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this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=vf(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let 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t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of 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o=s.reduce((g,y)=>g*y),l=R.getReshaped(a.shape,s,o),u=R.getPermuted(l.length,s.length),c=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(c,i,s.length),p=wt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=dr({inputs:{x:p},backend:n,attrs:{perm:u}}),m=wt({inputs:{x:f},backend:n,attrs:{shape:c}}),A=vi({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var sM={kernelName:wu,backendName:"cpu",kernelFunc:aM};function iM(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=Um(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var oM={kernelName:Wh,backendName:"cpu",kernelFunc:iM},lM=lt(Fa,(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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Bt(d.outShape,a.dtype),_=v.computeStrides(a.shape),x=v.computeStrides(s.shape),N=_[0],T=w?_[1]:_[2],E=w?_[2]:1,M=w?1:_[1],z=b.strides[0],B=w?b.strides[1]:b.strides[2],V=w?b.strides[2]:1,U=w?1:b.strides[1],j=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,G=b.values;for(let ee=0;ee=d.inHeight)continue;let ge=pe*x[0],me=Y+ue*T;for(let Se=0;Se=d.inWidth)continue;let rt=ge+Le*x[1],at=me+ze*E,ct=rt;for(let et=0;et=u.inDepth)continue;let ee=X*E[0],Y=z+G*T[1];for(let se=0;se=u.inHeight)continue;let ue=ee+Q*E[1],ge=Y+pe*T[2];for(let me=0;me=u.inWidth)continue;let ze=ue+Oe*E[2],rt=ge+Le*u.inChannels,at=ze;for(let ct=0;ctMath.cos(e)),NM={kernelName:_s,backendName:"cpu",kernelFunc:IM},SM=lt(mo,e=>Math.cosh(e)),TM={kernelName:mo,backendName:"cpu",kernelFunc:SM};function EM(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,f=s.shape[0],[m,A]=o,g=Ue([f,m,A,p],"float32"),y=n.data.get(s.dataId).values,w=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,_=v.computeStrides(a.shape),x=v.computeStrides(g.shape);for(let N=0;N=c)continue;let U=m>1?(z-E)*(h-1)/(m-1):0,j=A>1?(B-M)*(d-1)/(A-1):0;for(let X=0;X1?E*(h-1)+X*U:.5*(E+z)*(h-1);if(G<0||G>h-1){for(let ee=0;ee1?M*(d-1)+ne*j:.5*(M+B)*(d-1);if(le<0||le>d-1){for(let ge=0;ge1?M*(d-1)+ee*j:.5*(M+B)*(d-1);if(Y<0||Y>d-1){for(let le=0;leg+f-y-1:(g,y)=>g+y;for(let g=0;g`Only NHWC dataFormat supported on CPU for depthToSpace. 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y$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=Ww({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=hc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=Ym(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var x$={kernelName:oi,backendName:"cpu",kernelFunc:y$};function w$(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:f}=r,m=Bw({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=hc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=Ym(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var b$={kernelName:li,backendName:"cpu",kernelFunc:w$};function _$(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]=R.prepareAndValidate(r,a);if(u===0)return n.makeTensorInfo(l,r.dtype,[]);let d=Ue([u,c],r.dtype),p=n.data.get(a.dataId).values,f=n.data.get(r.dataId).values;for(let m=0;m=s/c)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let y=0;ye>=t?1:0),S$=Yt(Cs,N$,null,"bool"),T$={kernelName:Cs,backendName:"cpu",kernelFunc:S$};function E$(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=wt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=Uw(o,!0,n),u=wt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var C$={kernelName:Qh,backendName:"cpu",kernelFunc:E$},R$=lt(So,e=>Number.isFinite(e)?1:0,"bool"),F$={kernelName:So,backendName:"cpu",kernelFunc:R$},M$=lt(To,e=>Math.abs(e)===Infinity?1:0,"bool"),$$={kernelName:To,backendName:"cpu",kernelFunc:M$},D$=lt(Eo,e=>Number.isNaN(e)?1:0,"bool"),O$={kernelName:Eo,backendName:"cpu",kernelFunc:D$},z$=zt((e,t)=>e<=t?1:0),P$=Yt(Ro,z$,null,"bool"),L$={kernelName:Ro,backendName:"cpu",kernelFunc:P$};function W$(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=mw(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var B$={kernelName:td,backendName:"cpu",kernelFunc:W$},V$=lt(Fo,e=>Math.log1p(e)),U$={kernelName:Fo,backendName:"cpu",kernelFunc:V$},j$=zt((e,t)=>e&&t),H$=Yt(Mo,j$,null,"bool"),G$={kernelName:Mo,backendName:"cpu",kernelFunc:H$},q$=lt(Iu,e=>e?0:1,"bool"),X$={kernelName:Iu,backendName:"cpu",kernelFunc:q$},K$=zt((e,t)=>e||t),Z$=Yt(Nu,K$,null,"bool"),Y$={kernelName:Nu,backendName:"cpu",kernelFunc:Z$};function 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t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Pl(r,vp):p=new qa(r,vp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...yc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];c=R.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let 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)&&Lr().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 Ue(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=Lr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=dL){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 Lr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new oL(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new GP(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ki(e.shape),...Ii(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[ki(t),...Ii(t)],s=new Xb(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=gp(r),i;n?i=new Iz(s):i=new kz(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===Ac.DENSE){let m=yc(e.outputShape);i.texShape=m.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(m=>{if(m.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(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!mc(A.shape,m.shape)){let g=m,y=m.shape;m.shape=A.shape,m=this.packedReshape(m,y),o.push(m),A=this.texData.get(m.dataId),g.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=mP(e,l,u),h=this.getAndSaveBinary(c,()=>pP(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),fP(this.gpgpu,h,l,u,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}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=L(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(Ne(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?uL:cL}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=mb(n,o),t.texShape=c),a!=null){let h=gp(n),d,p=c[1],f=c[0],m=a instanceof Uint8Array;o?([p,f]=Ml(c[0],c[1]),d=new Ez(h,[f,p],m)):d=new Tz(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=tr.PIXELS:this.texData.get(A.dataId).usage=tr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let g=!0,y=this.runWebGLProgram(d,[A],r,null,g),w=this.texData.get(y.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(y.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=mL(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)}};Ll.nextDataId=0;function mL(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 Ll,2);var AL={forceHalfFloat:e_},t_=` if (isnan(a)) return a; if (isnan(b)) return b; `,Wl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOperation(float a, float b) { ${e} } void main() { float a = getAAtOutCoords(); float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } `}},kp=` 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; `,wc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.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=` ${dt(a)} coords = getOutputCoords(); `,a===1)s+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let i=yn("coords",a);s+=` bool nextRowOutOfBounds = (${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 Wn(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 gL={kernelName:Rs,backendName:"webgl",kernelFunc:Wn};function Xa(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=Wn({inputs:{x:r},backend:n}),l=Wn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var yL={kernelName:Bh,backendName:"webgl",kernelFunc:Xa},n_="return (a < 0.) ? b * a : a;",r_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function xL(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 wc(r_,a.shape,i.shape):new Wl(n_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var wL={kernelName:Fs,backendName:"webgl",kernelFunc:xL},a_="return (a < 0.) ? b * a : a;",s_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function bL(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wc(s_,r.shape,a.shape):new Wl(a_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var _L={kernelName:js,backendName:"webgl",kernelFunc:bL},i_="if (isnan(x)) return x;",vL=` if (isnan(a)) return a; if (isnan(b)) return b; `,kL=` 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 Pl(i.shape,t):c=new qa(i.shape,e),o.runWebGLProgram(c,[i],l)}}function on({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 f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,g]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:u.shape},T=new Wl(e,l.shape,u.shape);return c.runWebGLProgram(T,[x,N],lr(b.dtype,_.dtype))}),y=Xa({inputs:{real:A,imag:g},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(g),y}let h=s||lr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,g]=a(l.shape,u.shape,f.values,m.values,h),y=c.makeTensorInfo(g,h),w=c.texData.get(y.dataId);return w.values=A,y}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new wc(t,l.shape,u.shape,n):p=new Wl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function Ip(e,t=!1){if(e==="linear")return t?rL:QP;if(e==="relu")return t?sL:tL;if(e==="elu")return t?aL:eL;if(e==="relu6")return t?iL:nL;if(e==="prelu")return t?s_:a_;if(e==="leakyrelu")return t?r_:n_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var o_=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"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?m=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${i} }`:m=`vec4 activation(vec4 x) { ${i} }`,A="result = activation(result);");let g=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let y="rc.x",w="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. 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++${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],f=d.map(N=>"int "+N),m=yn("sourceLocR",h-1).concat("inIdx.r"),A=yn("sourceLocG",h-1).concat("inIdx.g"),g=yn("sourceLocB",h-1).concat("inIdx.b"),y=yn("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",b=r?"":` inIdx = round(vec4(getBestIndicesAChannel(${m.join()}), getBestIndicesAChannel(${A.join()}), getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${y.join()})));`,_=`vec4( getAChannel(${m.join()}), hasNextCol ? getAChannel(${A.join()}) : 0., hasNextRow ? getAChannel(${g.join()}) : 0., hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,x=r?"":` float getBestIndicesAChannel(${f.join()}) { return getChannel(getBestIndicesA(${d.join()}), vec2(${d.slice(-2).join()})); }`;this.userCode=` float getAChannel(${f.join()}) { return getChannel(getA(${d.join()}), vec2(${d.slice(-2).join()})); 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} return asin(x); `,lW=Qe({opSnippet:oW}),uW={kernelName:lo,backendName:"webgl",kernelFunc:lW},cW=Ir+"return log(x + sqrt(x * x + 1.0));",hW=Qe({opSnippet:cW}),dW={kernelName:uo,backendName:"webgl",kernelFunc:hW},pW=Ir+` return atan(x); `,fW=Qe({opSnippet:pW}),mW={kernelName:co,backendName:"webgl",kernelFunc:fW},AW=vL+` return atan(a, b); `,gW=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+kL+` return result; `,yW=on({opSnippet:AW,packedOpSnippet:gW}),xW={kernelName:po,backendName:"webgl",kernelFunc:yW},wW=Ir+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,bW=Qe({opSnippet:wW}),_W={kernelName:ho,backendName:"webgl",kernelFunc:bW},bc=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 f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,g="0.0";if(f||(g="-1.0 / 1e-20"),n){let N=">=";this.userCode=` const ivec2 strides = ivec2(${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?m:A:`wR * ${h} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let y="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let b=Math.floor(s/4)*4,_=s%4,x=` if (${f}) { avgValue += dot(values, ones); } else { minMaxValue = ${y}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${p}); const float initializationValue = ${g}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${g}); 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 + ${b}; if (${_===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${x} } else if (${_===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), initializationValue, initializationValue ); ${x} } else if (${_===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), getValue(batch, xR, xC + 2 * ${u}, d), initializationValue ); ${x} } } setOutput(${w}); } `}},yA=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,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,g=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",w="0.0";if(y||(w="-1.0 / 1e-20"),n){let E=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${A}, ${g}); 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 < ${f}; 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} * ${f} + wR * ${f} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let b="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let x=Math.floor(s/4)*4,N=s%4,T=` if (${y}) { avgValue += dot(values, ones); } else { minMaxValue = ${b}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${A}, ${g}); const float initializationValue = ${w}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${w}); 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 ); ${T} } } setOutput(${_}); } } `}};function vW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Wn({inputs:{x:a},backend:n});let h=new bc(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var kW={kernelName:As,backendName:"webgl",kernelFunc:vW};function IW(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=R.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new yA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var NW={kernelName:xu,backendName:"webgl",kernelFunc:IW},SW=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 >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},TW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=` const ivec3 pads = ivec3(${p}, ${f}, ${m}); const float avgMultiplier = float(${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 EW(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=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new TW(d);return n.runWebGLProgram(p,[a],i.dtype)}var CW={kernelName:Lh,backendName:"webgl",kernelFunc:EW};function RW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Fl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=R.computePool2DInfo(i.shape,o,l,1,u),h=new SW(c);return n.runWebGLProgram(h,[a],i.dtype)}var FW={kernelName:Ph,backendName:"webgl",kernelFunc:RW};function MW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Sp({a,b:s,transposeA:i,transposeB:o,backend:n})}var $W={kernelName:gs,backendName:"webgl",kernelFunc:MW},DW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.