max(a, b)); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+Yd+` return result; `,yU=tn({opSnippet:mU,packedOpSnippet:AU,cpuKernelImpl:Uz}),gU={kernelName:xs,backendName:"webgl",kernelFunc:yU};function xU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ml(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=C.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return $n({inputs:{x:a},backend:n});let h=new ec(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var wU={kernelName:ws,backendName:"webgl",kernelFunc:xU};function _U(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new Rm(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var bU={kernelName:uu,backendName:"webgl",kernelFunc:_U},vU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=` const ivec2 pads = ivec2(${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 < ${a}; wR += ${r}) { 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 * ${s} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},kU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${c}, ${h}, ${d}); 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 += ${a}) { 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) / ${r}.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 = ${p} - 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 IU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,u,c),p=new Rm(d,"max",!0),m=n.runWebGLProgram(p,[i],i.dtype),f=new kU(d),A=n.runWebGLProgram(f,[a,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var NU={kernelName:Rh,backendName:"webgl",kernelFunc:IU};function SU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;ml([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,u,1,c,h),p=!0,m=new ec(d,"max",p),f=n.runWebGLProgram(m,[o],o.dtype),A=new vU(d),y=n.runWebGLProgram(A,[a,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var TU={kernelName:Ch,backendName:"webgl",kernelFunc:SU};function EU(e,t,n,r){let a=new ec(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new ec(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var CU={kernelName:Fh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=C.computePool2DInfo(r.shape,a,s,u,i),[h,d]=EU(r,o,c,l);return[h,d]}};function RU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=ye({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=pi(i,"float32","mean",r),l=ye({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var FU={kernelName:_s,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,c=C.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],m=r;if(h){if(d){let _=i.texData.get(m.dataId).values,b=new Array(o);for(let N=0;Nu[0]+e[c]+u[1]);let r=e.length,a=ot(r),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===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=` ${a} start = ${a}(${s}); ${a} end = ${a}(${i}); void main() { ${a} outC = getOutputCoords(); for (int i = 0; i < ${r}; 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}; } } ${a} coords = outC - start; setOutput(getX(${o})); } `}},WU=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,m)=>p[0]+e[m]+p[1]);let r=e.length,a=ot(r),s=t.map(p=>p[0]).join(","),i=t.map((p,m)=>p[0]+e[m]).join(","),o=cn("rc",r),l=cn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=` ${a} source = rc; if (source < start) { source = start * 2 - source - ${h}; } else if (source >= end) { source = (end - 1) * 2 - source + ${h}; } source -= start; `;d=` ${a} rc = outputLoc; ${p} result[0] = getChannel(getX(${l.join()}), ${c}); ${o[r-1]} += 1; if(${u}) { ${p} result[1] = getChannel(getX(${l.join()}), ${c}); } `}else{let p=` ${a} source = rc; ${a} lt = ${a}(lessThan(source, start)); ${a} gte = ${a}(greaterThanEqual(source, end)); ${a} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${h}) + gte * ((end - 1) * 2 - source + ${h}); source -= start; `;d=` ${a} rc = outputLoc; ${p} result[0] = getChannel(getX(${l.join()}), ${c}); ${o[r-1]} += 1; if(${u}) { ${p} result[1] = getChannel(getX(${l.join()}), ${c}); } rc = outputLoc; ${o[r-2]} += 1; if(${o[r-2]} < ${this.outputShape[r-2]}) { ${p} result[2] = getChannel(getX(${l.join()}), ${c}); ${o[r-1]} += 1; if(${u}) { ${p} result[3] = getChannel(getX(${l.join()}), ${c}); } } `}this.userCode=` const ${a} start = ${a}(${s}); const ${a} end = ${a}(${i}); void main() { ${a} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${d} setOutput(result); } `}},BU=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new WU(r.shape,a,s):new LU(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},VU={kernelName:cu,backendName:"webgl",kernelFunc:BU},UU=`if (b == 0.0) return NAN; return mod(a, b);`,HU=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+Yd+` return result; `,jU=tn({opSnippet:UU,packedOpSnippet:HU}),GU={kernelName:Ao,backendName:"webgl",kernelFunc:jU},qU=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=` uniform float seed; void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; float r = random(seed); float cdf = 0.0; for (int i = 0; i < ${t-1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { setOutput(float(i)); return; } } // If no other event happened, last event happened. setOutput(float(${t-1})); } `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},XU=` if (a == b) { return 1.0; }; return a / b;`,KU=` // 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; `,G_=tn({opSnippet:XU,packedOpSnippet:KU,checkOutOfBounds:!0}),ZU={kernelName:us,backendName:"webgl",kernelFunc:G_},q_="return a - b;",X_=tn({opSnippet:q_,packedOpSnippet:q_,supportsComplex:!0,cpuKernelImpl:Jz}),YU={kernelName:Bs,backendName:"webgl",kernelFunc:X_};function K_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=j_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=ye({inputs:{x:o},backend:n,attrs:{shape:l}}),c=X_({inputs:{a,b:u},backend:n}),h=W_({inputs:{x:c},backend:n}),d=Cm({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=ye({inputs:{x:d},backend:n,attrs:{shape:l}}),m=G_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}var JU={kernelName:Ls,backendName:"webgl",kernelFunc:K_};function QU(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:K_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new qU(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var eH={kernelName:Mh,backendName:"webgl",kernelFunc:QU},Z_="return -x;";function tH(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=Gz(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new _l(r.shape,Z_):a=new Ma(r.shape,Z_),n.runWebGLProgram(a,[r],r.dtype)}var nH={kernelName:yo,backendName:"webgl",kernelFunc:tH},rH=Or.nonMaxSuppressionV3Impl;function aH(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=rH(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var sH={kernelName:xo,backendName:"webgl",kernelFunc:aH},iH=Or.nonMaxSuppressionV4Impl;function oH(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=iH(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var lH={kernelName:wo,backendName:"webgl",kernelFunc:oH},uH=Or.nonMaxSuppressionV5Impl;function cH(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,m=l,f=u,{selectedIndices:A,selectedScores:y}=uH(c,h,d,p,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var hH={kernelName:_o,backendName:"webgl",kernelFunc:cH},dH=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${r}), float(${n}), float(index == coords.y))); } `}},pH=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new dH(l,s,i,o),c=ye({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=ye({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},fH={kernelName:Is,backendName:"webgl",kernelFunc:pH};function ap(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=nc({inputs:{input:r},backend:n}),s=ap({inputs:{x:a},backend:n}),i=rp({inputs:{input:r},backend:n}),o=ap({inputs:{x:i},backend:n}),l=$a({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Om({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var mH={kernelName:Lo,backendName:"webgl",kernelFunc:ap};function Y_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=nc({inputs:{input:r},backend:n}),s=Y_({inputs:{x:a},backend:n}),i=rp({inputs:{input:r},backend:n}),o=ap({inputs:{x:i},backend:n}),l=$a({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Om({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var AH={kernelName:bo,backendName:"webgl",kernelFunc:Y_};function yH(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return $m({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=$m({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=C_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var gH={kernelName:vo,backendName:"webgl",kernelFunc:yH},xH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=ot(r),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,r);if(r===1){this.userCode=` int start = ${s}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(float(${n})); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${a} start = ${a}(${s}); ${a} end = ${a}(${i}); void main() { ${a} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(float(${n})); } else { ${a} coords = outC - start; setOutput(getX(${o})); } } `}},wH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let r=e.length,a=ot(r),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=cn("rc",r),l=cn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1; if(${u}) { `,r===1?"":`} rc = outputLoc; ${o[r-2]} += 1; if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${o[r-1]} += 1; if(${u}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let m=0,f=r===1?2:4;m{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wH(a.shape,s,i):new xH(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},_H={kernelName:Ns,backendName:"webgl",kernelFunc:J_},bH=` 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); `,vH=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); vec4 result = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS bvec4 isExpZero = equal(b, vec4(0.0)); result.r = isExpZero.r ? 1.0 : result.r; result.g = isExpZero.g ? 1.0 : result.g; result.b = isExpZero.b ? 1.0 : result.b; result.a = isExpZero.a ? 1.0 : result.a; vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b)); `+Yd+` return result; `,kH=tn({opSnippet:bH,packedOpSnippet:vH}),IH={kernelName:Ss,backendName:"webgl",kernelFunc:kH};function NH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=C.getAxesPermutation(c,o),d=a;h!=null&&(d=_n({inputs:{x:a},backend:n,attrs:{perm:h}}),c=C.getInnerMostAxes(c.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:A,outDtype:y}=qz(d.shape,d.dtype,m,c);p=n.makeTensorInfo(A,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(f),y=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=Hh(a.dtype),_=pi(y,g,"prod",n);p=ye({inputs:{x:_},backend:n,attrs:{shape:m}}),l.push(y),l.push(_)}if(i){l.push(p);let m=C.expandShapeToKeepDim(p.shape,u);p=ye({inputs:{x:p},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),p}var SH={kernelName:ko,backendName:"webgl",kernelFunc:NH},Q_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Xz(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},TH={kernelName:hu,backendName:"webgl",kernelFunc:Q_},EH="return 1.0 / x;",CH=qe({opSnippet:EH}),RH={kernelName:Io,backendName:"webgl",kernelFunc:CH},FH=Ar+` return (x < 0.0) ? 0.0 : x; `,MH=` 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; `,$H=qe({opSnippet:FH,packedOpSnippet:MH}),OH={kernelName:Es,backendName:"webgl",kernelFunc:$H},DH=Ar+` return (x < 0.0) ? 