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u=E.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,d=u.strideHeight,p=u.strideWidth,f=u.filterDepth,m=u.filterHeight,A=u.filterWidth,y=u.dilationDepth,g=u.dilationHeight,x=u.dilationWidth,v=u.effectiveFilterDepth,w=u.effectiveFilterHeight,b=u.effectiveFilterWidth,k=v-1-u.padInfo.front,N=b-1-u.padInfo.left,C=w-1-u.padInfo.top,F=Be(s.shape,"float32"),O=1/(f*m*A),L=n.bufferSync(a);for(let V=0;V=u.outDepth||Math.floor(Q)!==Q))for(let he=0;he=u.outHeight||Math.floor(oe)!==oe))for(let me=0;me=u.outWidth||Math.floor(pe)!==pe||(te+=L.get(V,Q,oe,pe,j))}}}F.set(te*O,V,U,X,G,j)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var U$={kernelName:Oh,backendName:"cpu",kernelFunc:j$};function H$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;ve([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=E.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,d=u.strideWidth,p=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,A=u.dilationWidth,y=u.effectiveFilterHeight,g=u.effectiveFilterWidth,x=g-1-u.padInfo.left,v=y-1-u.padInfo.top,w=Be(i.shape,"float32"),b=1/(p*f),k=n.data.get(a.dataId).values,N=Be(a.shape,"float32",k);for(let C=0;C=u.outHeight||Math.floor(G)!==G))for(let ee=0;ee=u.outWidth||Math.floor(Y)!==Y||(U+=N.get(C,G,Y,F))}}w.set(U*b,C,O,L,F)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var G$={kernelName:Dh,backendName:"cpu",kernelFunc:H$};function q$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;_.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean 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Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:rr.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. 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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 Ll(r,_p):p=new Ga(r,_p);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,c;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...bc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];u=E.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=_.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),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)&&ua().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=>_.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(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=_.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=_.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=_.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],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:_.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=_.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 c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=lB){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&_.sizeFromShape(n.shape)0&&_.isString(n[0])){let a=n.map(s=>_.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 ua().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new rB(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new WW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ti(e.shape),...Ei(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[Ti(t),...Ei(t)],s=new B3(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=Ap(r),i;n?i=new gL(s):i=new yL(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===xc.DENSE){let m=bc(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),_.sizeFromShape(s.shape)===0)return i.values=_.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&&_.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&&!gc(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=lW(e,l,c),h=this.getAndSaveBinary(u,()=>iW(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),oW(this.gpgpu,h,l,c,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=_.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=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(xe(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?sB:iB}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,c;l&&(c=_.now());let u=t.texShape;if(u==null&&(u=l3(n,o),t.texShape=u),a!=null){let h=Ap(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=$l(u[0],u[1]),d=new _L(h,[f,p],m)):d=new bL(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=rr.PIXELS:this.texData.get(A.dataId).usage=rr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=_.now()-c)}else{let h=this.acquireTexture(u,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=hB(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]*_.bytesPerElement(t)}};Wl.nextDataId=0;function hB(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 Wl,2);var dB={forceHalfFloat:q3},X3=` if (isnan(a)) return a; if (isnan(b)) return b; `,Bl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=` float binaryOperation(float a, float b) { ${e} } void main() { float a = getAAtOutCoords(); float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } `}},vp=` 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; `,vc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||_.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(s=` ${lt(a)} coords = getOutputCoords(); `,a===1)s+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let i=pn("coords",a);s+=` bool nextRowOutOfBounds = (${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 jn(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 pB={kernelName:Es,backendName:"webgl",kernelFunc:jn};function qa(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=jn({inputs:{x:r},backend:n}),l=jn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var fB={kernelName:Ph,backendName:"webgl",kernelFunc:qa},K3="return (a < 0.) ? b * a : a;",Z3=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function mB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",_.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(Z3,a.shape,i.shape):new Bl(K3,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var AB={kernelName:Cs,backendName:"webgl",kernelFunc:mB},Y3="return (a < 0.) ? b * a : a;",J3=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function yB(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(J3,r.shape,a.shape):new Bl(Y3,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var gB={kernelName:js,backendName:"webgl",kernelFunc:yB},Q3="if (isnan(x)) return x;",xB=` if (isnan(a)) return a; if (isnan(b)) return b; `,wB=` 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 Xe({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 c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Ll(i.shape,t):u=new Ga(i.shape,e),o.runWebGLProgram(u,[i],l)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[v,w]=x,b={dataId:v.dataId,dtype:v.dtype,shape:l.shape},k={dataId:w.dataId,dtype:w.dtype,shape:c.shape},N=new Bl(e,l.shape,c.shape);return u.runWebGLProgram(N,[b,k],dr(v.dtype,w.dtype))}),g=qa({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||dr(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),x=u.texData.get(g.dataId);return x.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new vc(t,l.shape,c.shape,n):p=new Bl(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function kp(e,t=!1){if(e==="linear")return t?JW:qW;if(e==="relu")return t?eB:KW;if(e==="elu")return t?QW:XW;if(e==="relu6")return t?tB:ZW;if(e==="prelu")return t?J3:Y3;if(e==="leakyrelu")return t?Z3:K3;if(e==="sigmoid")return t?nB:YW;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var e7=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 c=r?e[1]:e[2],u=Math.ceil(c/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) { ${i} }`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. 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} return asin(x); `,aV=Xe({opSnippet:rV}),sV={kernelName:po,backendName:"webgl",kernelFunc:aV},iV=Sr+"return log(x + sqrt(x * x + 1.0));",oV=Xe({opSnippet:iV}),lV={kernelName:fo,backendName:"webgl",kernelFunc:oV},uV=Sr+` return atan(x); `,cV=Xe({opSnippet:uV}),hV={kernelName:mo,backendName:"webgl",kernelFunc:cV},dV=xB+` return atan(a, b); `,pV=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+wB+` return result; `,fV=nn({opSnippet:dV,packedOpSnippet:pV}),mV={kernelName:yo,backendName:"webgl",kernelFunc:fV},AV=Sr+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,yV=Xe({opSnippet:AV}),gV={kernelName:Ao,backendName:"webgl",kernelFunc:yV},kc=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,c=e.dilationWidth,u=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) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let k=">=";this.userCode=` const ivec2 strides = ivec2(${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 < ${u}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${h}; wC += ${c}) { 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 ${k} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${r?a?m:A:`wR * ${h} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,w=s%4,b=` if (${f}) { avgValue += dot(values, ones); } else { minMaxValue = ${g}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${p}); const float initializationValue = ${y}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xR, int xC, int d) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${y}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${u}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${v}; wC += 4) { int xC = xCCorner + wC * ${c}; vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), getValue(batch, xR, xC + 3 * ${c}, d) ); ${b} } int xC = xCCorner + ${v}; if (${w===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${b} } else if (${w===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), initializationValue, initializationValue ); ${b} } else if (${w===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), initializationValue ); ${b} } } setOutput(${x}); } `}},CA=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,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let C=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${A}, ${y}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; for (int wD = 0; wD < ${d}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${p}; wR += ${u}) { 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 ${C} 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 * ${f} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let v="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let b=Math.floor(s/4)*4,k=s%4,N=` if (${g}) { avgValue += dot(values, ones); } else { minMaxValue = ${v}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${A}, ${y}); const float initializationValue = ${x}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xD, int xR, int xC, int ch) { if (xC < 0 || xC >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${x}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${d}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${p}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${b}; 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) ); ${N} } int xC = xCCorner + ${b}; if (${k===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${N} } else if (${k===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${h}, ch), initializationValue, initializationValue ); ${N} } else if (${k===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 ); ${N} } } setOutput(${w}); } } `}};function xV(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,c=1;_.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return jn({inputs:{x:a},backend:n});let h=new kc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var wV={kernelName:fs,backendName:"webgl",kernelFunc:xV};function bV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=E.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new CA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var _V={kernelName:ku,backendName:"webgl",kernelFunc:bV},vV=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,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${c}, ${u}); const float avgMultiplier = float(${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 >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},kV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=` const ivec3 pads = ivec3(${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 < ${u}; 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 += ${c}) { 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 IV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,h,c,u),p=new kV(d);return n.runWebGLProgram(p,[a],i.dtype)}var SV={kernelName:Oh,backendName:"webgl",kernelFunc:IV};function NV(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:c}=r,u=E.computePool2DInfo(i.shape,o,l,1,c),h=new vV(u);return n.runWebGLProgram(h,[a],i.dtype)}var TV={kernelName:Dh,backendName:"webgl",kernelFunc:NV};function EV(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Np({a,b:s,transposeA:i,transposeB:o,backend:n})}var CV={kernelName:ms,backendName:"webgl",kernelFunc:EV},RV=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(E.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))); } `}},MV=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(E.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); } `}},FV=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;_.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_.