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))); } `}},OW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.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); } `}},zW=({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 OW(r.shape,a.shape,s.shape,c,h,l):new DW(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},PW={kernelName:Es,backendName:"webgl",kernelFunc:zW},WW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=dt(this.rank),n=`uniform int start[${this.rank}];`,r=LW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${xA[o]} = start[${o}] + coords.${xA[o]};`);a=` ${t} sourceLoc; 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${n} }`:a?w=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:w=` float activation(float x) { ${n} } `,b="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${w} const ivec2 strides = ivec2(${o}, ${l}); const ivec2 pads = ivec2(${s}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${y}]; ivec2 xRCCorner = ivec2(coords[${A}], coords[${g}]) * 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 < ${p}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${m}) { 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 (${f===1}) { if (${m}) { dotProd += getX(batch, xR, xC, ${p}) * getW(wR, wC, ${p}, d2); } else { dotProd += getX(batch, ${p}, xR, xC) * getW(wR, wC, ${p}, d2); } } else if (${f===2}) { vec2 wValues = vec2( getW(wR, wC, ${p}, d2), getW(wR, wC, ${p} + 1, d2) ); if (${m}) { 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, ${p} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${f===3}) { vec3 wValues = vec3( getW(wR, wC, ${p}, d2), getW(wR, wC, ${p} + 1, d2), getW(wR, wC, ${p} + 2, d2) ); if (${m}) { 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; ${_} ${b} setOutput(result); } `}},pB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${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 < ${p}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${f===1}) { dotProd += getX(batch, xF, xR, xC, ${p}) * getW(wF, wR, wC, ${p}, d2); } else if (${f===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, ${p} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${f===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); } `}},fB=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,f=a*r,m=gn(),A=h==="channelsLast",g=A?0:1,y=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=` blockIndex = rc.y + ${_}; pos = rc.x + ${b}; if(blockIndex < ${e[1]} && pos < ${e[0]}) { offsetY = int(blockIndex / (${l})) * ${i} - ${p}; d0 = offsetY + ${c} * (pos / ${f}); if(d0 < ${t[g]} && d0 >= 0) { offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.); d1 = offsetX + ${u} * (int(mod(float(pos), ${f}.) / ${a}.)); if(d1 < ${t[y]} && d1 >= 0) { ch = int(mod(float(pos), ${a}.)); if (${A}) { innerDims = vec2(d1, ch); result[${b*2+_}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${b*2+_}] = getChannel( getA(ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec2 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${w} ${m.output} = result; } `}};function I_({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",f=!1,m=!1,A,g=[],y=(h===1||d===1)&&c>p_,w=l[2]%2!=0&&!!u.isPacked;if(y||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=we({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=we({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Sp({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=we({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),g.push(_),g.push(x),g.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={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(mc(u.shape,_.shape),()=>`packed reshape ${u.shape} to ${_.shape} isn't free`);let N=we({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});g.push(N);let T=Sp({a:_,b:N,backend:r,transposeA:f,transposeB:m,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=Wn({inputs:{x:T},backend:r}),A.shape=n.outShape,g.push(T)}for(let b of g)r.disposeIntermediateTensorInfo(b);return A}function N_({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,f=p==="channelsLast",m=l*u*c,A=d*h,g=[m,A],y=!0,w=!1,b=[],_=we({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=we({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let N=new fB(g,_.shape,n),T=r.runWebGLProgram(N,[_],"float32"),E=we({inputs:{x:T},backend:r,attrs:{shape:[1,g[0],g[1]]}});b.push(T),b.push(E);let M=a!=null,z=s!=null,B=o==="leakyrelu",V=o?Ip(o,!0):null,U=new o_(E.shape,x.shape,[1,A,n.outChannels],y,w,M,V,z,B),j=[E,x];if(a&&j.push(a),z&&j.push(s),B){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));j.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,j,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=we({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function mB(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=R.convertConv2DDataFormat(l),d=R.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=I_({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=N_({x:a,filter:s,convInfo:d,backend:n});else{let m=new k_(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=we({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var AB={kernelName:ws,backendName:"webgl",kernelFunc:mB},gB=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); } `}},yB=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); } `}},xB=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 >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},wB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${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 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function bB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new gB(d);return n.runWebGLProgram(p,[a,s],"float32")}var _B={kernelName:Vh,backendName:"webgl",kernelFunc:bB};function vB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(u),d=R.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new yB(d);return n.runWebGLProgram(p,[a,s],"float32")}var kB={kernelName:bs,backendName:"webgl",kernelFunc:vB};function IB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new pB(u);return n.runWebGLProgram(c,[a,s],"float32")}var NB={kernelName:_u,backendName:"webgl",kernelFunc:IB};function SB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=R.computeConv3DInfo(a.shape,l,i,1,o),c=new xB(u);return n.runWebGLProgram(c,[a,s],"float32")}var TB={kernelName:Uh,backendName:"webgl",kernelFunc:SB};function EB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=R.computeConv3DInfo(l,s.shape,o,1,i),c=new wB(u);return n.runWebGLProgram(c,[a,s],"float32")}var CB={kernelName:jh,backendName:"webgl",kernelFunc:EB},RB=i_+` return cos(x); `,FB=Qe({opSnippet:RB}),MB={kernelName:_s,backendName:"webgl",kernelFunc:FB},$B=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,DB=Qe({opSnippet:$B}),OB={kernelName:mo,backendName:"webgl",kernelFunc:DB},zB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,g]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[y,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=` const float height_ratio = float(${m}); const float width_ratio = float(${y}); 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 = ${w}; float in_y = ${g}; if( in_y < 0.0 || in_y > ${p} ) { setOutput(float(${a})); return; } float in_x = ${b}; if( in_x < 0.0 || in_x > ${f} ) { setOutput(float(${a})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${d} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}},PB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new zB(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},LB={kernelName:Ao,backendName:"webgl",kernelFunc:PB},E_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${S_(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() { ${dt(r)} coords = getOutputCoords(); int end = ${T_(r,"coords")}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${o}; ${T_(r,"coords")} = idx; val += getX(${S_(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 S_(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 ${e} is not yet supported`)}function T_(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function WB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=R.getAxesPermutation([s],l),c=a;u!=null&&(c=En({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=Wn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new E_(c.shape,!1,o),A=m.getCustomSetupFunc(f),g=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(g)}if(i){let f=new E_(c.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=R.getUndoAxesPermutation(u),m=En({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}return p}var BB={kernelName:vs,backendName:"webgl",kernelFunc:WB};function VB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=Hb(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=gP(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var UB={kernelName:Hh,backendName:"webgl",kernelFunc:VB},jB=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 HB(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),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new jB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var GB={kernelName:go,backendName:"webgl",kernelFunc:HB},C_=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,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",g="";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} } `,g="result = activation(result);");let y=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 / ${m}; int q = d2 - d1 * ${m}; 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 < ${f}; 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; ${y} ${g} setOutput(result); } `}},R_=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,f=e.filterWidth,m=f,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=R.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 R_(h):d=new C_(h),n.runWebGLProgram(d,[a,s],"float32")}var XB={kernelName:ks,backendName:"webgl",kernelFunc:qB},KB=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 >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},ZB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${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 * ${o} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function YB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=R.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new KB(h);return n.runWebGLProgram(d,[a,s],"float32")}var JB={kernelName:Gh,backendName:"webgl",kernelFunc:YB};function QB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=R.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new ZB(h);return n.runWebGLProgram(d,[a,s],"float32")}var eV={kernelName:qh,backendName:"webgl",kernelFunc:QB},tV=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function nV(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=we({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new tV(s),l=n.runWebGLProgram(o,[i],i.dtype),u=we({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var rV={kernelName:Xh,backendName:"webgl",kernelFunc:nV},aV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=` const ivec2 strides = ivec2(${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 sV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new aV(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=we({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var iV={kernelName:vu,backendName:"webgl",kernelFunc:sV},oV="return (x >= 0.0) ? x : (exp(x) - 1.0);",lV=` 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; `,uV=Qe({opSnippet:oV,packedOpSnippet:lV}),cV={kernelName:yo,backendName:"webgl",kernelFunc:uV},hV="return (b >= 1.0) ? a : a * (b + 1.0);",dV=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,pV=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wc(dV,r.shape,a.shape):new Wl(hV,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},fV={kernelName:Yh,backendName:"webgl",kernelFunc:pV},mV=` return vec4(equal(a, b)); `,AV="return float(a == b);",gV=on({opSnippet:AV,packedOpSnippet:mV,dtype:"bool"}),yV={kernelName:wo,backendName:"webgl",kernelFunc:gV},xV=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${R.ERF_P}; float a1 = ${R.ERF_A1}; float a2 = ${R.ERF_A2}; float a3 = ${R.ERF_A3}; float a4 = ${R.ERF_A4}; float a5 = ${R.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,wV=Qe({opSnippet:xV}),bV={kernelName:xo,backendName:"webgl",kernelFunc:wV},F_="return exp(x);",M_=Qe({opSnippet:F_,packedOpSnippet:F_,cpuKernelImpl:wP}),_V={kernelName:Ns,backendName:"webgl",kernelFunc:M_};function bA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),we({inputs:{x:s},backend:r,attrs:{shape:o}})}var vV={kernelName:bo,backendName:"webgl",kernelFunc:bA},$_="return exp(x) - 1.0;",kV=Qe({opSnippet:$_,packedOpSnippet:$_,cpuKernelImpl:bP}),IV={kernelName:_o,backendName:"webgl",kernelFunc:kV},D_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${a}; 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NAN : result.a; return result; `,CU=Qe({opSnippet:TU,packedOpSnippet:EU,cpuKernelImpl:SP}),RU={kernelName:Ms,backendName:"webgl",kernelFunc:CU},FU="return log(1.0 + x);",MU=Qe({opSnippet:FU}),$U={kernelName:Fo,backendName:"webgl",kernelFunc:MU},DU="return float(a >= 1.0 && b >= 1.0);",OU=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,zU=on({opSnippet:DU,packedOpSnippet:OU,dtype:"bool"}),PU={kernelName:Mo,backendName:"webgl",kernelFunc:zU},LU="return float(!(x >= 1.0));",WU=Qe({opSnippet:LU}),BU={kernelName:Iu,backendName:"webgl",kernelFunc:WU},VU="return float(a >= 1.0 || b >= 1.0);",UU=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,jU=on({opSnippet:VU,packedOpSnippet:UU,dtype:"bool"}),HU={kernelName:Nu,backendName:"webgl",kernelFunc:jU},GU=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 * ${o}; setOutput(val); } `}},qU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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); } `}},XU=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 qU(a.shape,s,i,o,l):new GU(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},KU={kernelName:Su,backendName:"webgl",kernelFunc:XU},ZU=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); } `}},YU=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 ZU(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},JU={kernelName:nd,backendName:"webgl",kernelFunc:YU};function QU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=we({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ei(i,e.dtype,"max",r),l=we({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function P_(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=R.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let y=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Wn({inputs:{x:a},backend:n});let h=new bc(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var ij={kernelName:Os,backendName:"webgl",kernelFunc:sj};function oj(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=R.