0.0 : min(6.0, x); `,zH=` 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; `,PH=qe({opSnippet:DH,packedOpSnippet:zH}),LH={kernelName:Rs,backendName:"webgl",kernelFunc:PH},WH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); const vec2 inputShapeRC = vec2(${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 = ${h}; // 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); } `}},BH=class{constructor(e,t,n,r,a){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=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/c[0]}, ${u[1]/c[1]}, ${u[1]/c[1]}); const vec3 inputShapeRC = vec3(${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 = ${h}; // 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 VH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new BH(a.shape,l,u,s,i):new WH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var UH={kernelName:Cs,backendName:"webgl",kernelFunc:VH},HH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,m=Math.ceil(d)*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(${c}); const float invHeightScale = float(${h}); const float invWidthScale = float(${d}); const int winHeight = int(${p}); 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), ${r-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), ${a-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 jH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new HH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var GH={kernelName:Dh,backendName:"webgl",kernelFunc:jH},qH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); const vec2 inputShapeRC = vec2(${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 coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}};function XH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new qH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var KH={kernelName:du,backendName:"webgl",kernelFunc:XH},ZH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${u}); const float widthScale = float(${c}); const float invHeightScale = float(${h}); const float invWidthScale = float(${d}); const int winHeight = int(${p}); 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(${l[1]})); int sourceNearestRow = int(min( float(int(${r}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${a}) - 1), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function YH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new ZH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var JH={kernelName:Oh,backendName:"webgl",kernelFunc:YH},QH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } `;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=ot(n);this.userCode=` void main() { ${s} coords = getOutputCoords(); setOutput(getX(${a})); } `}},ej=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-${n} tensor is not yet supported`);this.outputShape=e;let r=cn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${e[0]} - rc - 1), ${e[0]} - rc - 1); if(${a}){ 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 = ${o(r.slice())}; if(${a}){ result.g = ${l(r.slice())}; } if(${s}) { result.b = 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a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,u]=C.getImageCenter(r,a,s),c=l.toFixed(3),h=u.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=` vec3 fill = vec3(${n.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int y = coords[1]; float coordXFloat = (float(x) - ${c}) * ${o} - (float(y) - ${h}) * ${i}; float coordYFloat = (float(x) - ${c}) * ${i} + (float(y) - ${h}) * ${o}; int coordX = int(round(coordXFloat + ${c})); int coordY = int(round(coordYFloat + ${h})); ${d} if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${a}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}},aj={kernelName:Wo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new rj(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},sj=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,ij=qe({opSnippet:sj}),oj={kernelName:Ms,backendName:"webgl",kernelFunc:ij},lj="return inversesqrt(x);",uj=qe({opSnippet:lj,cpuKernelImpl:Kz}),cj={kernelName:$s,backendName:"webgl",kernelFunc:uj},eb=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ot(a.length),l=ot(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=` ${o} strides = ${o}(${a}); void main() { ${l} coords = getOutputCoords(); float sum = 0.0; bool found = false; for (int i = 0; i < ${e}; i++) { int flattenedIndex = 0; for (int j = 0; j < ${t}; j++) { int index = round(${c}); flattenedIndex += index * ${p}; } if (flattenedIndex == coords[0]) { sum += ${d}; found = true; } } setOutput(mix(getDefaultValue(), sum, float(found))); } `}};function hj(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=C.calculateShapes(s,a,i),d=[h/u,u];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=ye({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),m=ye({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new eb(l,o,p.shape.length,m.shape.length,c,d),y=n.runWebGLProgram(A,[m,p,f],m.dtype),g=ye({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),g}var dj={kernelName:So,backendName:"webgl",kernelFunc:hj},pj=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 1.0) { setOutput(getA(${a})); } else { setOutput(getB(${a})); } } `}};function fj(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new pj(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],tr(a.dtype,s.dtype))}var mj={kernelName:To,backendName:"webgl",kernelFunc:fj},Aj=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${C.SELU_SCALEALPHA}; float scale = ${C.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,yj=qe({opSnippet:Aj}),gj={kernelName:Eo,backendName:"webgl",kernelFunc:yj},xj="return 1.0 / (1.0 + exp(-1.0 * x));",wj=qe({opSnippet:xj}),_j={kernelName:Ds,backendName:"webgl",kernelFunc:wj},bj=` if (isnan(x)) { return 0.0; } return sign(x); `,vj=qe({opSnippet:bj}),kj={kernelName:Fo,backendName:"webgl",kernelFunc:vj},Ij=p_+` return sin(x); `,Nj=qe({opSnippet:Ij}),Sj={kernelName:Os,backendName:"webgl",kernelFunc:Nj},Tj=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,Ej=qe({opSnippet:Tj}),Cj={kernelName:Ro,backendName:"webgl",kernelFunc:Ej},Rj=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; bool too_large = x > -threshold; bool too_small = x < threshold; float result; float exp_x = exp(x); if (too_large){ result = x; } else if (too_small){ result = exp_x; } else{ result = log(exp_x + 1.0); } return result; `,Fj=qe({opSnippet:Rj}),Mj={kernelName:Mo,backendName:"webgl",kernelFunc:Fj},$j=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;yn.disposeIntermediateTensorInfo(y)),A},Oj={kernelName:pu,backendName:"webgl",kernelFunc:$j};function Dj(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:h}=C.calculateShapes(s,a,o),d=!1,p=new eb(u,l,a.shape.length,s.shape.length,c,[h,1],d),m=n.runWebGLProgram(p,[s,a,i],s.dtype),f=ye({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var zj={kernelName:zh,backendName:"webgl",kernelFunc:Dj};function Pj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),u=a.shape.length,c=new Array(u).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let m=tc({inputs:{x:a},backend:n,attrs:{begin:c,size:p}});return c[o]+=d,m})}var Lj={kernelName:$o,backendName:"webgl",kernelFunc:Pj},Wj="return sqrt(x);",Bj=qe({opSnippet:Wj}),Vj={kernelName:zs,backendName:"webgl",kernelFunc:Bj},Uj="return x * x;",Hj=qe({opSnippet:Uj}),jj={kernelName:fu,backendName:"webgl",kernelFunc:Hj},tb="return (a - b) * (a - b);",Gj=tn({opSnippet:tb,packedOpSnippet:tb}),qj={kernelName:Ws,backendName:"webgl",kernelFunc:Gj};function Xj({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=Ar+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new Ma(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var Kj={kernelName:_a,backendName:"webgl",kernelFunc:Xj},Zj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ot(n.length),s=ot(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${a} begin = ${a}(${e}); ${a} strides = ${a}(${t}); void main() { ${s} coords = getOutputCoords(); setOutput(getX(${i})); } `}};function Yj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:m,$strides:f,size:A,newShape:y,outShape:g}=on.sliceInfo(a.shape,s,i,o,l,u,c,h,d),_=ye({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=tc({inputs:{x:_},backend:n,attrs:{begin:m,size:A}});b=ye({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([_])){let x=n.texData.get(_.dataId).values,N=Le(_.shape,_.dtype,x),T=Yz(g,N,f,m);b=n.makeTensorInfo(g,_.dtype,T.values)}else{let x=new Zj(m,f,g);b=n.runWebGLProgram(x,[_],_.dtype)}let w=ye({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(_),n.disposeIntermediateTensorInfo(b),w}var Jj={kernelName:Oo,backendName:"webgl",kernelFunc:Yj},Qj="return tan(x);",eG=qe({opSnippet:Qj}),tG={kernelName:Do,backendName:"webgl",kernelFunc:eG},nG=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,rG=qe({opSnippet:nG}),aG={kernelName:Vs,backendName:"webgl",kernelFunc:rG},iG=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)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"],r=[];for(let a=0;av.decodeString(c)),l=Le(a.shape,a.dtype,o),u=Qz(l,s);return n.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new iG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var oG={kernelName:wa,backendName:"webgl",kernelFunc:nb};function lG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,u]=eP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var uG={kernelName:zo,backendName:"webgl",kernelFunc:lG};function cG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;ml(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=tP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([u.length],"int32",u)]}var hG={kernelName:Ph,backendName:"webgl",kernelFunc:cG};function dG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),c=0;for(let f=0;fn.disposeIntermediateTensorInfo(f)),m}var pG={kernelName:Po,backendName:"webgl",kernelFunc:dG},fG=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,h=` sumValue += dot(values, segFilter); `,d="";a%n>0&&(d=` if (inIdx < 0 || inIdx >= ${a}) { return initializationValue; } `);let p="";a%n>0&&(p=` if (inIdx < 0 || inIdx >= ${a}) { return -1.0; } `),this.userCode=` const float initializationValue = ${o}; float getValue(int batch, int inIdx) { ${d} return getX(batch, inIdx); } float getSegmentIdAtIndex(int inIdx) { ${p} 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 ); ${h} } int inIdx = inOffset + ${u}; if (${c===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 ); ${h} } else if (${c===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 ); ${h} } else if (${c===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 ); ${h} } setOutput(${l}); } `}};function mG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],u=0,c=C.getAxesPermutation([u],o),h=a;c!=null&&(h=_n({inputs:{x:a},backend:n,attrs:{perm:c}}),l.push(h),u=C.getInnerMostAxes(1,o)[0]);let d=C.segment_util.computeOutShape(h.shape,u,i),p=v.sizeFromShape([h.shape[u]]),m=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(m);let f=Hh(a.dtype),A=(b,w,x,N,T)=>{let E=b.shape[0],M=b.shape[1],D=C.segment_util.segOpComputeOptimalWindowSize(M,T),L={windowSize:D,inSize:M,batchSize:E,numSegments:T},W=new fG(L,w),U=n.compileAndRun(W,[b,x],N);if(l.push(U),U.shape[1]===T)return U;let H=Q_({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),X=nb({inputs:{x:H},backend:n,attrs:{reps:[M/D]}});return l.push(H),l.push(X),A(U,w,X,N,T)},y=A(m,"unsortedSegmentSum",s,f,i),g=ye({inputs:{x:y},backend:n,attrs:{shape:d}}),_=g;if(c!