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 c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new MV(r.shape,a.shape,s.shape,u,h,l):new RV(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},$V={kernelName:Ns,backendName:"webgl",kernelFunc:FV},OV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,r=DV(this.rank),a,s=e.map((i,o)=>`sourceLoc.${RA[o]} = start[${o}] + coords.${RA[o]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` `)} `,this.userCode=` ${n} void main() { ${a} setOutput(getSource(${r})); } `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},RA=["x","y","z","w","u","v"];function DV(e){if(e===1)return"sourceLoc";if(e<=6)return RA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var zV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=pn("coords",this.rank),r=pn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=` result.x = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${r[this.rank-1]}; result.y = ${s}; --${r[this.rank-1]}; } `,o=this.rank===1?"":` --${n[this.rank-1]}; if (++${n[this.rank-2]} < ${e[this.rank-2]}) { ++${r[this.rank-2]}; result.z = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${r[this.rank-1]}; result.w = ${s}; } } `,l=this.rank<=4?`sourceLoc = coords + ${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[u]} = ${n[u]} + start[${u}];`).join(` `);this.userCode=` uniform int start[${this.rank}]; void main() { ${t} coords = getOutputCoords(); ${t} sourceLoc; ${l} vec4 result = vec4(0.); ${i} ${o} setOutput(result); } `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function PV(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=un.computeFlatOffset(t,_.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function Ic(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=un.parseSliceParams(a,s,i);if(un.assertParamsValid(a,o,l),_.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=EW(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=un.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zV(l):new OV(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),PV(a,o,l,n)}var LV={kernelName:Jo,backendName:"webgl",kernelFunc:Ic},WV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;_.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,x)=>g*x),l=E.getReshaped(a.shape,s,o),c=E.getPermuted(l.length,s.length),u=E.getReshapedPermuted(a.shape,s,o),h=E.getSliceBeginCoords(i,s.length),d=E.getSliceSize(u,i,s.length),p=[],f=fe({inputs:{x:a},backend:n,attrs:{shape:l}}),m=fn({inputs:{x:f},backend:n,attrs:{perm:c}}),A=fe({inputs:{x:m},backend:n,attrs:{shape:u}}),y=Ic({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},BV={kernelName:Iu,backendName:"webgl",kernelFunc:WV};function VV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=P3(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var jV={kernelName:zh,backendName:"webgl",kernelFunc:VV},UV="return float(a != b);",h7=nn({opSnippet:UV,dtype:"bool"}),HV={kernelName:Wo,backendName:"webgl",kernelFunc:h7};function Sc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return jn({inputs:{x:a.complexTensorInfos.real},backend:n})}var GV={kernelName:sd,backendName:"webgl",kernelFunc:Sc},qV="return float(int(x));";function XV(e,t){let n=new Ga(e.shape,qV),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function MA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return jn({inputs:{x:a},backend:n});let i=Rt(a.shape),o=MA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=qa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Sc({inputs:{input:a},backend:n}),o=MA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!_.hasEncodingLoss(a.dtype,s)){let i=jn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return XV(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",_.getTypedArrayFromDType("bool",1)),o=h7({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var KV={kernelName:As,backendName:"webgl",kernelFunc:MA},d7="return ceil(x);",ZV=Xe({opSnippet:d7,packedOpSnippet:d7,cpuKernelImpl:hW}),YV={kernelName:ys,backendName:"webgl",kernelFunc:ZV},JV=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=` uniform float minVal; 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float im = abs(getImagAtOutCoords()); float mx = max(re, im); // sadly the length function in glsl is not underflow-safe // (at least not on Intel GPUs). 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${n} }`:a?x=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:x=` float activation(float x) { ${n} } `,v="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${x} const ivec2 strides = ivec2(${o}, ${l}); const ivec2 pads = ivec2(${s}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${g}]; ivec2 xRCCorner = ivec2(coords[${A}], coords[${y}]) * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${h}; wR++) { int xR = xRCorner + wR * ${c}; 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 < ${p}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${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; ${w} ${v} setOutput(result); } `}},cj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${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 < ${u}; 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 * ${c}; 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); } `}},hj=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:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=dn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,x="";for(let v=0;v<=1;v++)for(let w=0;w<=1;w++)x+=` blockIndex = rc.y + ${w}; pos = rc.x + ${v}; if(blockIndex < ${e[1]} && pos < ${e[0]}) { offsetY = int(blockIndex / (${l})) * ${i} - ${p}; d0 = offsetY + ${u} * (pos / ${f}); if(d0 < ${t[y]} && d0 >= 0) { offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.); d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.)); if(d1 < ${t[g]} && d1 >= 0) { ch = int(mod(float(pos), ${a}.)); if (${A}) { innerDims = vec2(d1, ch); result[${v*2+w}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${v*2+w}] = getChannel( getA(ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec2 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${x} ${m.output} = result; } `}};function A7({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&u>s7,x=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=fe({inputs:{x:e},backend:r,attrs:{shape:[1,v,n.inChannels]}}),b=fe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),k=Np({a:w,b,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=fe({inputs:{x:k},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(b),y.push(k)}else{let v=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},b=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,_.assert(gc(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let k=fe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(k);let N=Np({a:w,b:k,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=r.texData.get(N.dataId);_.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=b,C.shape=n.outShape,A=jn({inputs:{x:N},backend:r}),A.shape=n.outShape,y.push(N)}for(let v of y)r.disposeIntermediateTensorInfo(v);return A}function y7({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,A=d*h,y=[m,A],g=!0,x=!1,v=[],w=fe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),b=fe({inputs:{x:t},backend:r,attrs:{shape:[1,m,_.sizeFromShape(t.shape)/m]}});v.push(w),v.push(b);let k=new hj(y,w.shape,n),N=r.runWebGLProgram(k,[w],"float32"),C=fe({inputs:{x:N},backend:r,attrs:{shape:[1,y[0],y[1]]}});v.push(N),v.push(C);let F=a!=null,O=s!=null,L=o==="leakyrelu",V=o?kp(o,!0):null,j=new e7(C.shape,b.shape,[1,A,n.outChannels],g,x,F,V,O,L),U=[C,b];if(a&&U.push(a),O&&U.push(s),L){let Y=r.makeTensorInfo([],"float32",_.createScalarValue(i,"float32"));U.push(Y),v.push(Y)}let X=r.runWebGLProgram(j,U,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=fe({inputs:{x:X},backend:r,attrs:{shape:G}});v.push(X);for(let Y of v)r.disposeIntermediateTensorInfo(Y);return ee}function dj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!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=A7({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=y7({x:a,filter:s,convInfo:d,backend:n});else{let m=new m7(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=fe({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var pj={kernelName:gs,backendName:"webgl",kernelFunc:dj},fj=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); } `}},mj=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,c=s?2:3,u=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${u}]; ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${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); } `}},Aj=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 >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},yj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${l}, ${c}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyFCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; float dotProd = 0.0; for (int wF = 0; wF < ${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 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function gj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new fj(d);return n.runWebGLProgram(p,[a,s],"float32")}var xj={kernelName:Lh,backendName:"webgl",kernelFunc:gj};function wj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=E.convertConv2DDataFormat(c),d=E.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new mj(d);return n.runWebGLProgram(p,[a,s],"float32")}var bj={kernelName:xs,backendName:"webgl",kernelFunc:wj};function _j(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=E.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new cj(c);return n.runWebGLProgram(u,[a,s],"float32")}var vj={kernelName:Nu,backendName:"webgl",kernelFunc:_j};function kj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=E.computeConv3DInfo(a.shape,l,i,1,o),u=new Aj(c);return n.runWebGLProgram(u,[a,s],"float32")}var Ij={kernelName:Wh,backendName:"webgl",kernelFunc:kj};function Sj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=E.computeConv3DInfo(l,s.shape,o,1,i),u=new yj(c);return n.runWebGLProgram(u,[a,s],"float32")}var Nj={kernelName:Bh,backendName:"webgl",kernelFunc:Sj},Tj=Q3+` return cos(x); `,Ej=Xe({opSnippet:Tj}),Cj={kernelName:ws,backendName:"webgl",kernelFunc:Ej},Rj=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,Mj=Xe({opSnippet:Rj}),Fj={kernelName:xo,backendName:"webgl",kernelFunc:Mj},$j=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,x,v]=h>1?[`${(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(${g}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int y = coords[1]; int x = coords[2]; int d = coords[3]; // get box vals float y1 = getBoxes(b,0); float x1 = getBoxes(b,1); float y2 = getBoxes(b,2); float x2 = getBoxes(b,3); // get image in batch index int bInd = round(getBoxInd(b)); if(bInd < 0 || bInd >= ${s}) { return; } float height_scale = ${A}; float width_scale = ${x}; float in_y = ${y}; if( in_y < 0.0 || in_y > ${p} ) { setOutput(float(${a})); return; } float in_x = ${v}; 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); } } `}},Dj=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new $j(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},Oj={kernelName:wo,backendName:"webgl",kernelFunc:Dj},w7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${g7(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() { ${lt(r)} coords = getOutputCoords(); int end = ${x7(r,"coords")}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${o}; ${x7(r,"coords")} = idx; val += getX(${g7(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 g7(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 ${e} is not yet supported`)}function x7(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function zj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=E.getAxesPermutation([s],l),u=a;c!=null&&(u=fn({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=E.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=jn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new w7(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new w7(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=E.getUndoAxesPermutation(c),m=fn({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var Pj={kernelName:bs,backendName:"webgl",kernelFunc:zj};function Lj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=P3(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=cW(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Wj={kernelName:Vh,backendName:"webgl",kernelFunc:Lj},Bj=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 Vj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.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],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new Bj(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var jj={kernelName:bo,backendName:"webgl",kernelFunc:Vj},b7=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,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:a?A=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:A=` float activation(float x) { ${n} } `,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${A} const ivec2 strides = ivec2(${c}, ${u}); const ivec2 pads = ivec2(${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; ${g} ${y} setOutput(result); } `}},_7=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.