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new yA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var lj={kernelName:Tu,backendName:"webgl",kernelFunc:oj},uj=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 * ${s} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},cj=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${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 hj(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=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new yA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new cj(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var dj={kernelName:ad,backendName:"webgl",kernelFunc:hj};function pj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Fl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,u,1,c,h),p=!0,f=new bc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new uj(d),g=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),g}var fj={kernelName:rd,backendName:"webgl",kernelFunc:pj};function mj(e,t,n,r){let a=new bc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new bc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var Aj={kernelName:sd,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(R.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=R.computePool2DInfo(r.shape,a,s,u,i),[h,d]=mj(r,o,c,l);return[h,d]}};function gj(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=we({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ei(i,"float32","mean",r),l=we({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var yj={kernelName:zs,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=R.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,b=new Array(o);for(let N=0;Nu[0]+e[c]+u[1]);let r=e.length,a=dt(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})); } `}},Nj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=dt(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=yn("rc",r),l=yn("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.); ${d} setOutput(result); } `}},Sj=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Nj(r.shape,a,s):new Ij(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},Tj={kernelName:Eu,backendName:"webgl",kernelFunc:Sj},Ej=`if (b == 0.0) return NAN; return mod(a, b);`,Cj=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+kp+` return result; `,Rj=on({opSnippet:Ej,packedOpSnippet:Cj}),Fj={kernelName:$o,backendName:"webgl",kernelFunc:Rj},Mj=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)}}},$j=` if (a == b) { return 1.0; 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Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=Uj(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Hj={kernelName:zo,backendName:"webgl",kernelFunc:jj},Gj=Hr.nonMaxSuppressionV4Impl;function qj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=Gj(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var Xj={kernelName:Po,backendName:"webgl",kernelFunc:qj},Kj=Hr.nonMaxSuppressionV5Impl;function Zj(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=u,{selectedIndices:A,selectedScores:g}=Kj(c,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([g.length],"float32",new Float32Array(g))]}var Yj={kernelName:Lo,backendName:"webgl",kernelFunc:Zj},Jj=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${r}), float(${n}), float(index == coords.y))); } `}},Qj=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 Jj(l,s,i,o),c=we({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=we({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},eH={kernelName:Bs,backendName:"webgl",kernelFunc:Qj};function Rp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=vc({inputs:{input:r},backend:n}),s=Rp({inputs:{x:a},backend:n}),i=Cp({inputs:{input:r},backend:n}),o=Rp({inputs:{x:i},backend:n}),l=Xa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return _A({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var tH={kernelName:rl,backendName:"webgl",kernelFunc:Rp};function j_(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=vc({inputs:{input:r},backend:n}),s=j_({inputs:{x:a},backend:n}),i=Cp({inputs:{input:r},backend:n}),o=Rp({inputs:{x:i},backend:n}),l=Xa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return _A({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var nH={kernelName:Wo,backendName:"webgl",kernelFunc:j_};function rH(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return bA({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=bA({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=v_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var aH={kernelName:Bo,backendName:"webgl",kernelFunc:rH},sH=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=dt(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}; uniform float value; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(value); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${a} start = ${a}(${s}); ${a} end = ${a}(${i}); uniform float value; void main() { ${a} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { ${a} coords = outC - start; setOutput(getX(${o})); } } `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},iH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=dt(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=yn("rc",r),l=yn("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 f=0,m=r===1?2:4;f{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},H_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iH(a.shape,s,i):new sH(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},oH={kernelName:Vs,backendName:"webgl",kernelFunc:H_},lH=` 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); `,uH=` // 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)); `+kp+` return result; `,cH=on({opSnippet:lH,packedOpSnippet:uH}),hH={kernelName:Us,backendName:"webgl",kernelFunc:cH};function dH(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=R.getAxesPermutation(c,o),d=a;h!=null&&(d=En({inputs:{x:a},backend:n,attrs:{perm:h}}),c=R.getInnerMostAxes(c.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:g}=MP(d.shape,d.dtype,f,c);p=n.makeTensorInfo(A,g,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(m),g=we({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),y=yd(a.dtype),w=Ei(g,y,"prod",n);p=we({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(g),l.push(w)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,u);p=we({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var pH={kernelName:Vo,backendName:"webgl",kernelFunc:dH},G_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=$P(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},fH={kernelName:Cu,backendName:"webgl",kernelFunc:G_},mH="return 1.0 / x;",AH=Qe({opSnippet:mH}),gH={kernelName:Uo,backendName:"webgl",kernelFunc:AH},yH=Ir+` return (x < 0.0) ? 0.0 : x; `,xH=` 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; `,wH=Qe({opSnippet:yH,packedOpSnippet:xH}),bH={kernelName:Hs,backendName:"webgl",kernelFunc:wH},_H=Ir+` return (x < 0.0) ? 0.0 : min(6.0, x); `,vH=` 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; `,kH=Qe({opSnippet:_H,packedOpSnippet:vH}),IH={kernelName:qs,backendName:"webgl",kernelFunc:kH},NH=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); } `}},SH=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 TH(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 SH(a.shape,l,u,s,i):new NH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var EH={kernelName:Gs,backendName:"webgl",kernelFunc:TH},CH=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,f=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(${f}); // 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 RH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new CH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var FH={kernelName:ud,backendName:"webgl",kernelFunc:RH},MH=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 $H(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new MH(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var DH={kernelName:Ru,backendName:"webgl",kernelFunc:$H},OH=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,f=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(${p}); const int winWidth = int(${f}); // 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), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function zH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new OH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var PH={kernelName:ld,backendName:"webgl",kernelFunc:zH},LH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 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n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(m),y}var JH={kernelName:Ho,backendName:"webgl",kernelFunc:YH},QH=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 eG(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new QH(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],lr(a.dtype,s.dtype))}var tG={kernelName:Go,backendName:"webgl",kernelFunc:eG},nG=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${R.SELU_SCALEALPHA}; float scale = ${R.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); 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kG(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=R.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 f=_c({inputs:{x:a},backend:n,attrs:{begin:c,size:p}});return c[o]+=d,f})}var IG={kernelName:Jo,backendName:"webgl",kernelFunc:kG},NG="return sqrt(x);",SG=Qe({opSnippet:NG}),TG={kernelName:Qs,backendName:"webgl",kernelFunc:SG},EG="return x * x;",CG=Qe({opSnippet:EG}),RG={kernelName:Mu,backendName:"webgl",kernelFunc:CG},X_="return (a - b) * (a - b);",FG=on({opSnippet:X_,packedOpSnippet:X_}),MG={kernelName:ni,backendName:"webgl",kernelFunc:FG};function $G({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=Ir+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new qa(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var DG={kernelName:$a,backendName:"webgl",kernelFunc:$G},OG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=dt(n.length),s=dt(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 zG(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,$begin:f,$strides:m,size:A,newShape:g,outShape:y}=pn.sliceInfo(a.shape,s,i,o,l,u,c,h,d),w=we({inputs:{x:a},backend:n,attrs:{shape:g}}),b;if(p){let x=_c({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=we({inputs:{x},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(x)}else if(y.some(x=>x===0))b=n.makeTensorInfo(y,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let 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GG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var qG={kernelName:Ma,backendName:"webgl",kernelFunc:K_};function XG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,u]=WP(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 KG={kernelName:tl,backendName:"webgl",kernelFunc:XG},ZG=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${o} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * float(int(float(-inCoord / sz2))) + inCoord; } inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0; } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; inCoord -= sz2 * float(int(float(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } else if (${o} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord += len * (float(int(float(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord -= len * float(int(float(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (${o} == 4) { return clamp(outCoord, 0.0, len - 1.0); } else { return outCoord; } } float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${a}); } return outputValue; } void main() { ivec4 coords = getOutputCoords(); float outputValue; int batch = coords[0]; int x = coords[2]; int y = coords[1]; int channel = coords[3]; float xf = float(x); float yf = float(y); float a1 = getTransforms(batch, 0); float a2 = getTransforms(batch, 1); float a3 = getTransforms(batch, 2); float b1 = getTransforms(batch, 3); float b2 = getTransforms(batch, 4); float b3 = getTransforms(batch, 5); float c1 = getTransforms(batch, 6); float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = float(${a}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${t})); float mapY = mapCoord(inY, float(${e})); if (${i} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { float yFloor = floor(mapY); float xFloor = floor(mapX); float yCeil = yFloor + 1.0; float xCeil = xFloor + 1.0; float valueYFloor = (xCeil - mapX) * readWithFillValue(batch, int(yFloor), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yFloor), int(xCeil), channel); float valueYCeil = (xCeil - mapX) * readWithFillValue(batch, int(yCeil), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yCeil), int(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutput(outputValue); } `}};function YG(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=r,[c,h,d,p]=a.shape,[f,m]=u!=null?u:[h,d],A=[c,f,m,p],g=new ZG(h,d,i,o,l,A);return n.runWebGLProgram(g,[a,s],"float32")}var JG={kernelName:hd,backendName:"webgl",kernelFunc:YG};function QG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;Fl(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}=BP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([u.length],"int32",u)]}var eq={kernelName:dd,backendName:"webgl",kernelFunc:QG};function tq(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 m=0;mn.disposeIntermediateTensorInfo(m)),f}var nq={kernelName:nl,backendName:"webgl",kernelFunc:tq},rq=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 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AK={kernelName:Lo,backendName:"wasm",setupFunc:fK,kernelFunc:mK},gK=!1,yK=xn(Oo,gK,"bool"),k3;function xK(e){k3=e.wasm.cwrap(Bs,null,["number","number","number","number","number"])}function wK(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 k3(c,s,i,o,u),l}var bK={kernelName:Bs,backendName:"wasm",setupFunc:xK,kernelFunc:wK};function _K(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var vK={kernelName:Wo,backendName:"wasm",kernelFunc:_K};function kK(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return kA({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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Add some layers first.");this.model=new Aa({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 Tr("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 Tr("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 Tr("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 Tr("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 W("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 Kl))throw new Pe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=Rr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new W("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 W("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}}};Kl.className="Sequential";ae.registerClass(Kl);function tne(e){return new Aa(e)}function nne(e){return new Kl(e)}function rne(e,t){return t==null&&(t={}),Qte(e,t)}function y7(e){return S7(e)}function ane(e,t){mr.registerCallbackConstructor(e,t)}var Vn=class extends ae.Serializable{getConfig(){return{}}},tv=class extends Vn{apply(e,t=1){return OQ(e,t)}};tv.className="elu";ae.registerClass(tv);var nv=class extends Vn{apply(e){return Vd(e)}};nv.className="selu";ae.registerClass(nv);var rv=class extends Vn{apply(e){return jr(e)}};rv.className="relu";ae.registerClass(rv);var av=class extends Vn{apply(e){return L(()=>vl(6,jr(e)))}};av.className="relu6";ae.registerClass(av);var sv=class extends Vn{apply(e){return e}};sv.className="linear";ae.registerClass(sv);var iv=class extends Vn{apply(e){return Dn(e)}};iv.className="sigmoid";ae.registerClass(iv);var ov=class extends Vn{apply(e){return PQ(e)}};ov.className="hardSigmoid";ae.registerClass(ov);var lv=class extends Vn{apply(e){return bl(e)}};lv.className="softplus";ae.registerClass(lv);var uv=class extends Vn{apply(e){return zQ(e)}};uv.className="softsign";ae.registerClass(uv);var cv=class extends Vn{apply(e){return Al(e)}};cv.className="tanh";ae.registerClass(cv);var pg=class extends Vn{apply(e,t=-1){return oc(e,t)}};pg.className="softmax";ae.registerClass(pg);var hv=class extends Vn{apply(e,t=-1){return Dd(e,t)}};hv.className="logSoftmax";ae.registerClass(hv);var dv=class extends Vn{apply(e,t=1){return L(()=>Dn(e.mul(t)).mul(e))}};dv.className="swish";ae.registerClass(dv);function Qa(e){return e.getClassName()}function fg(e,t={}){return Tc(e,ae.SerializationMap.getMap().classNameMap,t,"activation")}function es(e){if(e==null){let t={};return t.className="linear",t.config={},fg(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},fg(t)}else return e instanceof Vn?e:fg(e)}function mg(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 pv=class extends ae.Serializable{},Wc=class extends pv{constructor(e){super();mg(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 L(()=>{let t=Ot([1]);return this.hasL1&&(t=ie(t,Fe(O(this.l1,Vt(e))))),this.hasL2&&(t=ie(t,Fe(O(this.l2,Mc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Wc.className="L1L2";ae.registerClass(Wc);function sne(e){return mg(e),new Wc({l1:e!