=null){l.push(g);let b=C.getUndoAxesPermutation(c);_=_n({inputs:{x:_},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),_}var AG={kernelName:mu,backendName:"webgl",kernelFunc:mG},yG=[uU,dU,YP,QP,nL,sL,oL,cL,dL,fL,gL,wL,vL,NL,ML,EL,DL,WL,PL,HL,GL,XL,JL,sW,oW,pW,mW,xW,bW,FP,NW,DW,PW,CW,VW,HW,WW,qW,ZW,QW,tB,rB,iB,dB,fB,lB,yB,wB,kB,TB,FB,OB,DB,zB,LB,BB,UB,jB,qB,YB,tV,rV,sV,lV,dV,AV,wV,RP,bV,IW,IV,TV,RV,$P,OV,LV,BV,XV,jV,JV,tU,sU,fU,bU,wU,NU,TU,CU,gU,FU,$U,PU,VU,GU,eH,LP,nH,sH,lH,hH,uW,fH,AH,gH,_H,IH,DP,SH,TH,cW,ZU,RH,LH,OH,BP,UH,GH,KH,JH,nj,aj,oj,cj,dj,mj,gj,_j,kj,Sj,Cj,rW,JU,Mj,Oj,zj,Lj,Vj,jj,qj,Kj,Jj,YU,XP,tG,aG,oG,uG,KP,hG,pG,AG,mH];for(let e of yG)qs(e);var On;(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"})(On||(On={}));var rc;(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"})(rc||(rc={}));var rb;function gG(e){rb=e.wasm.cwrap(Hs,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function xG(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r,d=n.dataIdMap.get(a.dataId).id,p=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let T=n.dataIdMap.get(i.dataId);if(T.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${T.shape.length}.`);m=T.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,A=rc[c];if(A==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],g=u?s.shape[1]:s.shape[2],_=a.shape[0],b=n.makeOutput([_,y,g],a.dtype),w=n.dataIdMap.get(b.dataId).id,x=new Uint8Array(new Int32Array(a.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return rb(d,x,a.shape.length,p,N,s.shape.length,l,u,A,m,f,h||0,w),b}var wG={kernelName:Hs,backendName:"wasm",setupFunc:gG,kernelFunc:xG};function bn(e){let t;function n(a){t=a.wasm.cwrap(e,null,["number","number"])}function r(a){let{backend:s,inputs:{x:i}}=a,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return v.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var _G=bn(Bi);function hn(e,t,n){let r;function a(i){r=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:c}=l,h=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(c.dataId).id,p=n!=null?n:u.dtype,m=C.assertAndGetBroadcastShape(u.shape,c.shape),f=o.makeOutput(m,p);if(v.sizeFromShape(m)===0)return f;let A=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),g=o.dataIdMap.get(f.dataId).id,_=()=>r(h,A,u.shape.length,d,y,c.shape.length,On[u.dtype],g);if(t&&u.dtype==="float32")return _(),f;let b=C.getBroadcastDims(u.shape,m),w=C.getBroadcastDims(c.shape,m),x=b.every((T,E)=>T===E),N=w.every((T,E)=>T===E);if(x&&N)return _(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var bG=!0,vG=hn(ga,bG),ab;function kG(e){ab=e.wasm.cwrap(Ja,null,["array","number","number","number"])}function IG(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(r.shape)===0)return r;let a=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=n.dataIdMap.get(r.dataId).id;return ab(s,a.length,On[r.dtype],i),r}var NG={kernelName:Ja,backendName:"wasm",setupFunc:kG,kernelFunc:IG};function sp(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),a=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(a),r}var SG={kernelName:ms,backendName:"wasm",kernelFunc:sp},sb;function TG(e){sb=e.wasm.cwrap(Us,null,["number","array","number","number","number","array","number"])}function ip(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=CG(t.x.shape,r.perm),i=!0;for(let m=0;m=a&&(s===-1||r[s]>r[i])&&(s=i);r[s]=a}return[n,r]}var RG={kernelName:Us,backendName:"wasm",kernelFunc:ip,setupFunc:TG};function Nl(e,t,n){let r=e.shape,a=e.shape.length,s=v.parseAxisParam(t,r),i=s,o=C.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let c=new Array(a);for(let d=0;d`new shape: ${i}, old shape: ${r.shape}. 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Please use 'channelsLast'.`);let D=r.makeOutput(p.outShape,"float32"),L=r.dataIdMap.get(D.dataId).id;return Ab(i,a.shape[0],a.shape[1],a.shape[2],o,m,f,A,y,g,_,M,b,w,x,N,T,E,L),D}var dq={kernelName:ls,backendName:"wasm",setupFunc:cq,kernelFunc:hq},pq=!1,fq=hn(to,pq,"bool"),mq=bn(cs);function zm(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),yr({inputs:{x:a},backend:r,attrs:{shape:o}})}var Aq={kernelName:no,backendName:"wasm",kernelFunc:zm};function yq(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var gq={kernelName:su,backendName:"wasm",kernelFunc:yq},yb;function xq(e){yb=e.wasm.cwrap(ao,null,["number","number","number","number","number","number"])}function wq(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,u,c]=r.shape;return yb(s,o,l,u,c,i),a}var _q={kernelName:ao,backendName:"wasm",kernelFunc:wq,setupFunc:xq},bq=bn(hs),vq=!1,kq=hn(ds,vq),gb;function Iq(e){gb=e.wasm.cwrap(ps,null,["number","number","number","number","number","number","number"])}function Nq(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let A=t.dataIdMap.get(f.dataId).id;return gb(c,h,d,p,m,a,A),f}var Sq={kernelName:ps,backendName:"wasm",setupFunc:Iq,kernelFunc:Nq},xb;function Tq(e){xb=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Eq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d),A=rc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=f.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=f.filterHeight,x=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,E=f.padInfo.bottom,M=f.padInfo.left,D=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(f.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return xb(y,G,ee,Y,g,w,x,b,N,T,E,M,X,D,L,W,U,H,_,A,ie,m||0,te),ae}var Cq={kernelName:js,backendName:"wasm",setupFunc:Tq,kernelFunc:Eq},wb;function Rq(e){wb=e.wasm.cwrap(Gs,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 Fq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,d,!0),A=rc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=f.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=f.filterHeight,x=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,E=f.padInfo.bottom,M=f.padInfo.left,D=f.dilationHeight,L=f.dilationWidth,W=f.strideHeight,U=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(f.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return wb(y,G,ee,Y,g,w,x,b,N,T,E,M,X,D,L,W,U,H,_,A,ie,m||0,te),ae}var Mq={kernelName:Gs,backendName:"wasm",setupFunc:Rq,kernelFunc:Fq},_b;function $q(e){_b=e.wasm.cwrap(io,null,["number","number","number","number","number","number","array","number"])}function Oq(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=tf.prepareAndValidate(r,a),u=t.makeOutput(s,r.dtype);if(i===0)return u;let c=a.shape,h=c[c.length-1],d=t.dataIdMap.get(r.dataId).id,p=t.dataIdMap.get(a.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return _b(d,On[r.dtype],p,i,h,o,m,f),u}var Dq={kernelName:io,backendName:"wasm",setupFunc:$q,kernelFunc:Oq},bb;function zq(e){bb=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Pq(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=yr({inputs:{x:a},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),d=yr({inputs:{x:s},attrs:{shape:[u.batchSize,h/u.batchSize]},backend:t}),p=[u.batchSize,u.outerSize,h/u.batchSize,u.sliceSize],m=t.makeOutput(p,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let f=c.shape.length-1,A=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(m.dataId).id,_=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(p)).buffer);return bb(A,On[a.dtype],_,f,y,u.batchSize,b,g),t.disposeData(c.dataId),t.disposeData(d.dataId),m.shape=u.outputShape,m}var Lq={kernelName:so,backendName:"wasm",setupFunc:zq,kernelFunc:Pq},Wq=!1,Bq=hn(oo,Wq,"bool"),Vq=!1,Uq=hn(fs,Vq,"bool"),vb;function Hq(e){vb=e.wasm.cwrap(As,null,["number","number","number"])}function jq(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,t.dtype);if(v.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;vb(a,n,i)}return s}var Gq={kernelName:As,backendName:"wasm",setupFunc:Hq,kernelFunc:jq},qq=!1,Xq=hn(ho,qq,"bool"),Kq=!1,Zq=hn(po,Kq,"bool"),Yq=bn(ys),Jq=!1,Qq=hn(mo,Jq,"bool"),kb;function eX(e){kb=e.wasm.cwrap(gs,null,["number, number, number"])}function tX(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:h,inputWasTransposed:d}=Nl(i,a,t);if(d){let g=t.dataIdMap.get(u.dataId).id;l=u,o=g}let p=l.shape.length;C.assertAxesAreInnerMostDims("max",c,p);let[m,f]=C.computeOutAndReduceShapes(l.shape,c),A=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;kb(o,A,g)}if(d&&t.disposeData(u.dataId),s){let g=C.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var nX={kernelName:gs,backendName:"wasm",setupFunc:eX,kernelFunc:tX},rX=!1,aX=hn(xs,rX),Ib;function sX(e){Ib=e.wasm.cwrap(ws,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function iX(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=C.computePool2DInfo(a.shape,i,o,1,l,u),h=c.filterHeight,d=c.filterWidth,p=c.padInfo.top,m=c.padInfo.right,f=c.padInfo.bottom,A=c.padInfo.left,y=c.dilationHeight,g=c.dilationWidth,_=c.strideHeight,b=c.strideWidth,w=c.inChannels,x=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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vX(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=r,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,d=Eb(c,h,s,a,i,o),{pSelectedIndices:p,selectedSize:m,pSelectedScores:f,pValidOutputs:A}=Pm(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",p),g=t.makeOutput([],"int32",A);return[y,g]}var kX={kernelName:wo,backendName:"wasm",setupFunc:bX,kernelFunc:vX},Cb;function IX(e){Cb=e.wasm.cwrap(_o,"number",["number","number","number","number","number","number"])}function NX(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=r,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,d=Cb(c,h,s,a,i,o),{pSelectedIndices:p,selectedSize:m,pSelectedScores:f,pValidOutputs:A}=Pm(t,d);t.wasm._free(A);let y=t.makeOutput([m],"int32",p),g=t.makeOutput([m],"float32",f);return[y,g]}var SX={kernelName:_o,backendName:"wasm",setupFunc:IX,kernelFunc:NX},TX=!1,EX=hn(go,TX,"bool"),Rb;function CX(e){Rb=e.wasm.cwrap(Is,null,["number","number","number","number","number"])}function RX(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=n.makeOutput([...a.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,c=n.dataIdMap.get(a.dataId).id;return Rb(c,s,i,o,u),l}var FX={kernelName:Is,backendName:"wasm",setupFunc:CX,kernelFunc:RX};function MX(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var $X={kernelName:bo,backendName:"wasm",kernelFunc:MX};function OX(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return zm({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let 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UX(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,o=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return Mb(s,i,l),o}var HX={kernelName:Ts,backendName:"wasm",setupFunc:VX,kernelFunc:UX},$b;function jX(e){$b=e.wasm.cwrap(ko,null,["number","number","number","number"])}function GX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=Nl(i,a,t),m=h;if(p){let _=t.dataIdMap.get(c.dataId).id;_!==o&&(u=c,l=_,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,A]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(A),g=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;$b(l,y,On[g.dtype],_)}if(p&&t.disposeData(c.dataId),s){let _=C.expandShapeToKeepDim(g.shape,d);g.shape=_}return g}var qX={kernelName:ko,backendName:"wasm",setupFunc:jX,kernelFunc:GX},XX=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=im(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},KX={kernelName:hu,backendName:"wasm",kernelFunc:XX},ZX=!0,YX=hn(us,ZX),JX=bn(Es),QX=bn(Rs),Ob;function eK(e){Ob=e.wasm.cwrap(Cs,null,["number","number","number","number","number","number","number","number","number","number"])}function tK(e){let{backend:t,inputs:n,attrs:r}=e,{images:a}=n,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,[c,h,d,p]=a.shape,m=[c,l,u,p],f=t.dataIdMap.get(a.dataId),A;f.dtype!