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,c=e.padInfo.left,u=e.strideHeight,h=e.strideWidth,d=e.dilationHeight,p=e.dilationWidth,f=e.filterHeight,m=e.filterWidth,A=m,y=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let w=0;w=0 && xR < ${i}) { `;for(let b=0;b= 0 && xCOffset < ${o}) { xTexelC${k} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { xTexelC${k}.zw = vec2(0.0); } } `,p===1&&k>0?y+=` xC${k} = vec4(xTexelC${k-2}.zw, xTexelC${k}.xy); `:y+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < ${o}) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { previous.zw = vec2(0.0); } xC${k} = vec4(previous.zw, xTexelC${k}.xy); } else { xC${k} = vec4(0.0, 0.0, xTexelC${k}.xy); } `):y+=` if (xC >= 0 && xC < ${o}) { xTexelC${k} = getX(batch, xR, xC, d1); if (xC + 1 >= ${o}) { xTexelC${k}.zw = vec2(0.0); } } xC${k} = xTexelC${k}; `,k+1= 0 && xCOffset < ${o}) { xTexelC${k+2} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { xTexelC${k+2}.zw = vec2(0.0); } } `,p>1&&(y+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < ${o}) { xTexelC${k} = getX(batch, xR, xCOffset, d1); } `),y+=` xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+2}.xy); `):N===1?y+=` xC${k+1} = xTexelC${k}; `:y+=` xCOffset = xC + ${N}; if (xCOffset >= 0 && xCOffset < ${o}) { xTexelC${k+2} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= ${o}) { xTexelC${k+2}.zw = vec2(0.0); } } xC${k+1} = xTexelC${k+2}; `}}else k= 0 && xCOffset < ${o}) { xTexelC${k} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= ${o}) { xTexelC${k}.zw = vec2(0.0); } } if(xC + 1 >= 0 && xC + 1 < ${o}) { xTexelC${k+2} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= ${o}) { xTexelC${k+2}.zw = vec2(0.0); } } xC${k} = vec4(xTexelC${k}.zw, xTexelC${k+2}.zw); `,k+1= 0 && xCOffset < ${o}) { final = getX(batch, xR, xCOffset, d1); } xC${k+1} = vec4(xTexelC${k+2}.xy, final.xy); `)):(y+=` if(xC >= 0 && xC < ${o}) { xTexelC${k} = getX(batch, xR, xC, d1); if (xC + 1 >= ${o}) { xTexelC${k}.zw = vec2(0.0); } } xCOffset = xC + ${h}; if(xCOffset >= 0 && xCOffset < ${o}) { xTexelC${k+2} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= ${o}) { xTexelC${k+2}.zw = vec2(0.); } } xC${k} = vec4( xTexelC${k}.xy, xTexelC${k+2}.xy); `,k+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=E.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new _7(h):d=new b7(h),n.runWebGLProgram(d,[a,s],"float32")}var Hj={kernelName:_s,backendName:"webgl",kernelFunc:Uj},Gj=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 >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},qj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${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 * ${o} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function Xj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=E.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new Gj(h);return n.runWebGLProgram(d,[a,s],"float32")}var Kj={kernelName:jh,backendName:"webgl",kernelFunc:Xj};function Zj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=E.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new qj(h);return n.runWebGLProgram(d,[a,s],"float32")}var Yj={kernelName:Uh,backendName:"webgl",kernelFunc:Zj},Jj=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function Qj(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=_.sizeFromShape(r.shape),i=fe({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new Jj(s),l=n.runWebGLProgram(o,[i],i.dtype),c=fe({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var eU={kernelName:Hh,backendName:"webgl",kernelFunc:Qj},tU=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=` const ivec2 strides = ivec2(${a}, ${s}); const ivec2 pads = ivec2(${u}, ${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 * ${c}; if (wIn >= 0 && wIn < ${n}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); float val = xVal + wVal; if (val > curVal) { curVal = val; } } } } } float result = curVal; setOutput(result); } `}};function nU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=E.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new tU(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=fe({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var rU={kernelName:Tu,backendName:"webgl",kernelFunc:nU};function aU(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=E.decodeEinsumEquation(a,s.length);E.checkEinsumDimSizes(i.length,l,s);let{path:c,steps:u}=E.getEinsumComputePath(o,l),h=u.length,d=null,p=i.length,f=[];for(let m=0;m=0&&(d=Sp({inputs:{x:d},backend:n,attrs:{axis:c[m]-(i.length-p),keepDims:!1}}),f.push(d)),p--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var sU={kernelName:Xh,backendName:"webgl",kernelFunc:aU},iU="return (x >= 0.0) ? x : (exp(x) - 1.0);",oU=` 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; `,lU=Xe({opSnippet:iU,packedOpSnippet:oU}),uU={kernelName:_o,backendName:"webgl",kernelFunc:lU},cU="return (b >= 1.0) ? a : a * (b + 1.0);",hU=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,dU=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(hU,r.shape,a.shape):new Bl(cU,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},pU={kernelName:Kh,backendName:"webgl",kernelFunc:dU},fU=` return vec4(equal(a, b)); `,mU="return float(a == b);",AU=nn({opSnippet:mU,packedOpSnippet:fU,dtype:"bool"}),yU={kernelName:ko,backendName:"webgl",kernelFunc:AU},gU=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${E.ERF_P}; float a1 = ${E.ERF_A1}; float a2 = ${E.ERF_A2}; float a3 = ${E.ERF_A3}; float a4 = ${E.ERF_A4}; float a5 = ${E.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,xU=Xe({opSnippet:gU}),wU={kernelName:vo,backendName:"webgl",kernelFunc:xU},v7="return exp(x);",k7=Xe({opSnippet:v7,packedOpSnippet:v7,cpuKernelImpl:pW}),bU={kernelName:ks,backendName:"webgl",kernelFunc:k7};function FA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(_.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),fe({inputs:{x:s},backend:r,attrs:{shape:o}})}var _U={kernelName:Io,backendName:"webgl",kernelFunc:FA},I7="return exp(x) - 1.0;",vU=Xe({opSnippet:I7,packedOpSnippet:I7,cpuKernelImpl:fW}),kU={kernelName:So,backendName:"webgl",kernelFunc:vU},S7=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}; float unaryOpComplex(float real, float expR, float imag, float expI) { ${i} } float mulMatDFT(int batch, int index) { float indexRatio = float(index) / float(${r}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; for (int i = 0; i < ${r}; i++) { // x = (-2|2 * PI / N) * index * i; float x = exponentMultiplierTimesIndexRatio * float(i); float expR = cos(x); float expI = sin(x); float real = getReal(batch, i); float imag = getImag(batch, i); result += unaryOpComplex(real, expR, imag, expI) / ${s}; } return result; } void main() { ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } `}};function N7(e,t,n){let r=n.texData.get(e.dataId),a=_.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=fe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new S7("real",l,t),u=new S7("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=qa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=fe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function IU(e){let{inputs:t,backend:n}=e,{input:r}=t;return N7(r,!1,n)}var SU={kernelName:Zh,backendName:"webgl",kernelFunc:IU},NU=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=` uniform float value; void main() { // Input can be obtained from uniform value. setOutput(value); } `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function $A(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||_.inferDtype(a),s==="string"){let i=_.getArrayFromDType(s,_.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new NU(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var TU={kernelName:Eu,backendName:"webgl",kernelFunc:$A},EU=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int coordX = ${t} - x; float outputValue; if(coordX >= 0 && coordX < ${t}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}},CU={kernelName:No,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new EU(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},T7="return floor(x);",RU=Xe({opSnippet:T7,packedOpSnippet:T7,cpuKernelImpl:mW}),MU={kernelName:Is,backendName:"webgl",kernelFunc:RU},FU=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); if (ib != 0) { // Windows (D3D) wants guaranteed non-zero int division at compile-time. return float(idiv(ia, ib, s)); } else { return NAN; } `,$U=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); ivec4 result = ivec4(0); vec4 s = sign(a) * sign(b); // Windows (D3D) wants guaranteed non-zero int division at compile-time. if (cond[0]) { result[0] = idiv(ia[0], ib[0], s[0]); } if (cond[1]) { result[1] = idiv(ia[1], ib[1], s[1]); } if (cond[2]) { result[2] = idiv(ia[2], ib[2], s[2]); } if (cond[3]) { result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); `,DU=nn({opSnippet:FU,packedOpSnippet:$U,dtype:"int32"}),OU={kernelName:Ss,backendName:"webgl",kernelFunc:DU},zU=class{constructor(e){this.variableNames=["A"];let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}},PU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } result[row * 2 + col] = floor(value * 255.0 + 0.5); } } ${t.output} = result; } `}},WU={kernelName:dd,backendName:"webgl",kernelFunc:LU},jl;function LU(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&(jl==null&&(jl=document.createElement("canvas").getContext("2d")),jl.canvas.width=l,jl.canvas.height=c,jl.drawImage(a,0,0,l,c),a=jl.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=rr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new PU(h):new zU(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function BU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=E.convertConv2DDataFormat(u),A=E.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=A7({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=y7({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let v=i!=null,w=o!=null,b=p==="leakyrelu",k=p?kp(p,!1):null,N=new m7(A,v,k,w,b),C=[a,s];if(i&&C.push(i),o&&C.push(o),b){let F=n.makeTensorInfo([],"float32",_.createScalarValue(f,"float32"));C.push(F),g.push(F)}y=n.runWebGLProgram(N,C,"float32")}let x=fe({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var VU={kernelName:oi,backendName:"webgl",kernelFunc:BU};function jU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=u;m==null&&(m=[1,1]),_.assert(E.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=E.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?kp(d,y):null,x=[a,s],v=i!=null,w=o!=null,b=d==="leakyrelu";if(v&&x.push(i),w&&x.push(o),b){let C=n.makeTensorInfo([],"float32",_.createScalarValue(p,"float32"));x.push(C),f.push(C)}let k;y?k=new _7(A,v,g,w,b):k=new b7(A,v,g,w,b);let N=n.runWebGLProgram(k,x,"float32");return f.forEach(C=>n.disposeIntermediateTensorInfo(C)),N}var UU={kernelName:li,backendName:"webgl",kernelFunc:jU},HU=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=lt(t.length),a=lt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=` ${r} strides = ${r}(${this.strides}); void main() { ${a} coords = getOutputCoords(); int flattenIndex = 0; for (int j = 0; j < ${this.sliceDim}; j++) { int index = round(getIndices(coords[0], j)); flattenIndex += index * ${s}; } setOutput(getX(flattenIndex, coords[1])); } `}};function GU(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=E.prepareAndValidate(r,a),h=fe({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=fe({inputs:{x:r},backend:n,attrs:{shape:[_.sizeFromShape(r.shape)/c,c]}}),p=new HU(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=fe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var qU={kernelName:Eo,backendName:"webgl",kernelFunc:GU},KU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=lt(this.rank),r=XU(e,2);this.userCode=` void main() { ${n} resRC = getOutputCoords(); setOutput(getA(${r})); } `}};function XU(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;an.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(c.outputShape,v.dtype,v.values)}let m=new KU(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=fe({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var YU={kernelName:To,backendName:"webgl",kernelFunc:ZU},JU="return float(a > b);",QU=` return vec4(greaterThan(a, b)); `,eH=nn({opSnippet:JU,packedOpSnippet:QU,cpuKernelImpl:yW,dtype:"bool"}),tH={kernelName:Co,backendName:"webgl",kernelFunc:eH},nH="return float(a >= b);",rH=` return vec4(greaterThanEqual(a, b)); `,aH=nn({opSnippet:nH,packedOpSnippet:rH,dtype:"bool"}),sH={kernelName:Ts,backendName:"webgl",kernelFunc:aH};function iH(e){let{inputs:t,backend:n}=e,{input:r}=t;return N7(r,!0,n)}var oH={kernelName:Yh,backendName:"webgl",kernelFunc:iH},lH="return float(!isnan(x) && !isinf(x));",uH=Xe({opSnippet:lH,dtype:"bool"}),cH={kernelName:Ro,backendName:"webgl",kernelFunc:uH},hH="return float(isinf(x));",dH=Xe({opSnippet:hH,dtype:"bool"}),pH={kernelName:Mo,backendName:"webgl",kernelFunc:dH},fH="return float(isnan(x));",mH=Xe({opSnippet:fH,dtype:"bool"}),AH={kernelName:Fo,backendName:"webgl",kernelFunc:mH},yH="return float(a < b);",gH=` return vec4(lessThan(a, b)); `,xH=nn({opSnippet:yH,packedOpSnippet:gH,cpuKernelImpl:gW,dtype:"bool"}),wH={kernelName:$o,backendName:"webgl",kernelFunc:xH},bH="return float(a <= b);",_H=` return vec4(lessThanEqual(a, b)); `,vH=nn({opSnippet:bH,packedOpSnippet:_H,dtype:"bool"}),kH={kernelName:Do,backendName:"webgl",kernelFunc:vH};function IH(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=xW(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var SH={kernelName:Qh,backendName:"webgl",kernelFunc:IH},NH=`if (x < 0.0) return NAN; return log(x);`,TH=` vec4 result = log(x); vec4 isNaN = vec4(lessThan(x, vec4(0.0))); result.r = isNaN.r == 1.0 ? NAN : result.r; result.g = isNaN.g == 1.0 ? NAN : result.g; result.b = isNaN.b == 1.0 ? NAN : result.b; result.a = isNaN.a == 1.0 ? NAN : result.a; return result; `,EH=Xe({opSnippet:NH,packedOpSnippet:TH,cpuKernelImpl:wW}),CH={kernelName:Rs,backendName:"webgl",kernelFunc:EH},RH="return log(1.0 + x);",MH=Xe({opSnippet:RH}),FH={kernelName:Oo,backendName:"webgl",kernelFunc:MH},$H="return float(a >= 1.0 && b >= 1.0);",DH=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,OH=nn({opSnippet:$H,packedOpSnippet:DH,dtype:"bool"}),zH={kernelName:zo,backendName:"webgl",kernelFunc:OH},PH="return float(!