=null?e.l1:null,l2:0})}function ine(e){return mg(e),new Wc({l2:e!=null?e.l2:null,l1:0})}var fv={l1l2:"L1L2"};function ft(e){return EA(e)}function mv(e,t={}){return Tc(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in fv?fv[e]:e,config:{}};return mv(t)}else return e instanceof pv?e:mv(e)}var Ag=class extends Je{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Be(e);let n=jr(e);return this.maxValue!=null&&(n=Nn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Ag.className="ReLU";ae.registerClass(Ag);var gg=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=Be(e);return ec(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};gg.className="LeakyReLU";ae.registerClass(gg);var yg=class extends Je{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=bt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=qt(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 W(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=pt(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(Mt(t),t==="channelsFirst"?it(e,[0,2,3,1]):e))}function Av(e,t){return L(()=>(Mt(t),t==="channelsFirst"?it(e,[0,2,3,4,1]):e))}function one(e,t,n,r=1,a="valid",s,i=1){return L(()=>{if(s==null&&(s=Sr()),Mt(s),e.shape.length!==3)throw new W(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new W(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new W(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=it(e,[0,2,1])),a==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Sd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Zr(o,n)),o})}function gv(e,t,n,r=[1,1],a="valid",s,i,o=null){return L(()=>{if(s==null&&(s=Sr()),Mt(s),e.rank!==3&&e.rank!==4)throw new W(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new W(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=_g(e,s);if(a==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ha.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=it(l,[0,3,1,2])),l})}function lne(e,t,n,r=[1,1,1],a="valid",s,i){return L(()=>{if(s==null&&(s=Sr()),Mt(s),e.rank!==4&&e.rank!==5)throw new W(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new W(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=Av(e,s);if(a==="causal")throw new Pe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=hm(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Zr(o,n)),s==="channelsFirst"&&(o=it(o,[0,4,1,2,3])),o})}var vg=class extends Je{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",vg.verifyArgs(t),this.rank=e,Jt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Pe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Zl(t.kernelSize,e,"kernelSize"),this.strides=Zl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,nr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Mt(this.dataFormat),this.activation=es(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=bt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=qt(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=Zl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new W(`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 W(`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 W(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Xr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!RA(e.kernelSize,"number",1,3))throw new W(`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:Qa(this.activation),useBias:this.useBias,biasInitializer:Et(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Gt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Bc=class extends vg{constructor(e,t){super(e,t);this.kernel=null,Bc.verifyArgs(t),this.filters=t.filters,Jt(this.filters,"filters"),this.kernelInitializer=bt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=qt(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new W(`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 L(()=>{e=Be(e);let n,r=this.bias==null?null:this.bias.read(),a=n7(this.activation.getClassName());if(a!=null&&this.rank===2)n=gv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=one(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=gv(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=lne(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Pe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=pt(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)}`)}},Vc=class extends Bc{constructor(e){super(2,e);Vc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!RA(e.kernelSize,"number",1,2))throw new W(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Vc.className="Conv2D";ae.registerClass(Vc);var o0=class extends Bc{constructor(e){super(3,e);o0.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 W(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};o0.className="Conv3D";ae.registerClass(o0);var kg=class extends Vc{constructor(e){super(e);if(this.inputSpec=[new Qt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new W(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=pt(e),e.length!==4)throw new W("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new W("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Qt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return L(()=>{let n=Be(e);if(n.shape.length!==4)throw new W(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=i0(o,h,u,this.padding),f=i0(l,d,c,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=it(n,[0,2,3,1]));let A=Td(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=it(A,[0,3,1,2])),this.bias!=null&&(A=Zr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=pt(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]=i0(t[r],o,s,this.padding),t[a]=i0(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};kg.className="Conv2DTranspose";ae.registerClass(kg);var yv=class extends Bc{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 W("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new W("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 W(`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=bt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=qt(t.depthwiseConstraint),this.pointwiseInitializer=bt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=qt(t.pointwiseConstraint)}build(e){if(e=pt(e),e.length{e=Be(e);let n;if(this.rank===1)throw new Pe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=it(e,[0,2,3,1])),n=Em(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Zr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=it(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=Et(this.depthwiseInitializer),e.pointwiseInitializer=Et(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Gt(this.depthwiseConstraint),e.pointwiseConstraint=Gt(this.pointwiseConstraint),e}};yv.className="SeparableConv";var Ig=class extends yv{constructor(e){super(2,e)}};Ig.className="SeparableConv2D";ae.registerClass(Ig);var l0=class extends Bc{constructor(e){super(1,e);l0.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"&&!RA(e.kernelSize,"number",1,1))throw new W(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};l0.className="Conv1D";ae.registerClass(l0);var Ng=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 L(()=>{if(e=Be(e),this.dataFormat==="channelsLast"){let n=Pp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Pp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Pp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Pp(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}};Ng.className="Cropping2D";ae.registerClass(Ng);var Sg=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,Mt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,EQ(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 L(()=>{let n=Be(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=it(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 it(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}};Sg.className="UpSampling2D";ae.registerClass(Sg);function une(e,t,n=[1,1],r="valid",a,s){return L(()=>{a==null&&(a=Sr()),Mt(a);let i=_g(e,a);if(e.rank!==4)throw new W(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new W(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=yl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=it(i,[0,3,1,2])),i})}var Tg=class extends vg{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=bt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=qt(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=pt(e),e.length<4)throw new W(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new W(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 L(()=>{e=Be(e);let n=une(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Zr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=pt(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=Fr(t,this.kernelSize[0],this.padding,this.strides[0]),s=Fr(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=Et(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Gt(this.depthwiseRegularizer),e}};Tg.className="DepthwiseConv2D";ae.registerClass(Tg);function xv(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new W("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 wv(e,t,n,r=!1,a,s,i=!1,o=!1){return L(()=>{let l=t.shape.length;if(l<3)throw new W(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Er(2,l));if(t=it(t,u),s!=null)throw new Pe("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=fn(a,-1)),a=it(a,u)),r&&(t=Pn(t,0),a!=null&&(a=Pn(a,0)));let c=[],h,d=n,p=t.shape[0],f=hr(t),m;a!=null&&(m=hr(a));for(let g=0;ge(y,d));if(a==null)h=w[0],d=w[1];else{let b=L(()=>{let _=m[g],x=zn(_).sub(_),N=w[0].mul(_).add(d[0].mul(x)),T=d.map((E,M)=>w[1][M].mul(_).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=mn(c,1)),[h,A,d]})}var Yr=class extends Je{constructor(e){super(e);let t;if(e.cell==null)throw new W("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new u0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new W("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 Qt({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 Er(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){YA(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 L(()=>{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 W(`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 Qt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){L(()=>{if(!this.stateful)throw new fa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new W("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=>Ot([n,r])):this.states_=[Ot([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ot([n,r])):this.states_[0]=Ot([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new W(`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()):Re(this.states_);for(let r=0;rZt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=xv(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 Qt({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 Cr){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 L(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Be(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 W(`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=wv((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.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 L(()=>{let t=Ot(e.shape);return t=Fe(t,[1,2]),t=Fc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?zA(t,[1,n]):t):this.cell.stateSize>1?[zA(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()===Yr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Rr(r,n);return new e(Object.assign(t,{cell:a}))}};Yr.className="RNN";ae.registerClass(Yr);var Dc=class extends Je{},c0=class extends Dc{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=es(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=qt(e.kernelConstraint),this.recurrentConstraint=qt(e.recurrentConstraint),this.biasConstraint=qt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=pt(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 L(()=>{if(e=e,e.length!==2)throw new W(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0zn(e),rate:this.dropout,training:r})),0zn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Kr(O(e,s),this.kernel.read()):a=Kr(e,this.kernel.read()),this.bias!=null&&(a=Zr(a,this.bias.read())),i!=null&&(n=O(n,i));let o=ie(a,Kr(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:Qa(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Gt(this.kernelConstraint),recurrentConstraint:Gt(this.recurrentConstraint),biasConstraint:Gt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};c0.className="SimpleRNNCell";ae.registerClass(c0);var Eg=class extends Yr{constructor(e){e.cell=new c0(e),super(e)}call(e,t){return L(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(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)}};Eg.className="SimpleRNN";ae.registerClass(Eg);var h0=class extends Dc{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 W("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Jt(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=qt(e.kernelConstraint),this.recurrentConstraint=qt(e.recurrentConstraint),this.biasConstraint=qt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=pt(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 L(()=>{if(e=e,e.length!