=="float32"&&(A=op({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(A.dataId));let y=f.id,g=t.makeOutput(m,"float32");if(v.sizeFromShape(a.shape)===0)return g;let _=t.dataIdMap.get(g.dataId).id;return Ob(y,c,h,d,p,l,u,s?1:0,i?1:0,_),A!=null&&t.disposeData(A.dataId),g}var nK={kernelName:Cs,backendName:"wasm",setupFunc:eK,kernelFunc:tK},Db;function 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Add some layers first.");this.model=new sa({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new xr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new xr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new xr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new xr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Fl))throw new $e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=br(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Fl.className="Sequential";re.registerClass(Fl);function dte(e){return new sa(e)}function pte(e){return new Fl(e)}function fte(e,t){return t==null&&(t={}),cte(e,t)}function I3(e){return $3(e)}function mte(e,t){ur.registerCallbackConstructor(e,t)}var Dn=class extends re.Serializable{getConfig(){return{}}},l7=class extends Dn{apply(e,t=1){return qJ(e,t)}};l7.className="elu";re.registerClass(l7);var u7=class extends Dn{apply(e){return md(e)}};u7.className="selu";re.registerClass(u7);var c7=class extends Dn{apply(e){return $r(e)}};c7.className="relu";re.registerClass(c7);var h7=class extends Dn{apply(e){return B(()=>il(6,$r(e)))}};h7.className="relu6";re.registerClass(h7);var d7=class extends Dn{apply(e){return e}};d7.className="linear";re.registerClass(d7);var p7=class extends Dn{apply(e){return Sn(e)}};p7.className="sigmoid";re.registerClass(p7);var f7=class extends Dn{apply(e){return KJ(e)}};f7.className="hardSigmoid";re.registerClass(f7);var m7=class extends Dn{apply(e){return al(e)}};m7.className="softplus";re.registerClass(m7);var A7=class extends Dn{apply(e){return XJ(e)}};A7.className="softsign";re.registerClass(A7);var y7=class extends Dn{apply(e){return Qo(e)}};y7.className="tanh";re.registerClass(y7);var NA=class extends Dn{apply(e,t=-1){return Bu(e,t)}};NA.className="softmax";re.registerClass(NA);var g7=class extends Dn{apply(e,t=-1){return ld(e,t)}};g7.className="logSoftmax";re.registerClass(g7);var x7=class extends Dn{apply(e,t=1){return B(()=>Sn(e.mul(t)).mul(e))}};x7.className="swish";re.registerClass(x7);function La(e){return e.getClassName()}function SA(e,t={}){return oc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function Wa(e){if(e==null){let t={};return t.className="linear",t.config={},SA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},SA(t)}else return e instanceof Dn?e:SA(e)}function TA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var w7=class extends re.Serializable{},xc=class extends w7{constructor(e){super();TA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return B(()=>{let t=Et([1]);return this.hasL1&&(t=se(t,Se(P(this.l1,Ot(e))))),this.hasL2&&(t=se(t,Se(P(this.l2,dc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};xc.className="L1L2";re.registerClass(xc);function Ate(e){return TA(e),new xc({l1:e!=null?e.l1:null,l2:0})}function yte(e){return TA(e),new xc({l2:e!=null?e.l2:null,l1:0})}var _7={l1l2:"L1L2"};function ut(e){return Vm(e)}function b7(e,t={}){return oc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function gt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in _7?_7[e]:e,config:{}};return b7(t)}else return e instanceof w7?e:b7(e)}var EA=class extends je{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ze(e);let n=$r(e);return this.maxValue!=null&&(n=gn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};EA.className="ReLU";re.registerClass(EA);var CA=class extends je{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return $u(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};CA.className="LeakyReLU";re.registerClass(CA);var RA=class extends je{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=yt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=gt(e.alphaRegularizer),this.alphaConstraint=Lt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=lt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r(St(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function v7(e,t){return B(()=>(St(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function gte(e,t,n,r=1,a="valid",s,i=1){return B(()=>{if(s==null&&(s=gr()),St(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=nt(e,[0,2,1])),a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=ed(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Wr(o,n)),o})}function k7(e,t,n,r=[1,1],a="valid",s,i,o=null){return B(()=>{if(s==null&&(s=gr()),St(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=OA(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ra.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function xte(e,t,n,r=[1,1,1],a="valid",s,i){return B(()=>{if(s==null&&(s=gr()),St(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=v7(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=kf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Wr(o,n)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var DA=class extends je{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",DA.verifyArgs(t),this.rank=e,jt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ml(t.kernelSize,e,"kernelSize"),this.strides=Ml(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Kn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,St(this.dataFormat),this.activation=Wa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=yt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Lt(t.biasConstraint),this.biasRegularizer=gt(t.biasRegularizer),this.activityRegularizer=gt(t.activityRegularizer),this.dilationRate=Ml(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(Pr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Hm(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:La(this.activation),useBias:this.useBias,biasInitializer:vt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Pt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},wc=class extends DA{constructor(e,t){super(e,t);this.kernel=null,wc.verifyArgs(t),this.filters=t.filters,jt(this.filters,"filters"),this.kernelInitializer=yt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Lt(t.kernelConstraint),this.kernelRegularizer=gt(t.kernelRegularizer)}build(e){e=lt(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],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,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 B(()=>{e=ze(e);let n,r=this.bias==null?null:this.bias.read(),a=u3(this.activation.getClassName());if(a!=null&&this.rank===2)n=k7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=gte(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=k7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=xte(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=lt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a 0 but got ${JSON.stringify(e.filters)}`)}},_c=class extends wc{constructor(e){super(2,e);_c.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Hm(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)}.`)}};_c.className="Conv2D";re.registerClass(_c);var $p=class extends wc{constructor(e){super(3,e);$p.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 ${JSON.stringify(e.kernelSize)}.`)}};$p.className="Conv3D";re.registerClass($p);var zA=class extends _c{constructor(e){super(e);if(this.inputSpec=[new Gt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=lt(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Gt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{let n=ze(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Mp(o,h,u,this.padding),m=Mp(l,d,c,this.padding),f=[a,p,m,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,1]));let A=td(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=nt(A,[0,3,1,2])),this.bias!=null&&(A=Wr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=lt(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Mp(t[r],o,s,this.padding),t[a]=Mp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};zA.className="Conv2DTranspose";re.registerClass(zA);var I7=class extends wc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new 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=yt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=gt(t.depthwiseRegularizer),this.depthwiseConstraint=Lt(t.depthwiseConstraint),this.pointwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=gt(t.pointwiseRegularizer),this.pointwiseConstraint=Lt(t.pointwiseConstraint)}build(e){if(e=lt(e),e.length{e=ze(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),n=Vf(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Wr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=nt(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=vt(this.depthwiseInitializer),e.pointwiseInitializer=vt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Pt(this.depthwiseConstraint),e.pointwiseConstraint=Pt(this.pointwiseConstraint),e}};I7.className="SeparableConv";var PA=class extends I7{constructor(e){super(2,e)}};PA.className="SeparableConv2D";re.registerClass(PA);var Op=class extends wc{constructor(e){super(1,e);Op.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"&&!Hm(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)}.`)}};Op.className="Conv1D";re.registerClass(Op);var LA=class extends je{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 B(()=>{if(e=ze(e),this.dataFormat==="channelsLast"){let n=hp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return hp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=hp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return hp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};LA.className="Cropping2D";re.registerClass(LA);var WA=class extends je{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,St(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,WJ(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 B(()=>{let n=ze(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=nt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};WA.className="UpSampling2D";re.registerClass(WA);function wte(e,t,n=[1,1],r="valid",a,s){return B(()=>{a==null&&(a=gr()),St(a);let i=OA(e,a);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=tl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var BA=class extends DA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=yt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Lt(e.depthwiseConstraint),this.depthwiseRegularizer=gt(e.depthwiseRegularizer)}build(e){if(e=lt(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],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,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 B(()=>{e=ze(e);let n=wte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Wr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=vr(t,this.kernelSize[0],this.padding,this.strides[0]),s=vr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=vt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Pt(this.depthwiseRegularizer),e}};BA.className="DepthwiseConv2D";re.registerClass(BA);function N7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function S7(e,t,n,r=!1,a,s,i=!1,o=!1){return B(()=>{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(wr(2,l));if(t=nt(t,u),s!=null)throw new $e("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."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Tn(a,-1)),a=nt(a,u)),r&&(t=Rn(t,0),a!=null&&(a=Rn(a,0)));let c=[],h,d=n,p=t.shape[0],m=sr(t),f;a!