(x >= 1.0));",LH=Xe({opSnippet:PH}),WH={kernelName:Cu,backendName:"webgl",kernelFunc:LH},BH="return float(a >= 1.0 || b >= 1.0);",VH=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,jH=nn({opSnippet:BH,packedOpSnippet:VH,dtype:"bool"}),UH={kernelName:Ru,backendName:"webgl",kernelFunc:jH},HH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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 * ${o}; setOutput(val); } `}},GH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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); } `}},qH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new GH(a.shape,s,i,o,l):new HH(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},XH={kernelName:Mu,backendName:"webgl",kernelFunc:qH},KH=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); } `}},ZH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new KH(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},YH={kernelName:ed,backendName:"webgl",kernelFunc:ZH};function JH(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=fe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Fi(i,e.dtype,"max",r),l=fe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function E7(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=E.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,x=new Array(o);for(let b=0;b`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return jn({inputs:{x:a},backend:n});let h=new kc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var sG={kernelName:$s,backendName:"webgl",kernelFunc:aG};function iG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=E.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new CA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var oG={kernelName:Fu,backendName:"webgl",kernelFunc:iG},lG=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=` const ivec2 pads = ivec2(${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 * ${s} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},uG=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=` const ivec3 pads = ivec3(${u}, ${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 < ${c}; 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} * ${c} + wR * ${c} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function cG(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,h,c,u),p=new CA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new uG(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var hG={kernelName:nd,backendName:"webgl",kernelFunc:cG};function dG(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:c,pad:u,dimRoundingMode:h}=r,d=E.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new kc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new lG(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var pG={kernelName:td,backendName:"webgl",kernelFunc:dG};function fG(e,t,n,r){let a=new kc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new kc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var mG={kernelName:rd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;_.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];_.assert(E.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,s,c,i),[h,d]=fG(r,o,u,l);return[h,d]}};function AG(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=fe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Fi(i,"float32","mean",r),l=fe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var yG={kernelName:Ds,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=_.parseAxisParam(s,r.shape),c=l,u=E.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let x=i.texData.get(f.dataId).values,v=new Array(o);for(let k=0;kc[0]+e[u]+c[1]);let r=e.length,a=lt(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).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})); } `}},IG=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=lt(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${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()}), ${u}); ${o[r-1]} += 1; if(${c}) { ${p} result[1] = getChannel(getX(${l.join()}), ${u}); } `}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()}), ${u}); ${o[r-1]} += 1; if(${c}) { ${p} result[1] = getChannel(getX(${l.join()}), ${u}); } rc = outputLoc; ${o[r-2]} += 1; if(${o[r-2]} < ${this.outputShape[r-2]}) { ${p} result[2] = getChannel(getX(${l.join()}), ${u}); ${o[r-1]} += 1; if(${c}) { ${p} result[3] = getChannel(getX(${l.join()}), ${u}); } } `}this.userCode=` const ${a} start = ${a}(${s}); const ${a} end = ${a}(${i}); void main() { ${a} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${d} setOutput(result); } `}},SG=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IG(r.shape,a,s):new kG(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},NG={kernelName:Ps,backendName:"webgl",kernelFunc:SG},TG=`if (b == 0.0) return NAN; return mod(a, b);`,EG=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+vp+` return result; `,CG=nn({opSnippet:TG,packedOpSnippet:EG}),RG={kernelName:Po,backendName:"webgl",kernelFunc:CG},MG=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)}}},FG=` if (a == b) { return 1.0; }; return a / b;`,$G=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; if(a.x == b.x) { result.x = 1.; } if(a.y == b.y) { result.y = 1.; } if(a.z == b.z) { result.z = 1.; } if(a.w == b.w) { result.w = 1.; } return result; `,C7=nn({opSnippet:FG,packedOpSnippet:$G,checkOutOfBounds:!0}),DG={kernelName:vs,backendName:"webgl",kernelFunc:C7},R7="return a - b;",M7=nn({opSnippet:R7,packedOpSnippet:R7,supportsComplex:!0,cpuKernelImpl:MW}),OG={kernelName:ni,backendName:"webgl",kernelFunc:M7};function F7(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=_.parseAxisParam([s],a.shape),o=E7({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=E.expandShapeToKeepDim(o.shape,i),c=fe({inputs:{x:o},backend:n,attrs:{shape:l}}),u=M7({inputs:{a,b:c},backend:n}),h=k7({inputs:{x:u},backend:n}),d=Sp({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=fe({inputs:{x:d},backend:n,attrs:{shape:l}}),f=C7({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var zG={kernelName:ei,backendName:"webgl",kernelFunc:F7};function PG(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:F7({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new MG(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var LG={kernelName:ad,backendName:"webgl",kernelFunc:PG},$7="return -x;";function WG(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=IW(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,$7):a=new Ga(r.shape,$7),n.runWebGLProgram(a,[r],r.dtype)}var BG={kernelName:Lo,backendName:"webgl",kernelFunc:WG},VG=Hr.nonMaxSuppressionV3Impl;function jG(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=VG(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var UG={kernelName:Bo,backendName:"webgl",kernelFunc:jG},HG=Hr.nonMaxSuppressionV4Impl;function GG(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=HG(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var qG={kernelName:Vo,backendName:"webgl",kernelFunc:GG},XG=Hr.nonMaxSuppressionV5Impl;function KG(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=XG(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var ZG={kernelName:jo,backendName:"webgl",kernelFunc:KG},YG=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${r}), float(${n}), float(index == coords.y))); } `}},JG=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=_.sizeFromShape(a.shape),c=new YG(l,s,i,o),u=fe({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=fe({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},QG={kernelName:Ws,backendName:"webgl",kernelFunc:JG};function Rp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Sc({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=qa({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 eq={kernelName:il,backendName:"webgl",kernelFunc:Rp};function D7(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=Sc({inputs:{input:r},backend:n}),s=D7({inputs:{x:a},backend:n}),i=Cp({inputs:{input:r},backend:n}),o=Rp({inputs:{x:i},backend:n}),l=qa({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 tq={kernelName:Uo,backendName:"webgl",kernelFunc:D7};function nq(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return FA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{_.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),_.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=FA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=f7({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var rq={kernelName:Ho,backendName:"webgl",kernelFunc:nq},aq=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=lt(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).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)}}},sq=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=lt(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1; if(${c}) { `,r===1?"":`} rc = outputLoc; ${o[r-2]} += 1; if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${o[r-1]} += 1; if(${c}) {`],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)}}},O7=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 sq(a.shape,s,i):new aq(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},iq={kernelName:Bs,backendName:"webgl",kernelFunc:O7},oq=` 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); `,lq=` // 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)); `+vp+` return result; `,uq=nn({opSnippet:oq,packedOpSnippet:lq}),cq={kernelName:Vs,backendName:"webgl",kernelFunc:uq};function hq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=_.parseAxisParam(s,a.shape),u=c,h=E.getAxesPermutation(u,o),d=a;h!=null&&(d=fn({inputs:{x:a},backend:n,attrs:{perm:h}}),u=E.getInnerMostAxes(u.length,o),l.push(d)),E.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=SW(d.shape,d.dtype,f,u);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=E.computeOutAndReduceShapes(d.shape,u),A=_.sizeFromShape(m),y=fe({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=yd(a.dtype),x=Fi(y,g,"prod",n);p=fe({inputs:{x},backend:n,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(p);let f=E.expandShapeToKeepDim(p.shape,c);p=fe({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var dq={kernelName:Go,backendName:"webgl",kernelFunc:hq},z7=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=NW(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},pq={kernelName:$u,backendName:"webgl",kernelFunc:z7},fq="return 1.0 / x;",mq=Xe({opSnippet:fq}),Aq={kernelName:qo,backendName:"webgl",kernelFunc:mq},yq=Sr+` return (x < 0.0) ? 0.0 : x; `,gq=` 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; `,xq=Xe({opSnippet:yq,packedOpSnippet:gq}),wq={kernelName:Us,backendName:"webgl",kernelFunc:xq},bq=Sr+` return (x < 0.0) ? 0.0 : min(6.0, x); `,_q=` 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; `,vq=Xe({opSnippet:bq,packedOpSnippet:_q}),kq={kernelName:Gs,backendName:"webgl",kernelFunc:vq},Iq=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 c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[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( ${c[0]/u[0]}, ${c[1]/u[1]}); const vec2 inputShapeRC = vec2(${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); } `}},Sq=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 c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[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( ${c[0]/u[0]}, ${c[1]/u[1]}, ${c[1]/u[1]}); const vec3 inputShapeRC = vec3(${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 Nq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Sq(a.shape,l,c,s,i):new Iq(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var Tq={kernelName:Hs,backendName:"webgl",kernelFunc:Nq},Eq=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],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,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(${c}); const float widthScale = float(${u}); const float invHeightScale = float(${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 Cq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Eq(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Rq={kernelName:od,backendName:"webgl",kernelFunc:Cq},Mq=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 c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[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( ${c[0]/u[0]}, ${c[1]/u[1]}); const vec2 inputShapeRC = vec2(${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 Fq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new Mq(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var $q={kernelName:Du,backendName:"webgl",kernelFunc:Fq},Dq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${u}); const float invHeightScale = float(${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), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function Oq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Dq(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var zq={kernelName:id,backendName:"webgl",kernelFunc:Oq},Pq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } `;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=lt(n);this.userCode=` void main() { ${s} coords = getOutputCoords(); setOutput(getX(${a})); } `}},Lq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=pn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=lt(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${e[0]} - rc - 1), ${e[0]} - rc - 1); if(${a}){ result.g = getChannel(getX(${e[0]} - (rc + 1) - 1), ${e[0]} - (rc + 1) - 1); } setOutput(result); } `:this.userCode=` void main() { ${i} rc = getOutputCoords(); vec4 result = vec4(0.); result.r = ${o(r.slice())}; if(${a}){ result.g = ${l(r.slice())}; } if(${s}) { result.b = ${c(r.slice())}; if(${a}) { result.a = ${u(r.slice())}; } } setOutput(result); } `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function Wq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=_.parseAxisParam(s,a.shape);if(i===0)return jn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Lq(a.shape,o):new Pq(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Bq={kernelName:qs,backendName:"webgl",kernelFunc:Wq},Vq=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float 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i=Array.from(n.readSync(a.dataId)),o=n.readSync(r.dataId),l=Array.from(n.readSync(s.dataId)),[c,u,h]=CW(o,r.shape,r.dtype,i,l);return[n.makeTensorInfo(u,r.dtype,c),n.