==2)throw new W(`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],0zn(e),rate:this.dropout,training:n,count:3})),0zn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(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)}};Cg.className="GRU";ae.registerClass(Cg);var Uc=class extends Dc{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=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=bt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=qt(e.kernelConstraint),this.recurrentConstraint=qt(e.recurrentConstraint),this.biasConstraint=qt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([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=pt(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 fr{apply(i,o){let l=a.apply([s]),u=new Wp().apply([s]),c=a.apply([s*2]);return d7(d7(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 L(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new W(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0zn(e),rate:this.dropout,training:n,count:4})),0zn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Rg.className="LSTM";ae.registerClass(Rg);var u0=class extends Dc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return L(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i{Mi(`RNNCell_${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(Rr(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 JA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;sf7(t(),n),i=()=>$c(s,t,r);return!a||a<=1?Zt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Zt(o.clone()))}var cne=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&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new W("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 L(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ot(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){L(()=>{if(!this.stateful)throw new fa("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 W("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(()=>Ot(a)):this.states_=[Ot(a)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ot(a)):this.states_[0]=Ot(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new W(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). 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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 fr{apply(c,h){let d=l.apply([u]),p=Ur([u]),f=l.apply([u*2]);return LA([d,p,f])}},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 L(()=>{if(e.length!==3)throw new W(`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;0zn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,se,ne)=>!se||!se[ne]?Y:O(se[ne],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0zn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),g=l(a,p,3),y=3,[w,b,_,x]=jt(this.kernel.read(),i,y),[N,T,E,M]=this.useBias?jt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,w,N,this.padding),c=this.inputConv(c,b,T,this.padding),h=this.inputConv(h,_,E,this.padding),d=this.inputConv(d,x,M,this.padding);let[z,B,V,U]=jt(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,z),m=this.recurrentConv(m,B),A=this.recurrentConv(A,V),g=this.recurrentConv(g,U);let j=this.recurrentActivation.apply(ie(u,f)),X=this.recurrentActivation.apply(ie(c,m)),G=ie(O(X,s),O(j,this.activation.apply(ie(h,A)))),ee=O(this.recurrentActivation.apply(ie(d,g)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=cne(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=la(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Zr(a,n,this.dataFormat):a}recurrentConv(e,t){return la(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};d0.className="ConvLSTM2DCell";ae.registerClass(d0);var Fg=class extends bv{constructor(e){let t=new d0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Fg.className="ConvLSTM2D";ae.registerClass(Fg);var p0=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=Be(e);if(0f7(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()}};p0.className="Dropout";ae.registerClass(p0);var Mg=class extends p0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Mg.className="SpatialDropout1D";ae.registerClass(Mg);var $g=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=es(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=bt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=bt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=qt(e.kernelConstraint),this.biasConstraint=qt(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=pt(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=pt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return L(()=>{this.invokeCallHook(e,t);let n=Be(e),r=n7(this.activation.getClassName()),a;return r!=null?a=Kr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Kr(n,this.kernel.read()),this.bias!=null&&(a=Zr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Qa(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Gt(this.kernelConstraint),biasConstraint:Gt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};$g.className="Dense";ae.registerClass($g);var Dg=class extends Je{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=pt(e);for(let t of e.slice(1))if(t==null)throw new W(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;aa.length);e.indexOf(null)===-1&&Ka(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return L(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Ya(r);for(let s of e){let i=s.rank;for(let o=0;o1){let u=Er(1,l).concat([0]);n.push(it(o,u)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=it(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(Er(0,i-1));s=it(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r{if(t==null)return null;if(!Array.isArray(t))throw new W("`mask` should be an Array");if(!Array.isArray(e))throw new W("`inputs` should be an Array");if(t.length!==e.length)throw new W(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:fn(r,0));let n=t[0];for(let r=1;r{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new W("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return L(()=>LA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new W("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new W("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new W("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new W(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return L(()=>{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 Pe("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 Pe("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 L(()=>{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 Pe("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 W(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new W(`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)=>jc(a,e[s].shape.length)):r=[jc(this.axes,t.shape.length),jc(this.axes,n.shape.length)],this.normalize&&(t=Jp(t,r[0]),n=Jp(n,r[1])),hne(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[jc(this.axes,e.length),jc(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 Pe("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}};Xg.className="Dot";ae.registerClass(Xg);var Kg=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 L(()=>{this.invokeCallHook(e,t);let n=Be(e);return $c(()=>Lp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Kg.className="GaussianNoise";ae.registerClass(Kg);var Zg=class extends Je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return L(()=>{this.invokeCallHook(e,t);let n=Be(e);return this.rate>0&&this.rate<1?$c(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Lp(n.shape,1,r))},()=>n,t.training||!1):n})}};Zg.className="GaussianDropout";ae.registerClass(Zg);var Yg=class extends Je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Be(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 L(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return $c(()=>{let r=Be(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Ua(kl(n),this.rate);o=Rc(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)},()=>Be(e),t.training||!1)}return e})}};Yg.className="AlphaDropout";ae.registerClass(Yg);function Hc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=dx(e,t,n,r,a,s);else if(e.rank===3)i=px(e,t,n,r,a,s);else if(e.rank===4)i=fx(e,t,n,r,a,s);else throw new Pe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function dne(e,t,n,r,a=.001){return L(()=>{let s=zd(e,r),i=s.mean,o=s.variance;return[Hc(e,i,o,n,t,a),i,o]})}function pne(e,t,n,r,a=.001){return L(()=>{let s=zd(e,r),i=s.mean,o=s.variance,l=[];for(let p of Er(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[Hc(e,u,c,d,h,a),i,o]})}function fne(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),Er(0,e.rank-1))?dne(e,t,n,r,a):pne(e,t,n,r,a)}var Jg=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=bt(e.betaInitializer||"zeros"),this.gammaInitializer=bt(e.gammaInitializer||"ones"),this.movingMeanInitializer=bt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=bt(e.movingVarianceInitializer||"ones"),this.betaConstraint=qt(e.betaConstraint),this.gammaConstraint=qt(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=pt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new W(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Qt({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 L(()=>{let n=t.training==null?!1:t.training,r=Be(e),a=r.shape,s=a.length,i=Er(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Ci(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,Er(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),g=this.movingVariance.read().reshape(l),y=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return Hc(r,A,g,y,w,this.epsilon)}else return Hc(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,f]=fne(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,g,y)=>{L(()=>{let w=1-y,b=A.read(),_=b.sub(g).mul(w);A.write(b.sub(_))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Et(this.betaInitializer),gammaInitializer:Et(this.gammaInitializer),movingMeanInitializer:Et(this.movingMeanInitializer),movingVarianceInitializer:Et(this.movingVarianceInitializer),betaRegularizer:ft(this.betaRegularizer),gammaRegularizer:ft(this.gammaRegularizer),betaConstraint:Gt(this.betaConstraint),gammaConstraint:Gt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Jg.className="BatchNormalization";ae.registerClass(Jg);var Qg=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=bt(e.betaInitializer||"zeros"),this.gammaInitializer=bt(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=pt(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!==Ka(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=Be(e),r=n.shape,a=r.length;return L(()=>{let s=!0,{mean:i,variance:o}=zd(n,this.axis,s),l=Ci(1,a);for(let f of this.axis)l[f]=r[f];let u=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,c=u(this.gamma.read()),h=u(this.beta.read()),d=[],p=[];for(let f=0;f{if(e.rank!==4)throw new W(`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 W("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Sr()),n!=="channelsLast"&&n!=="channelsFirst")throw new W(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],ua(e,r)})}var ey=class extends Je{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Sr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new W(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 W(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new W(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Qt({ndim:4})]}computeOutputShape(e){e=pt(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 L(()=>mne(Be(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ey.className="ZeroPadding2D";ae.registerClass(ey);function f0(e,t,n,r,a,s){return L(()=>{Mt(a),i7(s),nr(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=Sr()),s==null&&(s="max"),e=_g(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=nc(e,t,n,o):i=Ku(e,t,n,o),a==="channelsFirst"&&(i=it(i,[0,3,1,2])),i})}function _v(e,t,n,r,a,s){return L(()=>{Mt(a),i7(s),nr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=Sr()),s==null&&(s="max"),e=Av(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=_m(e,t,n,o):i=lm(e,t,n,o),a==="channelsFirst"&&(i=it(i,[0,4,1,2,3])),i})}var vv=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 W(`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 W(`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,nr(this.padding),this.inputSpec=[new Qt({ndim:3})]}computeOutputShape(e){e=pt(e);let t=Fr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return L(()=>{this.invokeCallHook(e,t),e=Fc(Be(e),2);let n=this.poolingFunction(Be(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ja(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},ty=class extends vv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Mt(a),nr(r),f0(e,t,n,r,a,"max")}};ty.className="MaxPooling1D";ae.registerClass(ty);var ny=class extends vv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Mt(a),nr(r),f0(e,t,n,r,a,"avg")}};ny.className="AveragePooling1D";ae.registerClass(ny);var kv=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 W(`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,Mt(this.dataFormat),nr(this.padding),this.inputSpec=[new Qt({ndim:4})]}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Fr(t,this.poolSize[0],this.padding,this.strides[0]),n=Fr(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 L(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(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}},ry=class extends kv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Mt(a),nr(r),f0(e,t,n,r,a,"max")}};ry.className="MaxPooling2D";ae.registerClass(ry);var ay=class extends kv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Mt(a),nr(r),f0(e,t,n,r,a,"avg")}};ay.className="AveragePooling2D";ae.registerClass(ay);var Iv=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 W(`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,Mt(this.dataFormat),nr(this.padding),this.inputSpec=[new Qt({ndim:5})]}computeOutputShape(e){e=pt(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=Fr(t,this.poolSize[0],this.padding,this.strides[0]),n=Fr(n,this.poolSize[1],this.padding,this.strides[1]),r=Fr(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 L(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(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}},sy=class extends Iv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Mt(a),nr(r),_v(e,t,n,r,a,"max")}};sy.className="MaxPooling3D";ae.registerClass(sy);var iy=class extends Iv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Mt(a),nr(r),_v(e,t,n,r,a,"avg")}};iy.className="AveragePooling3D";ae.registerClass(iy);var Nv=class extends Je{constructor(e){super(e);this.inputSpec=[new Qt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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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 u6(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>Un(d)[0]),c=[];r!