=null&&(f=sr(a));for(let y=0;ye(g,d));if(a==null)h=_[0],d=_[1];else{let b=B(()=>{let w=f[y],x=Cn(w).sub(w),N=_[0].mul(w).add(d[0].mul(x)),T=d.map((E,M)=>_[1][M].mul(w).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=Fn(c,1)),[h,A,d]})}var Br=class extends je{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 Dp({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 Gt({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 wr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){cA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return B(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}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;ni.shape[i.shape.length-1]),s))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=s.map(i=>new Gt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ra("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(r=>Et([n,r])):this.states_=[Et([n,this.cell.stateSize])];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Et([n,r])):this.states_[0]=Et([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()):Ee(this.states_);for(let r=0;rVt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=N7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.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 Gt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof _r){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=ze(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=S7((d,p)=>{let m=this.cell.call([d].concat(p),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return B(()=>{let t=Et(e.shape);return t=Se(t,[1,2]),t=hc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Zm(t,[1,n]):t):this.cell.stateSize>1?[Zm(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()===Br.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=br(r,n);return new e(Object.assign(t,{cell:a}))}};Br.className="RNN";re.registerClass(Br);var fc=class extends je{},zp=class extends fc{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,jt(this.units,"units"),this.activation=Wa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Tl([1,za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Tl([1,za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(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 B(()=>{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 r=t.training==null?!1:t.training;0Cn(e),rate:this.dropout,training:r})),0Cn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Lr(P(e,s),this.kernel.read()):a=Lr(e,this.kernel.read()),this.bias!=null&&(a=Wr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,Lr(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:La(this.activation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),recurrentConstraint:Pt(this.recurrentConstraint),biasConstraint:Pt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};zp.className="SimpleRNNCell";re.registerClass(zp);var VA=class extends Br{constructor(e){e.cell=new zp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};VA.className="SimpleRNN";re.registerClass(VA);var Pp=class extends fc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,jt(this.units,"units"),this.activation=Wa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Wa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Tl([1,za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Tl([1,za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(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 B(()=>{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,r=e[1];e=e[0],0Cn(e),rate:this.dropout,training:n,count:3})),0Cn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};UA.className="GRU";re.registerClass(UA);var bc=class extends fc{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,jt(this.units,"units"),this.activation=Wa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Wa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=gt(e.kernelRegularizer),this.recurrentRegularizer=gt(e.recurrentRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Tl([1,za([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Tl([1,za([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=lt(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 r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends lr{apply(i,o){let l=a.apply([s]),u=new pp().apply([s]),c=a.apply([s*2]);return x3(x3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return B(()=>{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 ${e.length}.`);let r=e[1],a=e[2];e=e[0],0Cn(e),rate:this.dropout,training:n,count:4})),0Cn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};HA.className="LSTM";re.registerClass(HA);var Dp=class extends fc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return B(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i{yi(`RNNCell_${r}`,()=>{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=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(br(a,n));return new e({cells:r})}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 hA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s_3(t(),n),i=()=>pc(s,t,r);return!a||a<=1?Vt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Vt(o.clone()))}var _te=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(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,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}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 B(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Et(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ra("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.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(()=>Et(a)):this.states_=[Et(a)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Et(a)):this.states_[0]=Et(a);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()):Ee(this.states_);for(let s=0;sVt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=vr(l,r[0],a,s[0],i[0]),h=vr(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}};T7.className="ConvRNN2D";var Lp=class extends bc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,jt(this.filters,"filters"),this.kernelSize=Ml(n,2,"kernelSize"),this.kernelSize.forEach(o=>jt(o,"kernelSize")),this.strides=Ml(r||1,2,"strides"),this.strides.forEach(o=>jt(o,"strides")),this.padding=a||"valid",Kn(this.padding),this.dataFormat=s||"channelsLast",St(this.dataFormat),this.dilationRate=Ml(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>jt(o,"dilationRate"))}build(e){var t;e=lt(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 r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);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 lr{apply(c,h){let d=l.apply([u]),p=Mr([u]),m=l.apply([u*2]);return Jm([d,p,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return B(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0Cn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0Cn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,m=l(a,p,0),f=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[_,b,w,x]=Jt(this.kernel.read(),i,g),[N,T,E,M]=this.useBias?Jt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,_,N,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,w,E,this.padding),d=this.inputConv(d,x,M,this.padding);let[D,L,W,U]=Jt(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,D),f=this.recurrentConv(f,L),A=this.recurrentConv(A,W),y=this.recurrentConv(y,U);let H=this.recurrentActivation.apply(se(u,m)),X=this.recurrentActivation.apply(se(c,f)),G=se(P(X,s),P(H,this.activation.apply(se(h,A)))),ee=P(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=_te(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=Yr(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Wr(a,n,this.dataFormat):a}recurrentConv(e,t){return Yr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Lp.className="ConvLSTM2DCell";re.registerClass(Lp);var jA=class extends T7{constructor(e){let t=new Lp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};jA.className="ConvLSTM2D";re.registerClass(jA);var Wp=class extends je{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 r=0;r{this.invokeCallHook(e,t);let n=ze(e);if(0_3(n,this.rate,a,this.seed),()=>n,r)}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()}};Wp.className="Dropout";re.registerClass(Wp);var GA=class extends Wp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};GA.className="SpatialDropout1D";re.registerClass(GA);var qA=class extends je{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,jt(this.units,"units"),this.activation=Wa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Lt(e.kernelConstraint),this.biasConstraint=Lt(e.biasConstraint),this.kernelRegularizer=gt(e.kernelRegularizer),this.biasRegularizer=gt(e.biasRegularizer),this.activityRegularizer=gt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=lt(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=lt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=ze(e),r=u3(this.activation.getClassName()),a;return r!=null?a=Lr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Lr(n,this.kernel.read()),this.bias!=null&&(a=Wr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:La(this.activation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),biasConstraint:Pt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};qA.className="Dense";re.registerClass(qA);var XA=class extends je{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=lt(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)}). 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Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;aa.length);e.indexOf(null)===-1&&Oa(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return B(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=za(r);for(let s of e){let i=s.rank;for(let o=0;o1){let u=wr(1,l).concat([0]);n.push(nt(o,u)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=nt(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(wr(0,i-1));s=nt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Tn(r,0));let n=t[0];for(let r=1;r{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return B(()=>Jm(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new V("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new V("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new V(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return B(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s3||t.shape.length>3)throw new $e("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 $e("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return B(()=>{let i;if(r>a){i=r-a;let l=[];for(let u=0;ur){i=a-r;let l=[];for(let u=0;u0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c"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 $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new V(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>vc(a,e[s].shape.length)):r=[vc(this.axes,t.shape.length),vc(this.axes,n.shape.length)],this.normalize&&(t=Ip(t,r[0]),n=Ip(n,r[1])),bte(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[vc(this.axes,e.length),vc(this.axes,t.length)],n}computeOutputShape(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].