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var _X={kernelName:ld,backendName:"webgl",kernelFunc:bX};function vX(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=E.calculateShapes(s,a,o),d=!1,p=new P7(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=fe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var kX={kernelName:ud,backendName:"webgl",kernelFunc:vX};function IX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=_.parseAxisParam(i,a.shape)[0],l=E.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=Ic({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var SX={kernelName:nl,backendName:"webgl",kernelFunc:IX},NX="return sqrt(x);",TX=Xe({opSnippet:NX}),EX={kernelName:Js,backendName:"webgl",kernelFunc:TX},CX="return x * x;",RX=Xe({opSnippet:CX}),MX={kernelName:zu,backendName:"webgl",kernelFunc:RX},L7="return (a - b) * (a - b);",FX=nn({opSnippet:L7,packedOpSnippet:L7}),$X={kernelName:ti,backendName:"webgl",kernelFunc:FX};function DX({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=Sr+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new Ga(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var OX={kernelName:Fa,backendName:"webgl",kernelFunc:DX},zX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=lt(n.length),s=lt(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` ${a} begin = 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LX={kernelName:rl,backendName:"webgl",kernelFunc:PX},WX="return tan(x);",BX=Xe({opSnippet:WX}),VX={kernelName:ri,backendName:"webgl",kernelFunc:BX},jX=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,UX=Xe({opSnippet:jX}),HX={kernelName:ai,backendName:"webgl",kernelFunc:UX},qX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a5){let o=n.readSync(a.dataId).map(u=>_.decodeString(u)),l=Be(a.shape,a.dtype,o),c=FW(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new qX(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var XX={kernelName:Ma,backendName:"webgl",kernelFunc:W7};function KX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=$W(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var ZX={kernelName:al,backendName:"webgl",kernelFunc:KX},YX=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(${o} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * float(int(float(-inCoord / sz2))) + inCoord; } inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0; } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; inCoord -= sz2 * float(int(float(inCoord / sz2))); if 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float yf = float(y); float a1 = getTransforms(batch, 0); float a2 = getTransforms(batch, 1); float a3 = getTransforms(batch, 2); float b1 = getTransforms(batch, 3); float b2 = getTransforms(batch, 4); float b3 = getTransforms(batch, 5); float c1 = getTransforms(batch, 6); float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = float(${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) * 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tK={kernelName:hd,backendName:"webgl",kernelFunc:eK};function nK(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],c=new Array(o-1),u=0;for(let m=0;mn.disposeIntermediateTensorInfo(m)),f}var rK={kernelName:sl,backendName:"webgl",kernelFunc:nK},aK=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",c=Math.floor(n/4)*4,u=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 < ${c}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 ); ${h} } int inIdx = inOffset + ${c}; if (${u===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 ); ${h} } else if (${u===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 ); ${h} } else if (${u===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 sK(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],c=0,u=E.getAxesPermutation([c],o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(h.shape,c,i),p=_.sizeFromShape([h.shape[c]]),f=fe({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let 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iK={kernelName:Pu,backendName:"webgl",kernelFunc:sK},oK=[XH,YH,FB,DB,PB,BB,jB,GB,XB,ZB,eV,nV,sV,lV,mV,hV,gV,_V,wV,SV,TV,CV,$V,BV,jV,KV,YV,tj,aj,fB,uj,xj,bj,pj,Ij,Nj,vj,Cj,Fj,Oj,Pj,Wj,jj,Kj,Yj,Hj,eU,rU,sU,uU,pU,yU,wU,bU,_U,kU,SU,TU,CU,MU,OU,WU,VU,UU,qU,YU,tH,sH,pB,oH,oj,cH,pH,AH,AB,wH,kH,SH,FH,CH,zH,WH,UH,QH,oG,sG,hG,pG,mG,rG,yG,xG,vG,NG,RG,LG,bB,BG,UG,qG,ZG,HV,QG,tq,rq,iq,cq,gB,dq,pq,GV,DG,Aq,kq,wq,vB,Tq,Rq,$q,zq,Bq,jq,Gq,Kq,Yq,eX,rX,iX,uX,dX,mX,LV,zG,gX,wX,_X,kX,SX,EX,MX,$X,OX,LX,OG,CB,VX,HX,XX,ZX,QX,RB,tK,rK,iK,eq];for(let e of oK)ui(e);var Fn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Fn||(Fn={}));var Nc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid"})(Nc||(Nc={}));var B7;function 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compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Er("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 Er("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 Er("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 B("Legacy serialization format not supported yet.");a=t}else _.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 De(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=Fr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("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 B("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";re.registerClass(Kl);function pae(e){return new ga(e)}function fae(e){return new Kl(e)}function mae(e,t){return t==null&&(t={}),hae(e,t)}function d6(e){return b6(e)}function Aae(e,t){gr.registerCallbackConstructor(e,t)}var On=class extends re.Serializable{getConfig(){return{}}},K6=class extends On{apply(e,t=1){return qte(e,t)}};K6.className="elu";re.registerClass(K6);var Z6=class extends On{apply(e){return Bd(e)}};Z6.className="selu";re.registerClass(Z6);var Y6=class extends On{apply(e){return Ur(e)}};Y6.className="relu";re.registerClass(Y6);var J6=class extends On{apply(e){return z(()=>kl(6,Ur(e)))}};J6.className="relu6";re.registerClass(J6);var Q6=class extends On{apply(e){return e}};Q6.className="linear";re.registerClass(Q6);var e4=class extends On{apply(e){return Tn(e)}};e4.className="sigmoid";re.registerClass(e4);var t4=class extends On{apply(e){return Kte(e)}};t4.className="hardSigmoid";re.registerClass(t4);var n4=class extends On{apply(e){return bi(e)}};n4.className="softplus";re.registerClass(n4);var r4=class extends On{apply(e){return Xte(e)}};r4.className="softsign";re.registerClass(r4);var a4=class extends On{apply(e){return yi(e)}};a4.className="tanh";re.registerClass(a4);var Sy=class extends On{apply(e,t=-1){return uc(e,t)}};Sy.className="softmax";re.registerClass(Sy);var s4=class extends On{apply(e,t=-1){return $d(e,t)}};s4.className="logSoftmax";re.registerClass(s4);var i4=class extends On{apply(e,t=1){return z(()=>Tn(e.mul(t)).mul(e))}};i4.className="swish";re.registerClass(i4);var o4=class extends On{apply(e){return z(()=>P(e,yi(bi(e))))}};o4.className="mish";re.registerClass(o4);function Qa(e){return e.getClassName()}function Ny(e,t={}){return Rc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function es(e){if(e==null){let t={};return t.className="linear",t.config={},Ny(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Ny(t)}else return e instanceof On?e:Ny(e)}function Ty(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 l4=class extends re.Serializable{},jc=class extends l4{constructor(e){super();Ty(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 z(()=>{let t=Rt([1]);return this.hasL1&&(t=se(t,Te(P(this.l1,zt(e))))),this.hasL2&&(t=se(t,Te(P(this.l2,Oc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};jc.className="L1L2";re.registerClass(jc);function yae(e){return Ty(e),new jc({l1:e!=null?e.l1:null,l2:0})}function gae(e){return Ty(e),new jc({l2:e!=null?e.l2:null,l1:0})}var u4={l1l2:"L1L2"};function ut(e){return VA(e)}function c4(e,t={}){return Rc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function xt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in u4?u4[e]:e,config:{}};return c4(t)}else return e instanceof l4?e:c4(e)}var Ey=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ze(e);let n=Ur(e);return this.maxValue!=null&&(n=En(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="ReLU";re.registerClass(Ey);var Cy=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return nc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="LeakyReLU";re.registerClass(Cy);var Ry=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=gt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=xt(e.alphaRegularizer),this.alphaConstraint=Vt(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 B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=st(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(Ct(t),t==="channelsFirst"?Je(e,[0,2,3,1]):e))}function h4(e,t){return z(()=>(Ct(t),t==="channelsFirst"?Je(e,[0,2,3,4,1]):e))}function xae(e,t,n,r=1,a="valid",s,i=1){return z(()=>{if(s==null&&(s=Tr()),Ct(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Je(e,[0,2,1])),a==="causal")throw new De("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=Rr(o,n)),o})}function d4(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=Tr()),Ct(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Dy(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ua.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Je(l,[0,3,1,2])),l})}function wae(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=Tr()),Ct(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=h4(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=_m(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Rr(o,n)),s==="channelsFirst"&&(o=Je(o,[0,4,1,2,3])),o})}var Oy=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Oy.verifyArgs(t),this.rank=e,qt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${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,ar(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=es(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Vt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(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 B(`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 B(`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 B(`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"&&!UA(e.kernelSize,"number",1,3))throw new B(`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:St(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Uc=class extends Oy{constructor(e,t){super(e,t);this.kernel=null,Uc.verifyArgs(t),this.filters=t.filters,qt(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Vt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`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 z(()=>{e=ze(e);let n,r=this.bias==null?null:this.bias.read(),a=Zv(this.activation.getClassName());if(a!=null&&this.rank===2)n=d4(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=xae(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=d4(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=wae(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new De("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(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)}`)}},Hc=class extends Uc{constructor(e){super(2,e);Hc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!UA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Hc.className="Conv2D";re.registerClass(Hc);var Gc=class extends Uc{constructor(e){super(3,e);Gc.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 B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Gc.className="Conv3D";re.registerClass(Gc);var zy=class extends Hc{constructor(e){super(e);if(this.inputSpec=[new Ft({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ft({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=ze(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Jr(o,h,c,this.padding),f=Jr(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Je(n,[0,2,3,1]));let A=Nd(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Je(A,[0,3,1,2])),this.bias!