=null&&(c=r.map(d=>Un(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((l6(d)||Ere(d)||Cre(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 Rre(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(c=>Un(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 Fre=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Mre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],$re=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function l6(e){return Fre.indexOf(e.op)>=0}function Ere(e){return Mre.indexOf(e.op)>=0}function Cre(e){return $re.indexOf(e.op)>=0}var Sy=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 Sy(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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Array(e),n=this.length();for(let r=0;rt===!0)}rowMajorBatch(e,t=!0){return new uae(this,e,t)}columnMajorBatch(e,t=!0,n=g6){return this.rowMajorBatch(e,t).map(r=>Zre(r,n))}concatenate(e,t){return new b6(w6([this,e]),t)}take(e){return e<0||e==null?this:new lae(this,e)}skip(e){return e<0||e==null?this:new oae(this,e)}prefetch(e){return new v6(this,e)}shuffle(e,t){return new fae(this,e,t)}serial(){return new iae(this)}},tae=class extends en{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:eae(e),done:!1}}},nae=class extends en{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: ${e.message}`,e}}},iae=class extends en{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},oae=class extends en{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},uae=class extends en{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}},cae=class extends en{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Re(e.value)}}},hae=class extends en{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_r.getTensorsInContainer(e.value),n=this.transform(e.value),r=_r.getTensorsInContainer(n);for(let a of t)_r.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},dae=class extends en{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},_6=class extends en{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_r.getTensorsInContainer(e.value),n=await this.transform(e.value),r=_r.getTensorsInContainer(n);for(let a of t)_r.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Cy=class extends en{constructor(){super();this.outputQueue=new Ty,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}}},pae=class extends Cy{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=_r.getTensorsInContainer(e.value),n=this.transform(e.value),r=_r.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)_r.isTensorInList(a,r)||a.dispose();return!0}},b6=class extends en{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}},ns;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ns||(ns={}));var aae=class extends en{constructor(e,t=ns.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 en?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await y6(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ns.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ns.SHORTEST:return{value:null,done:!0};case ns.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},v6=class extends en{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new x6(t)}summary(){return`${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()}},fae=class extends v6{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Xre.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}}},Yl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),jn(async()=>(await n.iterator()).columnMajorBatch(e,t,mae),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,jn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,jn(async()=>(await t.iterator()).filter(r=>L(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return jn(async()=>(await t.iterator()).map(n=>L(()=>e(n))),this.size)}mapAsync(e){let t=this;return jn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return jn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,jn(async()=>{let r=Ey(async()=>({value:await t.iterator(),done:!1}));return rae(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=qre.alea(t||v.now().toString());return jn(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,jn(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()}};Yl.MAX_BUFFER_SIZE=1e4;function jn(e,t=null){return new class extends Yl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Lre(e){return jn(async()=>w6(e),e.length)}function Wre(e){if(!Jl(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 y6(e,r=>{if(r instanceof Yl)return{value:r.iterator(),recurse:!1};if(Jl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return sae(n,ns.SHORTEST)},t)}function mae(e){if(e===null)return null;let t=e[0];return Jre(t)?{value:Aae(e),recurse:!1}:{value:null,recurse:!0}}function Aae(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof qe?mn(e):kr(e)}var d6=class extends Yl{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))}},x0='"',Xc=Symbol("out"),k6=Symbol("field"),w0=Symbol("quote"),Ry=Symbol("quoteafterquote"),I6=Symbol("quoteinquote"),p6=class extends Yl{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 d6(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 N6(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),kr(n,t)}},S6=class extends en{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=hn([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=Tn([s,a,o,i],[1,4])}else this.cropBox=Tn([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 S6(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=dl.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return L(()=>{let t=fn(xe(e,"float32"),0),n;n=Ke.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},T6=class{},E6=class extends en{split(e){return new gae(this,e)}},gae=class extends E6{constructor(e,t){super();this.upstream=e,this.impl=new yae(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},yae=class extends Cy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},wae=class extends en{decodeUTF8(){return new xae(this)}},xae=class extends E6{constructor(e){super();this.upstream=e,this.impl=new bae(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},bae=class extends Cy{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=ik();this.decoder=new t("utf8")}}summary(){return`${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}},C6=class extends wae{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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function vae(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=_ae(e));let a=await v.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new C6(s,t)}else throw new Error(a.statusText)}var _ae=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function R6(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var f6=class extends T6{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(R6(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new C6(this.input,this.options)}},m6=class extends 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r=$e(e,[0,1],[-1,2]),a=ie(r,t),s=$e(e,[0,3],[-1,2]),i=_e(s,n),o=_e(a,n),l=_e(i,2),u=be(o,l),c=ie(o,l),h=O(u,n),d=O(c,n);return gl([h,d],1)}var $6=class{constructor(t,n){this.model=t,this.anchorsData=Iae(t.inputs[0].shape[1]),this.anchors=Tn(this.anchorsData),this.inputSize=t.inputs[0].shape[2],this.config=n}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,a]=L(()=>{let d=t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(.5),p=this.model.predict(d),f;if(Array.isArray(p)){let y=p.sort((x,N)=>x.size-N.size),w=ot([y[0],y[2]],2),b=ot([y[1],y[3]],2);f=ot([b,w],1).squeeze(0)}else f=p.squeeze();let m=Sae(f,this.anchors,[this.inputSize,this.inputSize]),A=$e(f,[0,0],[-1,1]),g=Dn(A).squeeze();return[f,m,g]}),s=await Ke.nonMaxSuppressionAsync(r,a,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),i=s.arraySync();s.dispose();let l=i.map(h=>$e(r,[h,0],[1,-1])).map(h=>{let d=h.arraySync();return h.dispose(),d}),u=a.dataSync(),c=[];for(let h=0;hthis.config.face.detector.minConfidence){let f=Nae(l[h]),m=this.anchorsData[d],A=L(()=>$e(n,[d,M6-1],[1,-1]).squeeze().reshape([M6,-1]));c.push({box:f,landmarks:A,anchor:m,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),{boxes:c,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function D6(e){let t=await Ft(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new $6(t,e);return e.debug&&Me(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}function O6(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}function Kc(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function 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N=Ql({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],E=Ke.rotateWithOffset(t,c,0,T);h=k0(-c,N),n.face.mesh.enabled?u=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255):u=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{h=v0;let _=t.clone();n.face.mesh.enabled?u=eu({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.meshSize,this.meshSize]).div(255):u=eu({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:u};let[,d,p]=this.meshDetector.predict(u),f=d.dataSync()[0];if(f=I0.count?I0.symmetryLine:B6.symmetryLine;c=Fy(i.landmarks[_],i.landmarks[x]);let 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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 m4=[{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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A4=["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"],g4=["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 yr;async function S2(e){return yr||(yr=await Ft(e.body.modelPath),yr.width=parseInt(yr.signature.inputs["input_1:0"].tensorShape.dim[2].size),yr.height=parseInt(yr.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Me(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),yr}async function T2(e,t){if(!yr||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Ke.resizeBilinear(e,[yr.width,yr.height],!1),a=_e(r,[255]);r.dispose();let s;if(t.profile){let u=await Yn(()=>yr.predict(a));s=u.result.find(c=>c.size===195||c.size===155).dataSync(),u.result.forEach(c=>c.dispose()),gr("blazepose",u)}else{let u=await yr.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?A4:g4,l=5;for(let u=0;uR2,predict:()=>F2});var $r,C2=[],z0=Number.MAX_SAFE_INTEGER,P0=2.5,ase=["person","bicycle","car","motorcycle","airplane","bus","train","vehicle","boat","traffic light","fire hydrant","stop sign","parking 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i=Math.max(r.box[0],0),o=s*oe.lineHeight+r.box[1];oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText(a[s],i+5,o+16)),n.fillStyle=oe.labelColor,n.fillText(a[s],i+4,o+15)}if(n.lineWidth=1,r.mesh&&r.mesh.length>0){if(oe.drawPoints)for(let s of r.mesh)W0(n,s[0],s[1],s[2]);if(oe.drawPolygons){n.lineWidth=1;for(let s=0;sr.mesh[o]);D2(n,i)}if(r.annotations&&r.annotations.leftEyeIris){n.strokeStyle=oe.useDepth?"rgba(255, 200, 255, 0.3)":oe.color,n.beginPath();let s=Math.abs(r.annotations.leftEyeIris[3][0]-r.annotations.leftEyeIris[1][0])/2,i=Math.abs(r.annotations.leftEyeIris[4][1]-r.annotations.leftEyeIris[2][1])/2;n.ellipse(r.annotations.leftEyeIris[0][0],r.annotations.leftEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),oe.fillPolygons&&(n.fillStyle=oe.useDepth?"rgba(255, 255, 200, 0.3)":oe.color,n.fill())}if(r.annotations&&r.annotations.rightEyeIris){n.strokeStyle=oe.useDepth?"rgba(255, 200, 255, 0.3)":oe.color,n.beginPath();let 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r=0;ri.part==="leftShoulder"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&D2(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),B0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),B0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),B0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>gt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),B0(n,s)}}}}async function N4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=oe.font;for(let r of t){if(oe.drawBoxes&&(n.strokeStyle=oe.color,n.fillStyle=oe.color,oe.useRawBoxes?ru(n,e.width*r.boxRaw[0],e.height*r.boxRaw[1],e.width*r.boxRaw[2],e.height*r.boxRaw[3]):ru(n,r.box[0],r.box[1],r.box[2],r.box[3]),oe.drawLabels&&(oe.shadowColor&&oe.shadowColor!==""&&(n.fillStyle=oe.shadowColor,n.fillText("hand",r.box[0]+3,1+r.box[1]+oe.lineHeight,r.box[2])),n.fillStyle=oe.labelColor,n.fillText("hand",r.box[0]+2,0+r.box[1]+oe.lineHeight,r.box[2])),n.stroke()),oe.drawPoints&&r.landmarks&&r.landmarks.length>0)for(let a of r.landmarks)n.fillStyle=oe.useDepth?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.5)`:oe.color,W0(n,a[0],a[1]);if(oe.drawPolygons){let a=s=>{if(!!s)for(let 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0);Dt.set(this,(...t)=>{if(!ye(this,Jc))return;let n=this.tf.engine().state.numTensors,r=ye(this,au);Ta(this,au,n);let a=n-r;a!==0&&Me(...t,a)});j0.set(this,t=>{if(!ye(this,Qc))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof qe))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});eh.set(this,async(t=!1)=>{if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=ut();if(this.state="backend",this.config.backend&&this.config.backend!==""){if(this.config.debug&&Me("setting backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&Me("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&Me(`wasm execution: ${r?"SIMD":"no SIMD"} 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0:c.leftEyeIris)&&((h=A==null?void 0:A.annotations)==null?void 0:h.rightEyeIris)&&(delete A.annotations.leftEyeIris,delete A.annotations.rightEyeIris);let y=((d=A.annotations)==null?void 0:d.leftEyeIris)&&((p=A.annotations)==null?void 0:p.rightEyeIris)?11.7*Math.max(Math.abs(A.annotations.leftEyeIris[3][0]-A.annotations.leftEyeIris[1][0]),Math.abs(A.annotations.rightEyeIris[4][1]-A.annotations.rightEyeIris[2][1])):0;o.push({...A,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:y!==0?Math.trunc(y)/100:0,angle:g,tensor:this.config.face.detector.return?(f=A.image)==null?void 0:f.squeeze():null}),(m=A.image)==null||m.dispose(),ye(this,Dt).call(this,"End Face")}return ye(this,Dt).call(this,"End FaceMesh:"),this.config.async&&(ye(this,He).face&&delete ye(this,He).face,ye(this,He).age&&delete ye(this,He).age,ye(this,He).gender&&delete ye(this,He).gender,ye(this,He).emotion&&delete ye(this,He).emotion),o});G0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(V0);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});q0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+V0;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)}));X0.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(V0):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});this.tf=Ch,this.draw=$2,this.version=T4,this.config=Yc(gt,t),this.state="idle",Ta(this,au,0),Ta(this,Jc,!1),Ta(this,Qc,!1),Ta(this,Bi,!0),Ta(this,He,{}),this.models={face:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null},this.image=n=>M2(n,this.config),this.classes={facemesh:O2,age:Wy,gender:Uy,emotion:Ky,body:this.config.body.modelPath.includes("posenet")?m2:N2,hand:_2,nanodet:E2},this.sysinfo=J2()}profileData(){return this.config.profile?Ly:{}}simmilarity(t,n){return this.config.face.embedding.enabled?t2(t,n):0}enhance(t){return n2(t)}match(t,n,r=0){return j6(t,n,r)}async load(t={}){this.state="load";let n=ut();t&&(this.config=Yc(this.config,t)),ye(this,Bi)&&(this.config.debug&&Me(`version: ${this.version}`),this.config.debug&&Me(`tfjs version: ${this.tf.version_core}`),this.config.debug&&Me("platform:",this.sysinfo.platform),this.config.debug&&Me("agent:",this.sysinfo.agent),await ye(this,eh).