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};oy.className="Dot";re.registerClass(oy);var ly=class extends je{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 B(()=>{this.invokeCallHook(e,t);let n=ze(e);return pc(()=>dp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};ly.className="GaussianNoise";re.registerClass(ly);var uy=class extends je{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 B(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.rate>0&&this.rate<1?pc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(dp(n.shape,1,r))},()=>n,t.training||!1):n})}};uy.className="GaussianDropout";re.registerClass(uy);var cy=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ze(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 B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return pc(()=>{let r=ze(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Ea(ol(n),this.rate);o=cc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>ze(e),t.training||!1)}return e})}};cy.className="AlphaDropout";re.registerClass(cy);function kc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=w5(e,t,n,r,a,s);else if(e.rank===3)i=_5(e,t,n,r,a,s);else if(e.rank===4)i=b5(e,t,n,r,a,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function vte(e,t,n,r,a=.001){return B(()=>{let s=cd(e,r),i=s.mean,o=s.variance;return[kc(e,i,o,n,t,a),i,o]})}function kte(e,t,n,r,a=.001){return B(()=>{let s=cd(e,r),i=s.mean,o=s.variance,l=[];for(let p of wr(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let u=i.reshape(l),c=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[kc(e,u,c,d,h,a),i,o]})}function Ite(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),wr(0,e.rank-1))?vte(e,t,n,r,a):kte(e,t,n,r,a)}var hy=class extends je{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.movingMeanInitializer=yt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=yt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Lt(e.betaConstraint),this.gammaConstraint=Lt(e.gammaConstraint),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer)}build(e){e=lt(e);let 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 Gt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training,r=ze(e),a=r.shape,s=a.length,i=wr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=fi(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,wr(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,_=this.scale?this.gamma.read().reshape(l):null;return kc(r,A,y,g,_,this.epsilon)}else return kc(r,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 h();let[d,p,m]=Ite(r,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(A,y,g)=>{B(()=>{let _=1-g,b=A.read(),w=b.sub(y).mul(_);A.write(b.sub(w))})};return(()=>{f(this.movingMean,p,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:vt(this.betaInitializer),gammaInitializer:vt(this.gammaInitializer),movingMeanInitializer:vt(this.movingMeanInitializer),movingVarianceInitializer:vt(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:Pt(this.betaConstraint),gammaConstraint:Pt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};hy.className="BatchNormalization";re.registerClass(hy);var dy=class extends je{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=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.betaRegularizer=gt(e.betaRegularizer),this.gammaRegularizer=gt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=lt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Oa(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=ze(e),r=n.shape,a=r.length;return B(()=>{let s=!0,{mean:i,variance:o}=cd(n,this.axis,s),l=fi(1,a);for(let m of this.axis)l[m]=r[m];let u=m=>m!=null&&m.shape.length!==a&&this.axis!==[a-1]?m.reshape(l):m,c=u(this.gamma.read()),h=u(this.beta.read()),d=[],p=[];for(let m=0;m{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=gr()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],Jr(e,r)})}var py=class extends je{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?gr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new V(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 Gt({ndim:4})]}computeOutputShape(e){e=lt(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 B(()=>Nte(ze(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};py.className="ZeroPadding2D";re.registerClass(py);function Bp(e,t,n,r,a,s){return B(()=>{St(a),p3(s),Kn(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=gr()),s==null&&(s="max"),e=OA(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Du(e,t,n,o):i=Eu(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function E7(e,t,n,r,a,s){return B(()=>{St(a),p3(s),Kn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=gr()),s==null&&(s="max"),e=v7(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Of(e,t,n,o):i=_f(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var C7=class extends je{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(jt(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)}`);jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Kn(this.padding),this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){e=lt(e);let t=vr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return B(()=>{this.invokeCallHook(e,t),e=hc(ze(e),2);let n=this.poolingFunction(ze(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ca(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},fy=class extends C7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),Bp(e,t,n,r,a,"max")}};fy.className="MaxPooling1D";re.registerClass(fy);var my=class extends C7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),Bp(e,t,n,r,a,"avg")}};my.className="AveragePooling1D";re.registerClass(my);var R7=class extends je{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];jt(this.poolSize,"poolSize"),jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Kn(this.padding),this.inputSpec=[new Gt({ndim:4})]}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=vr(t,this.poolSize[0],this.padding,this.strides[0]),n=vr(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 B(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(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}},Ay=class extends R7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),Bp(e,t,n,r,a,"max")}};Ay.className="MaxPooling2D";re.registerClass(Ay);var yy=class extends R7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),Bp(e,t,n,r,a,"avg")}};yy.className="AveragePooling2D";re.registerClass(yy);var F7=class extends je{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];jt(this.poolSize,"poolSize"),jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Kn(this.padding),this.inputSpec=[new Gt({ndim:5})]}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=vr(t,this.poolSize[0],this.padding,this.strides[0]),n=vr(n,this.poolSize[1],this.padding,this.strides[1]),r=vr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(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}},gy=class extends F7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),E7(e,t,n,r,a,"max")}};gy.className="MaxPooling3D";re.registerClass(gy);var xy=class extends F7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Kn(r),E7(e,t,n,r,a,"avg")}};xy.className="AveragePooling3D";re.registerClass(xy);var M7=class extends je{constructor(e){super(e);this.inputSpec=[new Gt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},wy=class extends M7{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=ze(e);return bt(n,1)})}};wy.className="GlobalAveragePooling1D";re.registerClass(wy);var _y=class extends M7{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=ze(e);return Gn(n,1)})}};_y.className="GlobalMaxPooling1D";re.registerClass(_y);var $7=class extends je{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Gt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},by=class extends $7{call(e,t){return B(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?bt(n,[1,2]):bt(n,[2,3])})}};by.className="GlobalAveragePooling2D";re.registerClass(by);var vy=class extends $7{call(e,t){return B(()=>{let n=ze(e);return 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r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=k("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=k("tensorArrayId",e,t,n),a=k("tensor",e,t,n),s=k("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[be(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let 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k("x",e,t,n).map(u=>Ut(u.shape));case"Size":return[be(k("x",e,t,n).size,"int32")];case"Rank":return[be(k("x",e,t,n).rank,"int32")];case"NoOp":return[be(1)];case"Print":let s=k("x",e,t,n),i=k("data",e,t,n),o=k("message",e,t,n),l=k("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let u=0;ue.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),B(()=>{let r=sr(t),a=n.length,s=r.length;v.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i{let r=[];for(let a=0;a{switch(e.op){case"HashTable":case"HashTableV2":{let a=k("keyDType",e,t,n),s=k("valueDType",e,t,n),i=new Cne(a,s);return 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B(()=>Dne(s,i,o));case"slice_join":return B(()=>zne(s,i,o));case"spectral":return B(()=>Pne(s,i,o));case"transformation":return B(()=>Lne(s,i,o));case"hash_table":return Rne(s,i,o,r);case"custom":let l=U7(s.op);if(l&&l.customExecutor)return l.customExecutor(new Ane(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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t=0;tt.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 Av(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>zn(d)[0]),c=[];r!=null&&(c=r.map(d=>zn(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((mv(d)||Wne(d)||Bne(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Vne(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(c=>zn(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{r.has(c.name)&&s.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&s.push(c)}),o!=null&&o.forEach(c=>{r.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return u}var Une=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Hne=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],jne=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function mv(e){return Une.indexOf(e.op)>=0}function Wne(e){return Hne.indexOf(e.op)>=0}function Bne(e){return jne.indexOf(e.op)>=0}var Wy=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Wy(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(r=>r.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 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t=yn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(yn.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]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=yn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Wy(lv.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=lv.Instance.transformGraph(e.modelInitializer);this.initializer=new Wy(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=yn.