=null&&(A=Rr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=st(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]=Jr(t[r],o,s,this.padding),t[a]=Jr(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};zy.className="Conv2DTranspose";re.registerClass(zy);var Py=class extends Gc{constructor(e){super(e);if(this.inputSpec=[new Ft({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new B("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ft({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=ze(e);if(n.shape.length!==5)throw new B(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=r[o],c=r[s],u=r[i],h=this.kernelSize[0],d=this.kernelSize[1],p=this.kernelSize[2],f=this.strides[0],m=this.strides[1],A=this.strides[2],y=Jr(l,f,h,this.padding),g=Jr(c,m,d,this.padding),x=Jr(u,A,p,this.padding),v=[a,y,g,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Je(n,[0,2,3,4,1]));let w=ob(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=Je(w,[0,4,1,2,3])),this.bias!==null&&(w=Rr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=st(e);let t=e.slice(),n,r,a,s;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3,s=4):(n=4,r=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],h=this.strides[2];return t[n]=this.filters,t[r]=Jr(t[r],c,i,this.padding),t[a]=Jr(t[a],u,o,this.padding),t[s]=Jr(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Py.className="Conv3DTranspose";re.registerClass(Py);var p4=class extends Uc{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 B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("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 B(`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=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Vt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Vt(t.pointwiseConstraint)}build(e){if(e=st(e),e.length{e=ze(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Je(e,[0,2,3,1])),n=Bm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Rr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Je(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=St(this.depthwiseInitializer),e.pointwiseInitializer=St(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseConstraint),e.pointwiseConstraint=Bt(this.pointwiseConstraint),e}};p4.className="SeparableConv";var Ly=class extends p4{constructor(e){super(2,e)}};Ly.className="SeparableConv2D";re.registerClass(Ly);var i0=class extends Uc{constructor(e){super(1,e);i0.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"&&!UA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};i0.className="Conv1D";re.registerClass(i0);var Wy=class extends qe{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 z(()=>{if(e=ze(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}};Wy.className="Cropping2D";re.registerClass(Wy);var By=class extends qe{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,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Wte(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 z(()=>{let n=ze(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Je(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 Je(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}};By.className="UpSampling2D";re.registerClass(By);function bae(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=Tr()),Ct(a);let i=Dy(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=wl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=Je(i,[0,3,1,2])),i})}var Vy=class extends Oy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Vt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 z(()=>{e=ze(e);let n=bae(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Rr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(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=$r(t,this.kernelSize[0],this.padding,this.strides[0]),s=$r(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=St(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseRegularizer),e}};Vy.className="DepthwiseConv2D";re.registerClass(Vy);function f4(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("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 m4(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Cr(2,l));if(t=Je(t,c),s!=null)throw new De("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=Qt(a,-1)),a=Je(a,c)),r&&(t=Bn(t,0),a!=null&&(a=Bn(a,0)));let u=[],h,d=n,p=t.shape[0],f=mr(t),m;a!=null&&(m=mr(a));for(let y=0;ye(g,d));if(a==null)h=x[0],d=x[1];else{let v=z(()=>{let w=m[y],b=Wn(w).sub(w),k=x[0].mul(w).add(d[0].mul(b)),N=d.map((C,F)=>x[1][F].mul(w).add(C.mul(b)));return{output:k,newStates:N}});h=v.output,d=v.newStates}o&&u.push(h)}let A;return o&&(A=cn(u,1)),[h,A,d]})}var Zr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new o0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("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 Ft({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 Cr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){cy(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 z(()=>{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 B(`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 Ft({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new Aa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("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=>Rt([n,r])):this.states_=[Rt([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Rt([n,r])):this.states_[0]=Rt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let r=0;rHt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=f4(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 Ft({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 Mr){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=ze(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`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=m4((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],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return z(()=>{let t=Rt(e.shape);return t=Te(t,[1,2]),t=Dc(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()===Zr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Fr(r,n);return new e(Object.assign(t,{cell:a}))}};Zr.className="RNN";re.registerClass(Zr);var Pc=class extends qe{},l0=class extends Pc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,qt(this.units,"units"),this.activation=es(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(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=st(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0Wn(e),rate:this.dropout,training:r})),0Wn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Kr(P(e,s),this.kernel.read()):a=Kr(e,this.kernel.read()),this.bias!=null&&(a=Rr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(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:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};l0.className="SimpleRNNCell";re.registerClass(l0);var jy=class extends Zr{constructor(e){e.cell=new l0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};jy.className="SimpleRNN";re.registerClass(jy);var u0=class extends Pc{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 B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,qt(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=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(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=st(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 z(()=>{if(e=e,e.length!==2)throw new B(`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],0Wn(e),rate:this.dropout,training:n,count:3})),0Wn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};Uy.className="GRU";re.registerClass(Uy);var qc=class extends Pc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,qt(this.units,"units"),this.activation=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=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(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=st(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 yr{apply(i,o){let l=a.apply([s]),c=new Wp().apply([s]),u=a.apply([s*2]);return i6(i6(l,c),u)}},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 z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0Wn(e),rate:this.dropout,training:n,count:4})),0Wn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Hy.className="LSTM";re.registerClass(Hy);var o0=class extends Pc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i{zi(`RNNCell_${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(Fr(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 hy(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;sl6(t(),n),i=()=>zc(s,t,r);return!a||a<=1?Ht(i().clone()):Array(a).fill(void 0).map(i).map(o=>Ht(o.clone()))}var _ae=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&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("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 z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Rt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new Aa("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 B("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(()=>Rt(a)):this.states_=[Rt(a)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Rt(a)):this.states_[0]=Rt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`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,c=this.filters;o=new(t=class extends yr{apply(u,h){let d=l.apply([c]),p=Ln([c]),f=l.apply([c*2]);return JA([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 z(()=>{if(e.length!==3)throw new B(`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;0Wn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0Wn(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),y=l(a,p,3),g=3,[x,v,w,b]=Lt(this.kernel.read(),i,g),[k,N,C,F]=this.useBias?Lt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,x,k,this.padding),u=this.inputConv(u,v,N,this.padding),h=this.inputConv(h,w,C,this.padding),d=this.inputConv(d,b,F,this.padding);let[O,L,V,j]=Lt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,O),m=this.recurrentConv(m,L),A=this.recurrentConv(A,V),y=this.recurrentConv(y,j);let U=this.recurrentActivation.apply(se(c,f)),X=this.recurrentActivation.apply(se(u,m)),G=se(P(X,s),P(U,this.activation.apply(se(h,A)))),ee=P(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=_ae(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=ca(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Rr(a,n,this.dataFormat):a}recurrentConv(e,t){return ca(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};c0.className="ConvLSTM2DCell";re.registerClass(c0);var Gy=class extends A4{constructor(e){let t=new c0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Gy.className="ConvLSTM2D";re.registerClass(Gy);var h0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r{this.invokeCallHook(e,t);let n=ze(e);if(0l6(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()}};h0.className="Dropout";re.registerClass(h0);var qy=class extends h0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};qy.className="SpatialDropout1D";re.registerClass(qy);var Xy=class extends qe{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,qt(this.units,"units"),this.activation=es(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Vt(e.kernelConstraint),this.biasConstraint=Vt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(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=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=ze(e),r=Zv(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=Rr(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:St(this.kernelInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Xy.className="Dense";re.registerClass(Xy);var Ky=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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qe{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=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.movingMeanInitializer=gt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=gt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Vt(e.betaConstraint),this.gammaConstraint=Vt(e.gammaConstraint),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ft({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=ze(e),a=r.shape,s=a.length,i=Cr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=$i(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!_.arraysEqual(c,Cr(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return Kc(r,A,y,g,x,this.epsilon)}else return 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extends qe{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=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a=t)throw new Error(`Invalid axis: 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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 a8(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Un(d)[0]),u=[];r!=null&&(u=r.map(d=>Un(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((r8(d)||Vse(d)||jse(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&&c.indexOf(d.name)===-1&&u.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 Use(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>Un(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{r.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{r.