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Me("configuration:",this.config),this.config.debug&&Me("tf flags:",this.tf.ENV.flags))),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,this.models.nanodet]=await Promise.all([this.models.face||(this.config.face.enabled?O2.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?By(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?qy(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Jy(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?e2(this.config):null),this.models.handpose||(this.config.hand.enabled?I2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?g2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?S2(this.config):null),this.models.nanodet||(this.config.object.enabled?R2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await O2.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await By(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await qy(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Jy(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await e2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await I2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await g2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await S2(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await R2(this.config))),ye(this,Bi)&&(this.config.debug&&Me("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ta(this,Bi,!1));let r=Math.trunc(ut()-n);r>(ye(this,He).load||0)&&(ye(this,He).load=r)}async detect(t,n={}){return new Promise(async r=>{var f,m,A,g;this.state="config";let a;this.config=Yc(this.config,n),this.state="check";let s=ye(this,j0).call(this,t);s&&(Me(s,t),r({error:s}));let i=ut();await ye(this,eh).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),ye(this,Dt).call(this,"Start Scope:"),a=ut();let o=M2(t,this.config);if(!o||!o.tensor){Me("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}ye(this,He).image=Math.trunc(ut()-a),ye(this,Dt).call(this,"Get Image:");let l,u,c,h;this.config.async?(c=this.config.face.enabled?ye(this,th).call(this,o.tensor):[],ye(this,He).face&&delete ye(this,He).face):(this.state="run:face",a=ut(),c=this.config.face.enabled?await ye(this,th).call(this,o.tensor):[],ye(this,He).face=Math.trunc(ut()-a)),ye(this,Dt).call(this,"Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?(f=this.models.posenet)==null?void 0:f.estimatePoses(o.tensor,this.config):[]:l=this.config.body.enabled?T2(o.tensor,this.config):[],ye(this,He).body&&delete ye(this,He).body):(this.state="run:body",a=ut(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await((m=this.models.posenet)==null?void 0:m.estimatePoses(o.tensor,this.config)):[]:l=this.config.body.enabled?await T2(o.tensor,this.config):[],ye(this,He).body=Math.trunc(ut()-a)),ye(this,Dt).call(this,"End Body:"),ye(this,Dt).call(this,"Start Hand:"),this.config.async?(u=this.config.hand.enabled?(A=this.models.handpose)==null?void 0:A.estimateHands(o.tensor,this.config):[],ye(this,He).hand&&delete ye(this,He).hand):(this.state="run:hand",a=ut(),u=this.config.hand.enabled?await((g=this.models.handpose)==null?void 0:g.estimateHands(o.tensor,this.config)):[],ye(this,He).hand=Math.trunc(ut()-a)),ye(this,Dt).call(this,"End Hand:"),ye(this,Dt).call(this,"Start Object:"),this.config.async?(h=this.config.object.enabled?F2(o.tensor,this.config):[],ye(this,He).object&&delete ye(this,He).object):(this.state="run:object",a=ut(),h=this.config.object.enabled?await F2(o.tensor,this.config):[],ye(this,He).object=Math.trunc(ut()-a)),ye(this,Dt).call(this,"End Object:"),this.config.async&&([c,l,u,h]=await Promise.all([c,l,u,h])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),ye(this,Dt).call(this,"End Scope:");let d=[];this.config.gesture.enabled&&(a=ut(),d=[...x4(c),...y4(l),...b4(u),...w4(c)],this.config.async?ye(this,He).gesture&&delete ye(this,He).gesture:ye(this,He).gesture=Math.trunc(ut()-a)),ye(this,He).total=Math.trunc(ut()-i),this.state="idle";let p={face:c,body:l,hand:u,gesture:d,object:h,performance:ye(this,He),canvas:o.canvas};r(p)})}async warmup(t={}){let n=ut();t&&(this.config=Yc(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await ye(this,G0).call(this):typeof Image!="undefined"?a=await ye(this,q0).call(this):a=await ye(this,X0).call(this),this.config.videoOptimized=r;let s=ut();return this.config.debug&&Me("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};He=new WeakMap,au=new WeakMap,Jc=new WeakMap,Qc=new WeakMap,Bi=new WeakMap,Dt=new WeakMap,j0=new WeakMap,eh=new WeakMap,H0=new WeakMap,th=new WeakMap,G0=new WeakMap,q0=new WeakMap,X0=new WeakMap;var nh=0,E4=!1,vt={background:"darkslategray",hover:"lightgray",itemBackground:"black",itemColor:"white",buttonBackground:"lightblue",buttonHover:"lightgreen",checkboxOn:"lightgreen",checkboxOff:"lightcoral",rangeBackground:"lightblue",rangeLabel:"white",chartColor:"lightblue"};function use(){if(E4)return;let e=` :root { --rounded: 0.1rem; } .menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10; box-shadow: 0 0 8px dimgrey; background: ${vt.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; } .menu:hover { box-shadow: 0 0 8px ${vt.hover}; } .menu-container { display: block; max-height: 100vh; } .menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; } .menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; } .menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; } .menu-title { cursor: pointer; } .menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) } .menu-label { padding: 0; font-weight: 800; } .menu-list { margin-right: 0.8rem; } select:focus { outline: none; } .menu-list-item { background: ${vt.itemBackground}; color: ${vt.itemColor}; border: none; padding: 0.2rem; font-family: inherit; font-variant: inherit; border-radius: var(--rounded); font-weight: 800; } .menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center} .menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; } .menu-button { border: 0; background: ${vt.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer; box-shadow: 4px 4px 4px 0 dimgrey; border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; } .menu-button:hover { background: ${vt.buttonHover}; box-shadow: 4px 4px 4px 0 black; } .menu-button:focus { outline: none; } .menu-checkbox { width: 2.8rem; height: 1rem; background: ${vt.itemBackground}; margin: 0.5rem 0.5rem 0 0; position: relative; border-radius: var(--rounded); } .menu-checkbox:after { content: 'OFF'; color: ${vt.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; } .menu-checkbox:before { content: 'ON'; color: ${vt.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; } .menu-checkbox-label { width: 1.3rem; height: 0.8rem; cursor: pointer; position: absolute; top: 0.1rem; left: 0.1rem; z-index: 1; background: ${vt.checkboxOff}; border-radius: var(--rounded); transition: left 0.6s ease; } input[type=checkbox] { visibility: hidden; } input[type=checkbox]:checked + label { left: 1.4rem; background: ${vt.checkboxOn}; } .menu-range { margin: 0.2rem 0.5rem 0 0; width: 3.5rem; background: transparent; color: ${vt.rangeBackground}; } .menu-range:before { color: ${vt.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); } input[type=range] { -webkit-appearance: none; } input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${vt.itemBackground}; border-radius: var(--rounded); border: 1px; } input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${vt.itemBackground}; border-radius: var(--rounded); border: 1px; } input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${vt.rangeBackground}; cursor: pointer; -webkit-appearance: none; } input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${vt.rangeBackground}; cursor: pointer; -webkit-appearance: none; } .svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; } .svg-foreground { fill:white; cursor:pointer; opacity: 0.8; } `,t=document.createElement("style");t.innerHTML=e,document.getElementsByTagName("head")[0].appendChild(t),E4=!0}var C4=class{constructor(t,n,r,a){a&&(vt={...vt,...a}),use(),this.createMenu(t,n,r),this.id=0,this.instance=nh,nh++,this._maxFPS=0,this.hidden=0}createMenu(t,n="",r={top:null,left:null,bottom:null,right:null}){this.menu=document.createElement("div"),this.menu.id=`menu-${nh}`,this.menu.className="menu",r&&(r.top&&(this.menu.style.top=r.top),r.bottom&&(this.menu.style.bottom=r.bottom),r.left&&(this.menu.style.left=r.left),r.right&&(this.menu.style.right=r.right)),this.container=document.createElement("div"),this.container.id=`menu-container-${nh}`,this.container.className="menu-container menu-container-fadein";let a=document.createElement("div");a.className="menu-title",a.id=`menu-title-${nh}`;let s=` `;n&&(a.innerHTML=`${n}${s}`),this.menu.appendChild(a),a.addEventListener("click",()=>{this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"),this.menu.style.borderStyle=this.container.classList.contains("menu-container-fadeout")?"none":"solid"}),this.menu.appendChild(this.container),typeof t=="object"?t.appendChild(this.menu):document.getElementById(t).appendChild(this.menu)}get newID(){return this.id++,`menu-${this.instance}-${this.id}`}get ID(){return`menu-${this.instance}-${this.id}`}get width(){return this.menu.offsetWidth}get height(){return this.menu.offsetHeight}hide(){this.container.classList.contains("menu-container-fadein")&&(this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"))}visible(){return this.container.classList.contains("menu-container-fadein")}toggle(t){if(this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"),this.container.classList.contains("menu-container-fadein")&&t){let n=t.x||(t.touches&&t.touches[0]?t.touches[0].pageX:null);n&&(this.menu.style.left=`${n-this.menu.offsetWidth/2}px`),this.menu.offsetLeft<0&&(this.menu.style.left=0),this.menu.offsetLeft+this.menu.offsetWidth>window.innerWidth&&(this.menu.style.left=null,this.menu.style.right=0),this.menu.style.borderStyle="solid"}else this.menu.style.borderStyle="none"}addTitle(t){let n=document.createElement("div");return n.className="menu-title",n.id=this.newID,n.innerHTML=t,this.menu.appendChild(n),n.addEventListener("click",()=>{this.hidden=!this.hidden;let r=document.getElementsByClassName("menu");for(let a of r)a.style.display=this.hidden?"none":"block"}),n}addLabel(t){let n=document.createElement("div");return n.className="menu-item menu-label",n.id=this.newID,n.innerHTML=t,this.container.appendChild(n),n}addBool(t,n,r,a){let s=document.createElement("div");return s.className="menu-item",s.innerHTML=`
${t}`,this.container.appendChild(s),s.addEventListener("change",i=>{n[r]=i.target.checked,a&&a(i.target.checked)}),s}async addList(t,n,r,a){let s=document.createElement("div");s.className="menu-item";let i="";for(let o of n)i+=``;return s.innerHTML=`
${t}`,s.style.fontFamily=document.body.style.fontFamily,s.style.fontSize=document.body.style.fontSize,s.style.fontVariant=document.body.style.fontVariant,this.container.appendChild(s),s.addEventListener("change",o=>{a&&a(n[o.target.selectedIndex])}),s}addRange(t,n,r,a,s,i,o){let l=document.createElement("div");return l.className="menu-item",l.innerHTML=`${t}`,this.container.appendChild(l),l.addEventListener("change",u=>{n[r]=parseInt(u.target.value)===parseFloat(u.target.value)?parseInt(u.target.value):parseFloat(u.target.value),u.target.setAttribute("value",u.target.value),o&&o(u.target.value)}),l.input=l.children[0],l}addHTML(t){let n=document.createElement("div");return n.className="menu-item",n.id=this.newID,t&&(n.innerHTML=t),this.container.appendChild(n),n}addButton(t,n,r){let a=document.createElement("button");return a.className="menu-item 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a=r.getContext("2d");a.fillStyle=vt.background,a.fillRect(0,0,r.width,r.height);let s=r.width/n.length,i=1+Math.max(...n),o=r.height/i;for(let l=0;l 00 FPS `,R4=class{constructor(t,n={}){this.css=cse,this.svg=hse,this.paramLogger=()=>{},this.chartLogger=()=>{},this.chartLen=20,this.chartHz=20,this.names=[],this.cpuAccums=[],this.gpuAccums=[],this.activeAccums=[],this.chart=new Array(this.chartLen),this.now=()=>performance&&performance.now?performance.now():Date.now(),this.updateUI=()=>{[].forEach.call(this.nodes["gl-gpu-svg"],o=>o.style.display=this.trackGPU?"inline":"none")},Object.assign(this,n),this.detected=0,this.finished=[],this.isFramebuffer=0,this.frameId=0;let r,a=0,s,i=o=>{++a<20?r=requestAnimationFrame(i):(this.detected=Math.ceil(1e3*a/(o-s)/70),cancelAnimationFrame(r)),s||(s=o)};if(requestAnimationFrame(i),t){let o=async(c,h)=>Promise.resolve(setTimeout(()=>{t.getError();let d=this.now()-c;h.forEach((p,f)=>{p&&(this.gpuAccums[f]+=d)})},0)),l=(c,h,d)=>{let p=h.now();c.apply(d,arguments),h.trackGPU&&h.finished.push(o(p,h.activeAccums.slice(0)))},u="drawElements";t[u]?t[u]=l(t[u],this,t):console.log("bench: cannot attach to webgl function")}if(!this.withoutUI){this.dom||(this.dom=document.body);let o=document.createElement("div");o.id="gl-bench",this.dom.appendChild(o),this.dom.insertAdjacentHTML("afterbegin",'"),this.dom=o,this.dom.addEventListener("click",()=>{this.trackGPU=!this.trackGPU,this.updateUI()}),this.paramLogger=((l,u,c)=>{let h=["gl-cpu","gl-gpu","gl-mem","gl-fps","gl-gpu-svg","gl-chart"],d={...h};return h.forEach(p=>d[p]=u.getElementsByClassName(p)),this.nodes=d,(p,f,m,A,g,y,w)=>{d["gl-cpu"][p].style.strokeDasharray=(f*.27).toFixed(0)+" 100",d["gl-gpu"][p].style.strokeDasharray=(m*.27).toFixed(0)+" 100",d["gl-mem"][p].innerHTML=c[p]?c[p]:A?"mem: "+A.toFixed(0)+"mb":"",d["gl-fps"][p].innerHTML="FPS: "+g.toFixed(1),l(c[p],f,m,A,g,y,w)}})(this.paramLogger,this.dom,this.names),this.chartLogger=((l,u)=>{let c={"gl-chart":u.getElementsByClassName("gl-chart")};return(h,d,p)=>{let f="",m=d.length;for(let A=0;A=1e3){let a=this.frameId-this.paramFrame,s=a/r*1e3;for(let i=0;i{this.gpuAccums[i]=0,this.finished=[]})}this.paramFrame=this.frameId,this.paramTime=n}}if(!this.detected||!this.chartFrame)this.chartFrame=this.frameId,this.chartTime=n,this.circularId=0;else{let r=n-this.chartTime,a=this.chartHz*r/1e3;for(;--a>0&&this.detected;){let i=(this.frameId-this.chartFrame)/r*1e3;this.chart[this.circularId%this.chartLen]=i;for(let o=0;o0)||((a=(r=e==null?void 0:e.face[0])==null?void 0:r.embedding)==null?void 0:a.length)>=64)return;if(!Ui)if(Ui=e,e.face[0].tensor){let o=te.enhance(e.face[0]);if(o){let l=document.getElementById("orig"),u=o.squeeze();te.tf.browser.toPixels(u,l),o.dispose(),u.dispose()}}else document.getElementById("compare-canvas").getContext("2d").drawImage(Ui.canvas,0,0,200,200);let t=te.simmilarity((s=Ui==null?void 0:Ui.face[0])==null?void 0:s.embedding,(i=e==null?void 0:e.face[0])==null?void 0:i.embedding);document.getElementById("simmilarity").innerText=`simmilarity: ${Math.trunc(1e3*t)/10}%`}var M4=performance.now();async function Y0(e){let t=Z0,n=document.getElementById("canvas");if(he.drawFPS.push(1e3/(performance.now()-M4)),he.drawFPS.length>he.maxFPSframes&&he.drawFPS.shift(),M4=performance.now(),await Ae.process.updateChart("FPS",he.detectFPS),he.buffered||!t.canvas){let h=await te.image(e);t.canvas=h.canvas,te.tf.dispose(h.tensor)}let r=n.getContext("2d");r.fillStyle=he.baseBackground,r.fillRect(0,0,n.width,n.height),t.canvas?(t.canvas.width!==n.width&&(n.width=t.canvas.width),t.canvas.height!==n.height&&(n.height=t.canvas.height),r.drawImage(t.canvas,0,0,t.canvas.width,t.canvas.height,0,0,t.canvas.width,t.canvas.height)):r.drawImage(e,0,0,e.width,e.height,0,0,n.width,n.height),te.draw.face(n,t.face),te.draw.body(n,t.body),te.draw.hand(n,t.hand),te.draw.object(n,t.object),te.draw.gesture(n,t.gesture),await pse(t);let a=te.tf.engine(),s=a.backendInstance?`gpu: ${(a.backendInstance.numBytesInGPU?a.backendInstance.numBytesInGPU:0).toLocaleString()} bytes`:"",i=`system: ${a.state.numBytes.toLocaleString()} bytes ${s} | tensors: ${a.state.numTensors.toLocaleString()}`,o=t.canvas?`processing: ${t.canvas.width} x ${t.canvas.height}`:"",l=Math.trunc(10*he.detectFPS.reduce((h,d)=>h+d,0)/he.detectFPS.length)/10,u=Math.trunc(10*he.drawFPS.reduce((h,d)=>h+d,0)/he.drawFPS.length)/10,c=he.detectFPS.length>5&&l<5?'warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models':"";document.getElementById("log").innerHTML=` video: ${he.camera.name} | facing: ${he.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${he.camera.width} x ${he.camera.height} ${o}
