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human
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human/dist/tfjs.esm.js

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/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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Got strides ${t} and dilations '${a}'`);let i=s,l=!1;s.rank===3&&(l=!0,i=F(s,[1,s.shape[0],s.shape[1],s.shape[2]])),E(i.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),o!=null&&E(it(n),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${o} but got pad ${n}.`);let u={x:i},c={filterSize:e,strides:t,pad:n,dimRoundingMode:o},p=T.runKernel(Fo,u,c);return p=Y(p,s.dtype),l?F(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Ta=N({avgPool_:yU});function bU(r,e,t,n,o,s="NDHWC"){let a=k(r,"x","avgPool3d","float32"),i=a,l=!1;a.rank===4&&(l=!0,i=F(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),E(i.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`),E(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),o!=null&&E(it(n),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${n}.`);let u={x:i},c={filterSize:e,strides:t,pad:n,dimRoundingMode:o,dataFormat:s},p=T.runKernel(fl,u,c);return p=Y(p,i.dtype),l?F(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var kf=N({avgPool3d_:bU});function wU(r,e=0){E(r.length>=1,()=>"Pass at least one tensor to concat");let t=va(r,"tensors","concat","string_or_numeric");if(t[0].dtype==="complex64"&&t.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
with dtype ${s.dtype}. `)}),t.length===1)return wn(t[0]);let n=t,o={axis:e};return T.runKernel(ni,n,o)}var tt=N({concat_:wU});function _U(r){let t={x:k(r,"x","sigmoid","float32")};return T.runKernel(xs,t)}var zr=N({sigmoid_:_U});function kU(r,e,t){let n=k(r,"x","slice","string_or_numeric");if(n.rank===0)throw new Error("Slicing scalar is not possible");let o={x:n},s={begin:e,size:t};return T.runKernel(pi,o,s)}var Oe=N({slice_:kU});function vU(r){let t={x:k(r,"x","tanh","float32")};return T.runKernel(Cs,t)}var Ds=N({tanh_:vU});function CU(r,e,t,n,o,s){let a=k(r,"forgetBias","basicLSTMCell"),i=k(e,"lstmKernel","basicLSTMCell"),l=k(t,"lstmBias","basicLSTMCell"),u=k(n,"data","basicLSTMCell"),c=k(o,"c","basicLSTMCell"),p=k(s,"h","basicLSTMCell"),m=tt([u,p],1),f=ze(m,i),d=Z(f,l),h=d.shape[0],g=d.shape[1]/4,x=[h,g],y=Oe(d,[0,0],x),w=Oe(d,[0,g],x),_=Oe(d,[0,g*2],x),C=Oe(d,[0,g*3],x),A=Z(O(zr(y),Ds(w)),O(c,zr(Z(a,_)))),D=O(Ds(A),zr(C));return[A,D]}var IU=N({basicLSTMCell_:CU});function SU(r,e,t){let n=k(r,"x","batchToSpaceND"),o=e.reduce((i,l)=>i*l);E(n.rank>=1+e.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${e.length}`),E(t.length===e.length,()=>`crops.length is ${t.length} but should be equal to blockShape.length ${e.length}`),E(n.shape[0]%o==0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${o}`);let s={x:n},a={blockShape:e,crops:t};return T.runKernel(ri,s,a)}var Ea=N({batchToSpaceND_:SU});function R1(r){let e;return r.rank===0||r.rank===1?e=F(r,[1,1,1,r.size]):r.rank===2?e=F(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?e=F(r,[1,r.shape[0],r.shape[1],r.shape[2]]):e=r,e}function NU(r,e,t,n,o,s){s==null&&(s=.001);let a=k(r,"x","batchNorm"),i=k(e,"mean","batchNorm"),l=k(t,"variance","batchNorm"),u;o!=null&&(u=k(o,"scale","batchNorm"));let c;n!=null&&(c=k(n,"offset","batchNorm")),E(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),E(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),E(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:R1(a),scale:u,offset:c,mean:i,variance:l},f={varianceEpsilon:s},d=T.runKernel(Ko,m,f);return F(d,a.shape)}var lo=N({batchNorm_:NU});function TU(r,e,t,n,o,s){let a=k(r,"x","batchNorm"),i=k(e,"mean","batchNorm"),l=k(t,"variance","batchNorm"),u;o!