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return c}var Hse=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Gse=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],qse=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function r8(e){return Hse.indexOf(e.op)>=0}function Vse(e){return Gse.indexOf(e.op)>=0}function jse(e){return qse.indexOf(e.op)>=0}var Bg=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 Bg(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 this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=a8(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return Use(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(u=>this.graph.nodes[Un(u)[0]]),a=t.map(u=>Un(u)[0]),s=a.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return z(()=>{let u=new n8(this.weightMap,l,c,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,A]=Un(f),y=[];y[A]=e[f],h[m]=y});let d=this.getFrozenTensorIds(h),p={};for(let f=0;fyn(f,h,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=Jae(o.name,n,r);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!a.has(c.id)){let u=i[c.id];u===1?(c.dispose(),delete i[c.id]):u!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new n8(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>yn(h,i,s)),l=o.map(h=>h.id),c=Object.keys(e).map(h=>e[h].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.kept&&!d.isDisposed&&!u.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(u),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[Un(g)[0]]),i=n.map(g=>Un(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:h}=a8(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[x,v]=Un(g),w=[];w[v]=e[g],p[x]=w});let f={},m=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,m,i,f,l);await Promise.all(g)}u==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. 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Consider providing the following inputs: [${c}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&I("isConstant",u.node,r,n)&&([h]=xa(u.node.name,n)),r[u.node.name]==null){let d=t8(u.node,r,n,this._resourceManager);h||([h]=xa(u.node.name,n));let p=n.currentContext;_.isPromise(d)?c.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=xa(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!yn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!yn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Un(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);_.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&_.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Un(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Un(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Xse=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Kse="?tfjs-format=file",Zse="model.json",s8=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Xse}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Nn.browserHTTPRequest(e,this.loadOptions);else{let t=Nn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Nn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=Nn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Bg(Z4.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=Z4.Instance.transformGraph(e.modelInitializer);this.initializer=new Bg(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Nn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Pe)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Xt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},y8=class extends Xt{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=vr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Ug=class extends Xt{constructor(){super();this.outputQueue=new Vg,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return 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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 b8(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(_.sizeFromShape(t));return n.set(e,n.length-e.length),Ir(n,t)}},_8=class extends Xt{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=sn([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=Mn([s,a,o,i],[1,4])}else this.cropBox=Mn([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 _8(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&_.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=pi.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return z(()=>{let t=Qt(ge(e,"float32"),0),n;n=Le.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},v8=class{},k8=class extends Xt{split(e){return new Cie(this,e)}},Cie=class extends k8{constructor(e,t){super();this.upstream=e,this.impl=new Rie(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Rie=class extends Ug{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${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}},Fie=class extends Xt{decodeUTF8(){return new Mie(this)}},Mie=class extends k8{constructor(e){super();this.upstream=e,this.impl=new $ie(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},$ie=class extends Ug{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=W9();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}},I8=class extends Fie{constructor(e,t={}){super();this.file=e,this.options=t,_.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 Oie(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=Die(e));let a=await _.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new I8(s,t)}else throw new Error(a.statusText)}var Die=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 S8(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var u8=class extends v8{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(S8(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new I8(this.input,this.options)}},c8=class extends v8{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return S8(this.url)?new u8(this.url,this.fileOptions).iterator():Oie(this.url,this.fileOptions)}};function eie(e,t={}){return new l8(new c8(e),t)}function tie(e){let t=jg(e);return Hn(async()=>t)}function nie(e){return Hn(async()=>{let t=await e();return jg(()=>t.next())})}async function rie(e,t){return _8.create(e,t)}async function aie(e){return b8.create(e)}var sie="3.5.0",zie={tfjs:(_f==null?void 0:_f.version)||void 0,"tfjs-core":(vf==null?void 0:vf.version)||void 0,"tfjs-data":(kf==null?void 0:kf.version)||void 0,"tfjs-layers":(If==null?void 0:If.version)||void 0,"tfjs-converter":(Sf==null?void 0:Sf.version)||void 0,"tfjs-backend-cpu":v_||void 0,"tfjs-backend-webgl":G3||void 0,"tfjs-backend-wasm":zv||void 0};var Gn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function N8(){if(!lm(Gn.name)){le("backend registration:",Gn.name);try{Gn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Gn.width,Gn.height):document.createElement("canvas")}catch(e){le("error: cannot create canvas:",e);return}try{Gn.gl=Gn.canvas.getContext("webgl2",Gn.webGLattr)}catch(e){le("error: cannot get WebGL2 context:",e);return}try{yp(2,Gn.gl)}catch(e){le("error: cannot set WebGL2 context:",e);return}try{let e=new bp(Gn.gl);Al(Gn.name,()=>new Wl(e),Gn.priority)}catch(e){le("error: cannot register WebGL backend:",e);return}try{ul("webgl").forEach(t=>{let n={...t,backendName:Gn.name};ui(n)})}catch(e){le("error: cannot update WebGL backend registration:",e);return}try{_r.set("WEBGL_VERSION",2)}catch(e){le("error: cannot set WebGL backend 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r=Re(e,[0,1],[-1,2]),a=se(r,t),s=Re(e,[0,3],[-1,2]),i=Ae(s,n),o=Ae(a,n),l=Ae(i,2),c=ye(o,l),u=se(o,l),h=P(c,n),d=P(u,n);return xl([h,d],1)}var R8=class{constructor(t,n){this.model=t,this.anchorsData=Bie(t.inputs[0].shape[1]),this.anchors=Mn(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]=z(()=>{let d=t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(.5),p=this.model.predict(d),f;if(Array.isArray(p)){let g=p.sort((b,k)=>b.size-k.size),x=rt([g[0],g[2]],2),v=rt([g[1],g[3]],2);f=rt([v,x],1).squeeze(0)}else f=p.squeeze();let m=jie(f,this.anchors,[this.inputSize,this.inputSize]),A=Re(f,[0,0],[-1,1]),y=Tn(A).squeeze();return[f,m,y]}),s=await Le.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=>Re(r,[h,0],[1,-1])).map(h=>{let d=h.arraySync();return h.dispose(),d}),c=a.dataSync(),u=[];for(let h=0;hthis.config.face.detector.minConfidence){let f=Vie(l[h]),m=this.anchorsData[d],A=z(()=>Re(n,[d,C8-1],[1,-1]).squeeze().reshape([C8,-1]));u.push({box:f,landmarks:A,anchor:m,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),{boxes:u,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function M8(e){let t=await ct(pt(e.modelBasePath,e.face.detector.modelPath),{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new R8(t,e);return!t||!t.modelUrl?le("load model failed:",e.face.detector.modelPath):e.debug&&le("load model:",t.modelUrl),n}function F8(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 Qc(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function 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i=R0(C0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Qc(i),l=Le.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&_r.flags.IS_BROWSER&&(l=Le.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&_r.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=$0.count?$0.symmetryLine:P8.symmetryLine;u=c2(i.landmarks[w],i.landmarks[b]);let k=Ql({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],C=Le.rotateWithOffset(t,u,0,N);h=F0(-u,k),n.face.mesh.enabled?c=eu({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255):c=eu({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.boxSize,this.boxSize]).div(255)}else{h=M0;let w=t.clone();n.face.mesh.enabled?c=eu({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshSize,this.meshSize]).div(255):c=eu({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),f=d.dataSync()[0];if(f=$0.count?$0.symmetryLine:P8.symmetryLine;u=c2(i.landmarks[w],i.landmarks[b]);let k=Ql({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],C=Le.rotateWithOffset(t.toFloat(),u,0,N);h=F0(-u,k),c=eu({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255)}let x={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:A},v=R0(i);return this.storedBoxes[o]={...v,landmarks:y,confidence:i.confidence,faceConfidence:f},x}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=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,landmarks:t}}};var A2=class{constructor(t,n,r,a){this.facePipeline=new f2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Qr))l[h]=Qr[h].map(d=>i[d]);let c=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[2],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[1],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxConfidence:Math.round(100*s.boxConfidence)/100,faceConfidence:Math.round(100*s.faceConfidence)/100,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Kt=[null,null,null];async function y2(e){return!Kt[0]&&e.face.enabled||!Kt[1]&&e.face.mesh.enabled||!Kt[2]&&e.face.iris.enabled?(Kt=await Promise.all([!Kt[0]&&e.face.enabled?M8(e):null,!Kt[1]&&e.face.mesh.enabled?ct(pt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Kt[2]&&e.face.iris.enabled?ct(pt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Kt[1]||!Kt[1].modelUrl?le("load model failed:",e.face.mesh.modelPath):e.debug&&le("load model:",Kt[1].modelUrl)),e.face.iris.enabled&&(!Kt[2]||!Kt[1].modelUrl?le("load model failed:",e.face.iris.modelPath):e.debug&&le("load model:",Kt[2].modelUrl))):e.debug&&(le("cached model:",Kt[0].model.modelUrl),le("cached model:",Kt[1].modelUrl),le("cached model:",Kt[2].modelUrl)),new A2(Kt[0],Kt[1],Kt[2],e)}var L8=ji,W8=eh;var N2={};lr(N2,{PoseNet:()=>T2,load:()=>E2});function Kie(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var g2=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=Kie(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function x2(e){return Math.floor(e/2)}var w2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(x2(t),t);)this.exchange(t,x2(t)),t=x2(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(nt){l=!1;break}if(!l)break}return l}function B8(e,t,n){let[r,a,s]=n.shape,i=new w2(r*a*s,({score:o})=>o);for(let o=0;o(e[t]=n,e),{}),Yie=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],kle=Yie.map(([e,t])=>[th[e],th[t]]),V8=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function b2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+au)}}function O0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=b2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function _2(e,t,n){return en?n:e}function j8(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function v2(e,t){return{x:e.x+t.x,y:e.y+t.y}}function U8(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;ae.toTensor().mul(xe(t,"int32")).toFloat().add(Qie(e,n)))}function eoe(e,t){return z(()=>{let n=e.div(xe(t,"int32"));return e.sub(n.mul(xe(t,"int32")))})}function G8(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(xe(n,"int32")).expandDims(1),o=eoe(s,n).expandDims(1);return rt([i,o],1)})}var q8=V8.map(([e,t])=>[th[e],th[t]]),k2=q8.map(([,e])=>e),X8=q8.map(([e])=>e),toe=16;function noe(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function I2(e,t,n,r){return{y:_2(Math.round(e.y/t),0,n-1),x:_2(Math.round(e.x/t),0,r-1)}}function K8(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=I2(t.position,s,l,c),h=noe(e,u,i),p=v2(t.position,h);for(let A=0;A=0;--d){let p=k2[d],f=X8[d];l[p]&&!l[f]&&(l[f]=K8(d,l[p],f,t,n,r,s))}for(let d=0;d(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:ru[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var roe=1,J8=16;function Q8(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return j8(r,n,i.y,i.x)<=t})}function aoe(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(Q8(e,t,s,o)||(a+=i),a),0)/n.length}function ek(e,t,n,r,a,s,i){let o=[],l=B8(i,roe,e),c=a^2;for(;o.lengthi&&o.push({keypoints:d,score:Math.round(100*p)/100})}return o}async function tk(e){return Promise.all(e.map(t=>t.buffer()))}function soe(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:r,part:a,position:s})=>({score:r,part:a,position:{x:Math.trunc(s.x*n),y:Math.trunc(s.y*t)}}))}}function nk(e,[t,n]){let r=e.squeeze(0),a=r.resizeBilinear([t,n]);return r.dispose(),a}function S2(e,[t,n],[r,a]){return e.map(i=>soe(i,t/r,n/a))}var Ui;async function ioe(e,t,n,r){return new Promise(async a=>{let s=await tk([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),i=s[0],o=s[1],l=s[2],c=s[3],u=await ek(i,o,l,c,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=S2(u,[e.shape[1],e.shape[2]],[r,r]);a(h)})}async function ooe(e,t,n,r){return new Promise(async a=>{let s=await Y8(t.heatmapScores,t.offsets,n.body.scoreThreshold),i=S2([s],[e.shape[1],e.shape[2]],[r,r]);a(i)})}var T2=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let r=nk(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await ooe(t,a,n,this.inputSize):await ioe(t,a,n,this.inputSize);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function E2(e){Ui?e.debug&&le("cached model:",Ui.modelUrl):(Ui=await ct(pt(e.modelBasePath,e.body.modelPath)),!Ui||!Ui.