backend: ${te.tf.getBackend()} | ${i}
performance: ${dse(t.performance)}ms FPS process:${l} refresh:${u}
${c}
`,he.framesDraw++,he.lastFrame=performance.now(),he.buffered?he.drawThread=requestAnimationFrame(()=>Y0(e,n)):!he.buffered&&he.drawThread&&(Gn("stopping buffered refresh"),cancelAnimationFrame(he.drawThread),he.drawThread=null)}async function J0(){var u;if(he.busy)return null;he.busy=!0;let e=document.getElementById("video"),t=document.getElementById("canvas"),n=document.getElementById("log"),r=e.srcObject?e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused:!1,a="";if(rr("setting up camera"),!navigator.mediaDevices)return a="camera access not supported",n.innerText+=` ${a}`,Gn(a),rr(a),he.busy=!1,a;let s,i={audio:!1,video:{facingMode:he.facing?"user":"environment",resizeMode:he.crop?"crop-and-scale":"none"}};window.innerWidth>window.innerHeight?i.video.width={ideal:window.innerWidth}:i.video.height={ideal:window.innerHeight-document.getElementById("menubar").offsetHeight};try{s=await navigator.mediaDevices.getUserMedia(i)}catch(c){return c.name==="PermissionDeniedError"||c.name==="NotAllowedError"?a="camera permission denied":c.name==="SourceUnavailableError"?a="camera not available":a=`camera error: ${c.message||c}`,n.innerText+=` ${a}`,rr(a),Gn("camera error:",c),he.busy=!1,a}if(s)e.srcObject=s;else return he.busy=!1,"camera stream empty";let o=s.getVideoTracks()[0],l=o.getSettings();return he.camera={name:(u=o.label)==null?void 0:u.toLowerCase(),width:l.width,height:l.height,facing:l.facingMode==="user"?"front":"back"},new Promise(c=>{e.onloadeddata=async()=>{e.width=e.videoWidth,e.height=e.videoHeight,t.width=e.width,t.height=e.height,t.style.width=t.width>t.height?"100vw":"",t.style.height=t.width>t.height?"":"100vh",he.menuWidth.input.setAttribute("value",e.width),he.menuHeight.input.setAttribute("value",e.height),r&&e.play(),r&&!he.detectThread&&ah(e,t),he.busy=!1,rr(""),c()}})}function $4(){if(!Vi){let e=null;Vi=new F4(e,{trackGPU:!1,chartHz:20,chartLen:20}),Vi.begin()}}function fse(e,t,n,r){K0||(Gn("creating worker thread"),K0=new Worker(he.worker,{type:"module"}),K0.addEventListener("message",a=>{a.data.result.performance&&a.data.result.performance.total&&he.detectFPS.push(1e3/a.data.result.performance.total),he.detectFPS.length>he.maxFPSframes&&he.detectFPS.shift(),he.bench&&(Vi||$4(),Vi.nextFrame(r)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=he.bench?"block":"none"),Z0=a.data.result,he.framesDetect++,he.drawThread||Y0(e),he.detectThread=requestAnimationFrame(s=>ah(e,n,s))})),K0.postMessage({image:t.data.buffer,width:n.width,height:n.height,userConfig:ls},[t.data.buffer])}function ah(e,t,n){var a;if(!(e.srcObject&&e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused)&&e.srcObject){he.drawThread&&cancelAnimationFrame(he.drawThread),he.detectThread&&cancelAnimationFrame(he.detectThread),he.drawThread=null,he.detectThread=null,e.paused?Gn("camera paused"):e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState<=2?setTimeout(()=>ah(e,t),500):Gn(`camera not ready: track state: ${(a=e.srcObject)==null?void 0:a.getVideoTracks()[0].readyState} stream state: ${e.readyState}`),clearTimeout(he.drawThread),he.drawThread=null,Gn("frame statistics: process:",he.framesDetect,"refresh:",he.framesDraw),Gn("memory",te.tf.engine().memory());return}if(rr(""),he.useWorker){let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t.width,t.height):document.createElement("canvas");s.width=t.width,s.height=t.height;let i=s.getContext("2d");i.drawImage(e,0,0,e.width,e.height,0,0,t.width,t.height);let o=i.getImageData(0,0,t.width,t.height);fse(e,o,t,ls,n)}else te.detect(e,ls).then(s=>{s.performance&&s.performance.total&&he.detectFPS.push(1e3/s.performance.total),he.detectFPS.length>he.maxFPSframes&&he.detectFPS.shift(),he.bench&&(Vi||$4(),Vi.nextFrame(n)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=he.bench?"block":"none"),s.error?(Gn(s.error),document.getElementById("log").innerText+=` Human error: ${s.error}`):(Z0=s,he.drawThread||Y0(e),he.framesDetect++,he.detectThread=requestAnimationFrame(i=>ah(e,t,i)))})}async function mse(e){return new Promise(t=>{let n=new Image;n.onload=async()=>{Gn("Processing image:",encodeURI(n.src));let r=document.getElementById("canvas");n.width=n.naturalWidth,n.height=n.naturalHeight,r.width=te.config.filter.width&&te.config.filter.width>0?te.config.filter.width:n.naturalWidth,r.height=te.config.filter.height&&te.config.filter.height>0?te.config.filter.height:n.naturalHeight;let a=await te.detect(n,ls);Z0=a,await Y0(n);let s=document.createElement("canvas");s.className="thumbnail",s.width=window.innerWidth/(he.columns+.1),s.height=s.width*r.height/r.width,a.face&&a.face.length>0?s.title=a.face.map((o,l)=>`#${l} face: ${Math.trunc(100*o.faceConfidence)}% box: ${Math.trunc(100*o.boxConfidence)}% age: ${Math.trunc(o.age)} gender: ${Math.trunc(100*o.genderConfidence)}% ${o.gender}`).join(" | "):s.title="no face detected",s.getContext("2d").drawImage(r,0,0,r.width,r.height,0,0,s.width,s.height),document.getElementById("samples-container").appendChild(s),n.src="",t(!0)},n.src=e})}async function D4(){document.getElementById("samples-container").style.display="none",document.getElementById("canvas").style.display="block";let e=document.getElementById("video"),t=document.getElementById("canvas");if(e.srcObject!==null&&!e.paused)document.getElementById("play").style.display="block",document.getElementById("btnStart").className="button button-start",document.getElementById("btnStart").innerHTML="start
video",rr("paused"),e.pause();else{let n=await J0();if(n)rr(n);else{document.getElementById("play").style.display="none";for(let r of Object.values(Ae))r.hide();rr(""),document.getElementById("btnStart").className="button button-stop",document.getElementById("btnStart").innerHTML="pause
video",await e.play(),he.detectThread||ah(e,t)}}}async function Ase(){ls.videoOptimized=!1,document.getElementById("play").style.display="none",document.getElementById("canvas").style.display="none",document.getElementById("samples-container").style.display="block",Gn("Running detection of sample images"),rr("processing images"),document.getElementById("samples-container").innerHTML="";for(let e of Object.values(Ae))e.hide();for(let e of he.samples)await mse(e);rr("")}function gse(){let e=[];window.innerWidth>800?e=[`${document.getElementById("btnDisplay").offsetLeft-50}px`,`${document.getElementById("btnImage").offsetLeft-50}px`,`${document.getElementById("btnProcess").offsetLeft-50}px`,`${document.getElementById("btnModel").offsetLeft-50}px`]:e=["0rem","11rem","21.1rem","33rem"],Ae.display=new rh(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[0]}),Ae.display.addBool("perf monitor",he,"bench",t=>he.bench=t),Ae.display.addBool("buffered output",he,"buffered",t=>he.buffered=t),Ae.display.addBool("crop & scale",he,"crop",t=>{he.crop=t,J0()}),Ae.display.addBool("camera facing",he,"facing",t=>{he.facing=t,J0()}),Ae.display.addHTML('
'),Ae.display.addBool("use 3D depth",te.draw.drawOptions,"useDepth"),Ae.display.addBool("draw with curves",te.draw.drawOptions,"useCurves"),Ae.display.addBool("print labels",te.draw.drawOptions,"drawLabels"),Ae.display.addBool("draw points",te.draw.drawOptions,"drawPoints"),Ae.display.addBool("draw boxes",te.draw.drawOptions,"drawBoxes"),Ae.display.addBool("draw polygons",te.draw.drawOptions,"drawPolygons"),Ae.display.addBool("fill polygons",te.draw.drawOptions,"fillPolygons"),Ae.image=new rh(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[1]}),Ae.image.addBool("enabled",te.config.filter,"enabled",t=>te.config.filter.enabled=t),he.menuWidth=Ae.image.addRange("image width",te.config.filter,"width",0,3840,10,t=>te.config.filter.width=parseInt(t)),he.menuHeight=Ae.image.addRange("image height",te.config.filter,"height",0,2160,10,t=>te.config.filter.height=parseInt(t)),Ae.image.addHTML('
'),Ae.image.addRange("brightness",te.config.filter,"brightness",-1,1,.05,t=>te.config.filter.brightness=parseFloat(t)),Ae.image.addRange("contrast",te.config.filter,"contrast",-1,1,.05,t=>te.config.filter.contrast=parseFloat(t)),Ae.image.addRange("sharpness",te.config.filter,"sharpness",0,1,.05,t=>te.config.filter.sharpness=parseFloat(t)),Ae.image.addRange("blur",te.config.filter,"blur",0,20,1,t=>te.config.filter.blur=parseInt(t)),Ae.image.addRange("saturation",te.config.filter,"saturation",-1,1,.05,t=>te.config.filter.saturation=parseFloat(t)),Ae.image.addRange("hue",te.config.filter,"hue",0,360,5,t=>te.config.filter.hue=parseInt(t)),Ae.image.addRange("pixelate",te.config.filter,"pixelate",0,32,1,t=>te.config.filter.pixelate=parseInt(t)),Ae.image.addHTML('
'),Ae.image.addBool("negative",te.config.filter,"negative",t=>te.config.filter.negative=t),Ae.image.addBool("sepia",te.config.filter,"sepia",t=>te.config.filter.sepia=t),Ae.image.addBool("vintage",te.config.filter,"vintage",t=>te.config.filter.vintage=t),Ae.image.addBool("kodachrome",te.config.filter,"kodachrome",t=>te.config.filter.kodachrome=t),Ae.image.addBool("technicolor",te.config.filter,"technicolor",t=>te.config.filter.technicolor=t),Ae.image.addBool("polaroid",te.config.filter,"polaroid",t=>te.config.filter.polaroid=t),Ae.process=new rh(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[2]}),Ae.process.addList("backend",["cpu","webgl","wasm","humangl"],te.config.backend,t=>te.config.backend=t),Ae.process.addBool("async operations",te.config,"async",t=>te.config.async=t),Ae.process.addBool("use web worker",he,"useWorker"),Ae.process.addHTML('
'),Ae.process.addLabel("model parameters"),Ae.process.addRange("max objects",te.config.face.detector,"maxFaces",1,50,1,t=>{te.config.face.detector.maxFaces=parseInt(t),te.config.body.maxDetections=parseInt(t),te.config.hand.maxHands=parseInt(t)}),Ae.process.addRange("skip frames",te.config.face.detector,"skipFrames",0,50,1,t=>{te.config.face.detector.skipFrames=parseInt(t),te.config.face.emotion.skipFrames=parseInt(t),te.config.face.age.skipFrames=parseInt(t),te.config.hand.skipFrames=parseInt(t)}),Ae.process.addRange("min confidence",te.config.face.detector,"minConfidence",0,1,.05,t=>{te.config.face.detector.minConfidence=parseFloat(t),te.config.face.gender.minConfidence=parseFloat(t),te.config.face.emotion.minConfidence=parseFloat(t),te.config.hand.minConfidence=parseFloat(t)}),Ae.process.addRange("score threshold",te.config.face.detector,"scoreThreshold",.1,1,.05,t=>{te.config.face.detector.scoreThreshold=parseFloat(t),te.config.hand.scoreThreshold=parseFloat(t),te.config.body.scoreThreshold=parseFloat(t)}),Ae.process.addRange("overlap",te.config.face.detector,"iouThreshold",.1,1,.05,t=>{te.config.face.detector.iouThreshold=parseFloat(t),te.config.hand.iouThreshold=parseFloat(t)}),Ae.process.addBool("detection rotation",te.config.face.detector,"rotation",t=>{te.config.face.detector.rotation=t,te.config.hand.rotation=t}),Ae.process.addHTML('
'),Ae.process.addButton("process sample images","process images",()=>Ase()),Ae.process.addHTML('
'),Ae.process.addChart("FPS","FPS"),Ae.models=new rh(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[3]}),Ae.models.addBool("face detect",te.config.face,"enabled",t=>te.config.face.enabled=t),Ae.models.addBool("face mesh",te.config.face.mesh,"enabled",t=>te.config.face.mesh.enabled=t),Ae.models.addBool("face iris",te.config.face.iris,"enabled",t=>te.config.face.iris.enabled=t),Ae.models.addBool("face age",te.config.face.age,"enabled",t=>te.config.face.age.enabled=t),Ae.models.addBool("face gender",te.config.face.gender,"enabled",t=>te.config.face.gender.enabled=t),Ae.models.addBool("face emotion",te.config.face.emotion,"enabled",t=>te.config.face.emotion.enabled=t),Ae.models.addHTML('
'),Ae.models.addBool("body pose",te.config.body,"enabled",t=>te.config.body.enabled=t),Ae.models.addBool("hand pose",te.config.hand,"enabled",t=>te.config.hand.enabled=t),Ae.models.addHTML('
'),Ae.models.addBool("gestures",te.config.gesture,"enabled",t=>te.config.gesture.enabled=t),Ae.models.addHTML('
'),Ae.models.addBool("object detection",te.config.object,"enabled",t=>te.config.object.enabled=t),Ae.models.addHTML('
'),Ae.models.addBool("face compare",te.config.face.embedding,"enabled",t=>{te.config.face.embedding.enabled=t,Ui=null}),document.getElementById("btnDisplay").addEventListener("click",t=>Ae.display.toggle(t)),document.getElementById("btnImage").addEventListener("click",t=>Ae.image.toggle(t)),document.getElementById("btnProcess").addEventListener("click",t=>Ae.process.toggle(t)),document.getElementById("btnModel").addEventListener("click",t=>Ae.models.toggle(t)),document.getElementById("btnStart").addEventListener("click",()=>D4()),document.getElementById("play").addEventListener("click",()=>D4())}async function yse(e){let t=document.getElementById("canvas");t.width=e.canvas.width,t.height=e.canvas.height,t.getContext("2d").drawImage(e.canvas,0,0,e.canvas.width,e.canvas.height,0,0,t.width,t.height),await te.draw.all(t,e)}async function xse(){if(Gn("Demo starting ..."),gse(),document.getElementById("log").innerText=`Human: version ${te.version}`,he.modelsPreload&&!he.useWorker){rr("loading"),await te.load(ls);let e=Object.keys(te.models).filter(t=>te.models[t]);Gn("Demo loaded models:",e)}if(!he.useWorker){rr("initializing");let e=await te.warmup(ls);e&&e.canvas&&he.drawWarmup&&await yse(e)}rr("human: ready"),document.getElementById("loader").style.display="none",document.getElementById("play").style.display="block",Gn("Demo ready...")}window.onload=xse;window.onresize=J0; /** * @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=demo-browser-index.js.map