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){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ze)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Nt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Array(e),n=this.length();for(let r=0;rt===!0)}rowMajorBatch(e,t=!0){return new wre(this,e,t)}columnMajorBatch(e,t=!0,n=kv){return this.rowMajorBatch(e,t).map(r=>ore(r,n))}concatenate(e,t){return new Tv(Sv([this,e]),t)}take(e){return e<0||e==null?this:new xre(this,e)}skip(e){return e<0||e==null?this:new gre(this,e)}prefetch(e){return new Cv(this,e)}shuffle(e,t){return new Ire(this,e,t)}serial(){return new yre(this)}},dre=class extends qt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:hre(e),done:!1}}},pre=class extends qt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},yre=class extends qt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},gre=class extends qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},wre=class extends qt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},_re=class extends qt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},bre=class extends qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=fr.getTensorsInContainer(e.value),n=this.transform(e.value),r=fr.getTensorsInContainer(n);for(let a of t)fr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},vre=class extends qt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Ev=class extends qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=fr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=fr.getTensorsInContainer(n);for(let a of t)fr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Uy=class extends qt{constructor(){super();this.outputQueue=new By,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}}},kre=class extends Uy{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=fr.getTensorsInContainer(e.value),n=this.transform(e.value),r=fr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)fr.isTensorInList(a,r)||a.dispose();return!0}},Tv=class extends qt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Va;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Va||(Va={}));var mre=class extends qt{constructor(e,t=Va.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof qt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await Iv(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Va.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Va.SHORTEST:return{value:null,done:!0};case Va.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Cv=class extends qt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Nv(t)}summary(){return`${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()}},Ire=class extends Cv{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=sre.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}}},$l=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Pn(async()=>(await n.iterator()).columnMajorBatch(e,t,Nre),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Pn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Pn(async()=>(await t.iterator()).filter(r=>B(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Pn(async()=>(await t.iterator()).map(n=>B(()=>e(n))),this.size)}mapAsync(e){let t=this;return Pn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Pn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Pn(async()=>{let r=Vy(async()=>({value:await t.iterator(),done:!1}));return fre(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=are.alea(t||v.now().toString());return Pn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Pn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};$l.MAX_BUFFER_SIZE=1e4;function Pn(e,t=null){return new class extends $l{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Zne(e){return Pn(async()=>Sv(e),e.length)}function Yne(e){if(!Ol(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await Iv(e,r=>{if(r instanceof $l)return{value:r.iterator(),recurse:!1};if(Ol(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Are(n,Va.SHORTEST)},t)}function Nre(e){if(e===null)return null;let t=e[0];return ure(t)?{value:Sre(e),recurse:!1}:{value:null,recurse:!0}}function Sre(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ze?Fn(e):mr(e)}var xv=class extends $l{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` `).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Gp='"',Sc=Symbol("out"),Rv=Symbol("field"),qp=Symbol("quote"),Hy=Symbol("quoteafterquote"),Fv=Symbol("quoteinquote"),wv=class extends $l{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 xv(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((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" 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={},r={};for(let a=0;a14||!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(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Mv(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: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),mr(n,t)}},$v=class extends qt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ut([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=wn([s,a,o,i],[1,4])}else this.cropBox=wn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new $v(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=Xo.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return B(()=>{let t=Tn(me(e,"float32"),0),n;n=Ge.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return j(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Ov=class{},Dv=class extends qt{split(e){return new Tre(this,e)}},Tre=class extends Dv{constructor(e,t){super();this.upstream=e,this.impl=new Ere(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Ere=class extends Uy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${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}},Rre=class extends qt{decodeUTF8(){return new Cre(this)}},Cre=class extends Dv{constructor(e){super();this.upstream=e,this.impl=new Fre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Fre=class extends Uy{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=$8();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},zv=class extends Rre{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(J().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 r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof 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r2={};Jn(r2,{FaceBoxes:()=>a2,load:()=>eae});var n2={};function hr(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(o=>o.kernelTimeMs>0).reduce((o,l)=>o+=l.kernelTimeMs,0),a=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.kernelTimeMs>0).sort((o,l)=>l.kernelTimeMs-o.kernelTimeMs),s=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.totalBytesSnapshot>0).sort((o,l)=>l.totalBytesSnapshot-o.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n);let i={newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s};n2[e]=i,Te("Human profiler",e,i)}var a2=class{constructor(t,n){this.enlarge=1.1,this.model=t,this.config=n}async estimateFaces(t,n){n&&(this.config=n);let r=[],a=Ge.resizeBilinear(t,[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),s=a.toInt(),i,o;if(n.profile){let l=await 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l2={};Jn(l2,{load:()=>d2,predict:()=>p2});var Ii,u2={gender:""},e0=Number.MAX_SAFE_INTEGER,c2=!1,h2=[.2989,.587,.114];async function d2(e){return Ii||(Ii=await Nt(e.face.gender.modelPath),c2=Ii.inputs[0].shape[3]===1,e.debug&&Te(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),Ii}async function p2(e,t){return Ii?e0{let r=Ge.resizeBilinear(e,[t.face.gender.inputSize,t.face.gender.inputSize],!1),a;c2?a=B(()=>{let[o,l,u]=Jt(r,3,3),c=P(o,h2[0]),h=P(l,h2[1]),d=P(u,h2[2]);return Yo([c,h,d]).sub(.5).mul(2)}):a=P(r,[255]),Ee(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await Ii.predict(a));else{let o=t.face.gender.enabled?await Hn(()=>Ii.predict(a)):{};s=o.result.clone(),o.result.dispose(),hr("gender",o)}if(a.dispose(),s){let o=s.dataSync();if(c2){let l=Math.trunc(100*Math.abs(o[0]-o[1]))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]>o[1]?"female":"male",i.confidence=l)}else{let 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this.storedBoxes[i]=null;y.dispose()}else{let l=i0(o0(o),_6),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var v6=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.703125,y_center:.046875},{w:1,h:1,x_center:.703125,y_center:.046875},{w:1,h:1,x_center:.734375,y_center:.046875},{w:1,h:1,x_center:.734375,y_center:.046875},{w:1,h:1,x_center:.765625,y_center:.046875},{w:1,h:1,x_center:.76562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k6=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],I6=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var dr;async function H2(e){return dr||(dr=await Nt(e.body.modelPath),dr.width=parseInt(dr.signature.inputs["input_1:0"].tensorShape.dim[2].size),dr.height=parseInt(dr.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Te(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),dr}async function j2(e,t){if(!dr||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Ge.resizeBilinear(e,[dr.width||t.body.inputSize,dr.height||t.body.inputSize],!1),a=ge(r,[255]);r.dispose();let s;if(t.profile){let u=await Hn(()=>dr.predict(a));s=u.result.find(c=>c.size===195||c.size===155).dataSync(),u.result.forEach(c=>c.dispose()),hr("blazepose",u)}else{let u=await dr.predict(a);s=u.find(c=>c.size===195||c.size===155).dataSync(),u.forEach(c=>c.dispose())}a.dispose();let i=[],o=s.length===195?k6:I6,l=5;for(let u=0;u{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.yl.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},S6=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=e[n].mesh[35][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing camera"}):t.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let o=e[n].mesh[152][2];Math.abs(o)>10&&t.push({face:n,gesture:`head ${o<0?"up":"down"}`})}return t},T6=e=>{if(!e)return[];let t=[];for(let n=0;n{if(!e)return[];let t=[];for(let n=0;n0){let a=r.reduce((i,o)=>i.position[2]i.position[1](u[d]=0,h))},a=function(o,l){let u=e.createShader(l);if(e.shaderSource(u,o),e.compileShader(u),!e.getShaderParameter(u,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(u));return u};this.uniform={},this.attribute={};let s=a(t,e.VERTEX_SHADER),i=a(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,s),e.attachShader(this.id,i),e.linkProgram(this.id),!e.getProgramParameter(this.id,e.LINK_STATUS))throw new Error("Filter: GL link failed",e.getProgramInfoLog(this.id));e.useProgram(this.id),r(t,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=e.getAttribLocation(this.id,o);r(t,"uniform",this.uniform),r(n,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=e.getUniformLocation(this.id,o)}function C6(e){e||(e={});let t=0,n=null,r=!1,a=-1,s=[null,null],i=[],o=-1,l=-1,u=null,c=null,h={},d=e.canvas||document.createElement("canvas"),p={},m={INTERMEDIATE:1},f=d.getContext("webgl");if(!f)throw new Error("Filter: getContext() failed");this.addFilter=function(w){let x=Array.prototype.slice.call(arguments,1),N=h[w];i.push({func:N,args:x})},this.reset=function(){i=[]};let A=function(w,x){if(!(w===o&&x===l)){if(d.width=w,o=w,d.height=x,l=x,!u){let N=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);u=f.createBuffer(),f.bindBuffer(f.ARRAY_BUFFER,u),f.bufferData(f.ARRAY_BUFFER,N,f.STATIC_DRAW),f.pixelStorei(f.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}f.viewport(0,0,o,l),s=[null,null]}},y=function(w,x){let N=f.createFramebuffer();f.bindFramebuffer(f.FRAMEBUFFER,N);let T=f.createRenderbuffer();f.bindRenderbuffer(f.RENDERBUFFER,T);let E=f.createTexture();return f.bindTexture(f.TEXTURE_2D,E),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,w,x,0,f.RGBA,f.UNSIGNED_BYTE,null),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.LINEAR),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.LINEAR),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.framebufferTexture2D(f.FRAMEBUFFER,f.COLOR_ATTACHMENT0,f.TEXTURE_2D,E,0),f.bindTexture(f.TEXTURE_2D,null),f.bindFramebuffer(f.FRAMEBUFFER,null),{fbo:N,texture:E}},g=function(w){return s[w]=s[w]||y(o,l),s[w]},_=function(w=null){var E,M;let x=null,N=null,T=!1;t===0?x=n:x=(E=g(a))==null?void 0:E.texture,t++,r&&!(w&m.INTERMEDIATE)?(N=null,T=t%2==0):(a=(a+1)%2,N=(M=g(a))==null?void 0:M.fbo),f.bindTexture(f.TEXTURE_2D,x),f.bindFramebuffer(f.FRAMEBUFFER,N),f.uniform1f(c.uniform.flipY,T?