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",Ui.modelUrl));let t=new g2(Ui);return new T2(t)}var $2={};lr($2,{HandPose:()=>O2,load:()=>z2});function z0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function nh(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function rk(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Le.cropAndResize(t,s,[0],n)}function ak(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]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function P0(e,t=1.5){let n=nh(e),r=z0(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function L0(e){let t=nh(e),n=z0(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var C2=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=Mn(this.anchors),this.inputSize=n,this.inputSizeTensor=sn([n,n]),this.doubleInputSizeTensor=sn([n*2,n*2])}normalizeBoxes(t){return z(()=>{let n=Re(t,[0,0],[-1,2]),r=Re(t,[0,2],[-1,2]),a=se(Ae(n,this.inputSizeTensor),this.anchorsTensor),s=Ae(r,this.doubleInputSizeTensor),i=P(ye(a,s),this.inputSizeTensor),o=P(se(a,s),this.inputSizeTensor);return xl([i,o],1)})}normalizeLandmarks(t,n){return z(()=>{let r=se(Ae(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return P(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),a=r.squeeze();r.dispose();let s=z(()=>Tn(Re(a,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Re(a,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let c=await Le.nonMaxSuppressionAsync(l,i,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),u=c.arraySync();s.dispose(),c.dispose();let h=[];for(let d of u)if(i[d]>=n.hand.minConfidence){let p=Re(l,[d,0],[1,-1]),f=Re(a,[d,5],[1,14]),m=z(()=>this.normalizeLandmarks(f,d).reshape([-1,2]));f.dispose(),h.push({box:p,palmLandmarks:m,confidence:i[d]})}return a.dispose(),l.dispose(),h}async estimateHandBounds(t,n){let r=t.shape[1],a=t.shape[2],s=z(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let c=l.box.dataSync(),u=c.slice(0,2),h=c.slice(2,4),d=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(ak({startPoint:u,endPoint:h,palmLandmarks:d,confidence:l.confidence},[a/this.inputSize,r/this.inputSize]))}return o}};function loe(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function sk(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return loe(n)}var ik=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function as(e,t){let n=0;for(let r=0;rM2([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return P0(L0(a),coe)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=P0(L0(n),uk);r.palmLandmarks=[];for(let a=0;a[i[0]*(p[0]-this.inputSize/2),i[1]*(p[1]-this.inputSize/2),i[2]*p[2]]),l=R2(r,[0,0]),c=o.map(p=>[...M2(p,l),p[2]]),u=lk(a),h=[...nh(n),1],d=[as(h,u[0]),as(h,u[1])];return c.map(p=>[p[0]+d[0],p[1]+d[1],p[2]])}async estimateHands(t,n){let r=!1,a;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(a=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let i=0;i=n.hand.minConfidence){let x=H(y,[-1,3]),v=x.arraySync();y.dispose(),x.dispose();let w=this.transformRawCoords(v,p,l,d),b=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=b;let k={landmarks:w,confidence:g,box:{topLeft:b.startPoint,bottomRight:b.endPoint}};s.push(k)}else this.storedBoxes[i]=null;y.dispose()}else{let l=P0(L0(o),uk),c={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(c)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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dk=["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"],pk=["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 xn;async function L2(e){return xn?e.debug&&le("cached model:",xn.modelUrl):(xn=await ct(pt(e.modelBasePath,e.body.modelPath)),xn.width=parseInt(xn.signature.inputs["input_1:0"].tensorShape.dim[2].size),xn.height=parseInt(xn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!xn||!xn.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",xn.modelUrl)),xn}async function W2(e,t){if(!xn||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Le.resizeBilinear(e,[xn.width,xn.height],!1),a=Ae(r,[255]);r.dispose();let s;if(t.profile){let u=await an(()=>xn.predict(a));s=u.result.find(h=>h.size===195||h.size===155).dataSync(),u.result.forEach(h=>h.dispose()),gn("blazepose",u)}else{let u=await xn.predict(a);s=u.find(h=>h.size===195||h.size===155).dataSync(),u.forEach(h=>h.dispose())}a.dispose();let i=[],o=s.length===195?dk:pk,l=5;for(let u=0;uh.score>u?h.score:u,0),keypoints:i}]}var 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tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});oh.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let r=Ye();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&le("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&le("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let a=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&le(`wasm execution: ${a?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!a&&le("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&N8();try{await this.tf.setBackend(this.config.backend)}catch(a){le("error: cannot set backend:",this.config.backend,a)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(le("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let a=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&le(`gl version:${a.getParameter(a.VERSION)} renderer:${a.getParameter(a.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(Ye()-r)}});K0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(G0);break;case"full":n=await t(q0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});Z0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+G0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+q0;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)}));Y0.set(this,async()=>{let t=a=>Buffer.from(a,"base64"),n;if(this.config.warmup==="face"&&(n=t(G0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(q0)),!n)return null;let r;if(typeof void 0!="undefined"){let a=(void 0).decodeJpeg(n),s=a.expandDims(0);this.tf.dispose(a),r=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&le("Warmup tfjs-node not loaded");return r});this.tf=gu,this.draw=K2,this.version=kk,this.config=Jn(ht,t),this.state="idle",us(this,iu,0),us(this,sh,!1),us(this,ih,!1),us(this,Gi,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>X2(n,this.config),this.classes={facemesh:m2,age:Gg,gender:Xg,emotion:Qg,faceres:s2,body:this.config.body.modelPath.includes("posenet")?N2:P2,hand:$2,nanodet:j2},this.faceTriangulation=L8,this.faceUVMap=W8,this.sysinfo=c5()}profileData(){return this.config.profile?x0:{}}similarity(t,n){return this.config.face.description.enabled?o2(t,n):this.config.face.embedding.enabled?T8(t,n):0}enhance(t){return l2(t)}match(t,n,r=0){return E8(t,n,r)}async load(t={}){this.state="load";let n=Ye();t&&(this.config=Jn(this.config,t)),ur(this,Gi)&&(this.config.debug&&le(`version: ${this.version}`),this.config.debug&&le(`tfjs version: ${this.tf.version_core}`),this.config.debug&&le("platform:",this.sysinfo.platform),this.config.debug&&le("agent:",this.sysinfo.agent),await ur(this,oh).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&le("configuration:",this.config),this.config.debug&&le("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.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?y2(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?qg(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Jg(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?n2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?r2(this.config):null),this.models.handpose||(this.config.hand.enabled?z2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?E2(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?L2(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?B2(this.config):null),this.models.nanodet||(this.config.object.enabled?H2(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?i2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await y2(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await qg(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Jg(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await n2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await r2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await z2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await E2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await L2(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await B2(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await H2(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await i2(this.config))),ur(this,Gi)&&(this.config.debug&&le("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),us(this,Gi,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var A,y,g,x;this.state="config";let a;this.config=Jn(this.config,n),this.state="check";let s=ur(this,X0).call(this,t);s&&(le(s,t),r({error:s}));let i=Ye();await ur(this,oh).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(typeof HTMLImageElement!="undefined"&&t instanceof HTMLImageElement||typeof Image!="undefined"&&t instanceof Image||typeof ImageData!="undefined"&&t instanceof ImageData||typeof ImageBitmap!="undefined"&&q2 instanceof ImageBitmap)&&(le("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),a=Ye();let l=X2(t,this.config);if(!l||!l.tensor){le("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let c,u,h,d,p;this.config.async?(h=this.config.face.enabled?u2(this,l.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),h=this.config.face.enabled?await u2(this,l.tensor):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.face=p)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?(A=this.models.posenet)==null?void 0:A.estimatePoses(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?W2(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?V2(l.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=Ye(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await((y=this.models.posenet)==null?void 0:y.estimatePoses(l.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?await W2(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?await V2(l.tensor,this.config):[]),p=Math.trunc(Ye()-a),p>0&&(this.perf.body=p)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(g=this.models.handpose)==null?void 0:g.estimateHands(l.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=Ye(),u=this.config.hand.enabled?await((x=this.models.handpose)==null?void 0:x.estimateHands(l.tensor,this.config)):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.hand=p)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(d=this.config.object.enabled?G2(l.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=Ye(),d=this.config.object.enabled?await G2(l.tensor,this.config):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.object=p)),this.analyze("End Object:"),this.config.async&&([h,c,u,d]=await Promise.all([h,c,u,d])),_e(l.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(a=Ye(),f=[...mk(h),...fk(c),...yk(u),...Ak(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(Ye()-a)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(Ye()-i),this.state="idle";let m={face:h,body:c,hand:u,gesture:f,object:d,performance:this.perf,canvas:l.canvas};r(m)})}async warmup(t={}){let n=Ye();if(t&&(this.config=Jn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await ur(this,K0).call(this):typeof Image!="undefined"?a=await ur(this,Z0).call(this):a=await ur(this,Y0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&le("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};iu=new WeakMap,sh=new WeakMap,ih=new WeakMap,Gi=new WeakMap,X0=new WeakMap,oh=new WeakMap,K0=new WeakMap,Z0=new WeakMap,Y0=new WeakMap;export{woe as Human,woe as default}; /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the License); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an AS IS BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** @license See the LICENSE file. */ //# sourceMappingURL=human.esm.js.map