-1:1),f.drawArrays(f.TRIANGLES,0,6)};this.apply=function(w){if(A(w.width,w.height),t=0,n||(n=f.createTexture()),f.bindTexture(f.TEXTURE_2D,n),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.NEAREST),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.NEAREST),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,f.RGBA,f.UNSIGNED_BYTE,w),i.length===0)return _(),d;for(let x=0;x0,a=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,s=r,i=a;if(t.filter.width>0?s=t.filter.width:t.filter.height>0&&(s=r*(t.filter.height/a)),t.filter.height>0?i=t.filter.height:t.filter.width>0&&(i=a*(t.filter.width/r)),!s||!i)return Te("Human: invalid input",e),null;(!Tt||Tt.width!==s||Tt.height!==i)&&(Tt=typeof OffscreenCanvas!="undefined"?new 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HTMLCanvasElement))return;let n=e.getContext("2d");if(!n)return;n.font=ce.font,n.fillStyle=ce.color;let r=1;for(let a=0;a1&&i[1].length>0){let o=s[1]>0?`#${s[1]}`:"",l=`${s[0]} ${o}: ${i[1]}`;ce.shadowColor&&ce.shadowColor!==""&&(n.fillStyle=ce.shadowColor,n.fillText(l,8,2+r*ce.lineHeight)),n.fillStyle=ce.labelColor,n.fillText(l,6,0+r*ce.lineHeight),r+=1}}}async function Z6(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n)for(let r of t){n.font=ce.font,n.strokeStyle=ce.color,n.fillStyle=ce.color,ce.drawBoxes&&Z2(n,r.box[0],r.box[1],r.box[2],r.box[3]);let a=[];if(a.push(`face confidence: ${Math.trunc(100*r.confidence)}%`),r.genderConfidence&&a.push(`${r.gender||""} ${Math.trunc(100*r.genderConfidence)}% confident`),r.age&&a.push(`age: ${r.age||""}`),r.iris&&a.push(`iris distance: ${r.iris}`),r.emotion&&r.emotion.length>0){let s=r.emotion.map(i=>`${Math.trunc(100*i.score)}% ${i.emotion}`);a.push(s.join(" 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o=[];o.push([s.position.x,s.position.y]),i=t[r].keypoints.find(l=>l.part==="leftElbow"),i&&i.score>pt.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[r].keypoints.find(l=>l.part==="leftWrist"),i&&i.score>pt.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[r].keypoints.find(l=>l.part==="leftPalm"),i&&i.score>pt.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Ll(n,o)}if(s=t[r].keypoints.find(o=>o.part==="rightShoulder"),s&&s.score>pt.body.scoreThreshold){let o=[];o.push([s.position.x,s.position.y]),i=t[r].keypoints.find(l=>l.part==="rightElbow"),i&&i.score>pt.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[r].keypoints.find(l=>l.part==="rightWrist"),i&&i.score>pt.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[r].keypoints.find(l=>l.part==="rightPalm"),i&&i.score>pt.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Ll(n,o)}n.stroke(a)}}}}async function J6(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=ce.font;for(let r of t){if(ce.drawBoxes&&(n.strokeStyle=ce.color,n.fillStyle=ce.color,Z2(n,r.box[0],r.box[1],r.box[2],r.box[3]),ce.shadowColor&&ce.shadowColor!==""&&(n.fillStyle=ce.shadowColor,n.fillText("hand",r.box[0]+3,1+r.box[1]+ce.lineHeight,r.box[2])),n.fillStyle=ce.labelColor,n.fillText("hand",r.box[0]+2,0+r.box[1]+ce.lineHeight,r.box[2]),n.stroke()),ce.drawPoints&&r.landmarks&&r.landmarks.length>0)for(let a of r.landmarks)n.fillStyle=ce.useDepth?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.5)`:ce.color,c0(n,a[0],a[1]);if(ce.drawPolygons){let a=s=>{if(!!s)for(let i=0;i0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function Eae(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function Cae(e,t){!t||!e||e instanceof HTMLCanvasElement&&(Z6(e,t.face),Y6(e,t.body),J6(e,t.hand),K6(e,t.gesture))}var ct=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Rc(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Rc(s,i):n[a]=i}),n),{})}var e4=class{constructor(t={}){this.tf=rh,this.draw=K2,this.package=q2,this.version=X2,this.config=Rc(pt,t),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.image=n=>G2(n,this.config),this.facemesh=Q6,this.age=s2,this.gender=l2,this.emotion=f2,this.body=this.config.body.modelType.startsWith("posenet")?F2:U2,this.hand=L2}profile(){return this.config.profile?n2:{}}analyze(...t){if(!this.analyzeMemoryLeaks)return;let n=this.tf.engine().state.numTensors,r=this.numTensors;this.numTensors=n;let a=n-r;a!==0&&Te(...t,a)}sanity(t){if(!this.checkSanity)return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null}simmilarity(t,n){return this.config.face.embedding.enabled?Jv(t,n):0}async load(t=null){this.state="load";let n=ct();t&&(this.config=Rc(this.config,t)),this.firstRun&&(this.config.debug&&Te(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Te("configuration:",this.config),this.config.debug&&Te("tf flags:",this.tf.ENV.flags)));let r=this.config.face.detector.modelPath.includes("faceboxes")?r2:Q6;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose]=await Promise.all([this.models.face||(this.config.face.enabled?r.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?i2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?d2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?y2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?x2(this.config):null),this.models.handpose||(this.config.hand.enabled?V2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("posenet")?$2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelType.startsWith("blazepose")?H2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await r.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await i2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await d2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await y2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await x2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await V2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelType.startsWith("posenet")&&(this.models.posenet=await $2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelType.startsWith("blazepose")&&(this.models.blazepose=await H2(this.config))),this.firstRun&&(this.config.debug&&Te("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let a=Math.trunc(ct()-n);a>(this.perf.load||0)&&(this.perf.load=a)}async checkBackend(t=!1){if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=ct();if(this.state="backend",this.config.backend&&this.config.backend!==""){this.config.debug&&Te("setting backend:",this.config.backend),this.config.backend==="wasm"&&(this.config.debug&&Te("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT")||Te("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&Lv();try{await this.tf.setBackend(this.config.backend)}catch(r){Te("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Te("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Te(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(ct()-n)}}async detectFace(t){var u,c,h,d,p,m;let n,r,a,s,i,o=[];this.state="run:face",n=ct();let l=await((u=this.models.face)==null?void 0:u.estimateFaces(t,this.config));this.perf.face=Math.trunc(ct()-n);for(let f of l){if(this.analyze("Get Face"),!f.image||f.image.isDisposedInternal){Te("Face object is disposed:",f.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?o2(f.image,this.config):{}:(this.state="run:age",n=ct(),r=this.config.face.age.enabled?await o2(f.image,this.config):{},this.perf.age=Math.trunc(ct()-n)),this.analyze("Start Gender:"),this.config.async?a=this.config.face.gender.enabled?p2(f.image,this.config):{}:(this.state="run:gender",n=ct(),a=this.config.face.gender.enabled?await p2(f.image,this.config):{},this.perf.gender=Math.trunc(ct()-n)),this.analyze("Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?g2(f.image,this.config):{}:(this.state="run:emotion",n=ct(),s=this.config.face.emotion.enabled?await g2(f.image,this.config):{},this.perf.emotion=Math.trunc(ct()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?w2(f.image,this.config):[]:(this.state="run:embedding",n=ct(),i=this.config.face.embedding.enabled?await w2(f.image,this.config):[],this.perf.embedding=Math.trunc(ct()-n)),this.analyze("End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((c=f==null?void 0:f.annotations)==null?void 0:c.leftEyeIris)&&((h=f==null?void 0:f.annotations)==null?void 0:h.rightEyeIris)&&(delete f.annotations.leftEyeIris,delete f.annotations.rightEyeIris);let A=((d=f.annotations)==null?void 0:d.leftEyeIris)&&((p=f.annotations)==null?void 0:p.rightEyeIris)?11.7*Math.max(Math.abs(f.annotations.leftEyeIris[3][0]-f.annotations.leftEyeIris[1][0]),Math.abs(f.annotations.rightEyeIris[4][1]-f.annotations.rightEyeIris[2][1])):0;o.push({confidence:f.confidence,faceConfidence:f.faceConfidence,boxConfidence:f.boxConfidence,box:f.box,mesh:f.mesh,boxRaw:f.boxRaw,meshRaw:f.meshRaw,annotations:f.annotations,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:A!==0?Math.trunc(A)/100:0}),(m=f.image)==null||m.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),o}async detect(t,n={}){return new Promise(async r=>{var d,p,m,f;this.state="config";let a;this.config=Rc(this.config,n),this.state="check";let s=this.sanity(t);s&&(Te(s,t),r({error:s}));let i,o,l,u=ct();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:"),a=ct();let c=G2(t,this.config);if(!c||!c.tensor){Te("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(ct()-a),this.analyze("Get Image:"),this.config.async?(l=this.config.face.enabled?this.detectFace(c.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=ct(),l=this.config.face.enabled?await this.detectFace(c.tensor):[],this.perf.face=Math.trunc(ct()-a)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelType.startsWith("posenet")?i=this.config.body.enabled?(d=this.models.posenet)==null?void 0:d.estimatePoses(c.tensor,this.config):[]:i=this.config.body.enabled?j2(c.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",a=ct(),this.config.body.modelType.startsWith("posenet")?i=this.config.body.enabled?await((p=this.models.posenet)==null?void 0:p.estimatePoses(c.tensor,this.config)):[]:i=this.config.body.enabled?await j2(c.tensor,this.config):[],this.perf.body=Math.trunc(ct()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(o=this.config.hand.enabled?(m=this.models.handpose)==null?void 0:m.estimateHands(c.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=ct(),o=this.config.hand.enabled?await((f=this.models.handpose)==null?void 0:f.estimateHands(c.tensor,this.config)):[],this.perf.hand=Math.trunc(ct()-a)),this.analyze("End Hand:"),this.config.async&&([l,i,o]=await Promise.all([l,i,o])),c.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let h=[];this.config.gesture.enabled&&(a=ct(),h=[...S6(l),...N6(i),...E6(o),...T6(l)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(ct()-a)),this.perf.total=Math.trunc(ct()-u),this.state="idle",r({face:l,body:i,hand:o,gesture:h,performance:this.perf,canvas:c.canvas})})}async warmupBitmap(){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(l0);break;case"full":n=await t(u0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r}async warmupCanvas(){return new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+l0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+u0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)})}async warmupNode(){let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(l0):t(u0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s}async warmup(t){let n=ct();t&&(this.config=Rc(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await this.warmupBitmap():typeof Image!="undefined"?a=await this.warmupCanvas():a=await this.warmupNode(),this.config.videoOptimized=r;let s=ct();return this.config.debug&&Te("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};export{e4 as default}; /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the License); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an AS IS BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** @license See the LICENSE file. */ //# sourceMappingURL=human.esm.js.map