=null&&(u=k(o,"scale","batchNorm"));let c;return n!=null&&(c=k(n,"offset","batchNorm")),E(a.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`),E(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),E(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&E(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),lo(a,i,l,c,u,s)}var L_=N({batchNorm2d_:TU});function EU(r,e,t,n,o,s){let a=k(r,"x","batchNorm"),i=k(e,"mean","batchNorm"),l=k(t,"variance","batchNorm"),u;o!=null&&(u=k(o,"scale","batchNorm"));let c;return n!=null&&(c=k(n,"offset","batchNorm")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),lo(a,i,l,c,u,s)}var z_=N({batchNorm3d_:EU});function AU(r,e,t,n,o,s){let a=k(r,"x","batchNorm"),i=k(e,"mean","batchNorm"),l=k(t,"variance","batchNorm"),u;o!=null&&(u=k(o,"scale","batchNorm"));let c;return n!=null&&(c=k(n,"offset","batchNorm")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),lo(a,i,l,c,u,s)}var B_=N({batchNorm4d_:AU});function DU(r,e,t){let n=k(r,"x","bincount"),o=k(e,"weights","bincount");E(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(o.size===n.size||o.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${o.shape}.`);let s={x:n,weights:o},a={size:t};return T.runKernel(jc,s,a)}var vf=N({bincount_:DU});function $U(r,e){let t=k(r,"s0","broadcastArgs","int32"),n=k(e,"s1","broadcastArgs","int32");if(t.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let o={s0:t,s1:n};return T.runKernel(Hc,o)}var V_=N({broadcastArgs_:$U});function RU(r,e){let t=k(r,"broadcastTo","x"),n=t.shape;if(e.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${e}].`);if(e.length<t.rank)throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t.rank}.`);if(e.length>t.rank){let u=t.shape.slice();for(;u.length<e.length;)u.unshift(1);t=F(t,u)}let o=t.shape,s=Array.from(e);for(let u=e.length-1;u>=0;u--)if(o[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return wn(t);let i={x:t},l={reps:s};return T.runKernel(Hn,i,l)}var Aa=N({broadcastTo_:RU});function FU(r){let t={x:k(r,"x","ceil","float32")};return T.runKernel(Po,t)}var Cf=N({ceil_:FU});function OU(r,e,t){let n=k(r,"x","clipByValue");E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`);let o={x:n},s={clipValueMin:e,clipValueMax:t};return T.runKernel(to,o,s)}var gr=N({clipByValue_:OU});function PU(r){return tt(r,0)}var G_=N({concat1d_:PU});function MU(r,e){return tt(r,e)}var W_=N({concat2d_:MU});function LU(r,e){return tt(r,e)}var U_=N({concat3d_:LU});function zU(r,e){return tt(r,e)}var j_=N({concat4d_:zU});function BU(r,e,t,n,o="NHWC",s=[1,1],a){let i=k(r,"x","conv2d","float32"),l=k(e,"filter","conv2d","float32"),u=i,c=!1;i.rank===3&&(c=!0,u=F(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),a!=null&&E(it(n),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`);let p=o==="NHWC"?u.shape[3]:u.shape[1];E(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),E($r(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`);let m={x:u,filter:l},f={strides:t,pad:n,dataFormat:o,dilations:s,dimRoundingMode:a},d=T.runKernel(Mo,m,f);return c?F(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var nn=N({conv2d_:BU});function VU(r,e,t,n,o="NWC",s=1,a){let i=k(r,"x","conv1d"),l=k(e,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=F(i,[1,i.shape[0],i.shape[1]])),E(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),E(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),a!=null&&E(it(n),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`),E(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),E($r(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${t} and dilation '${s}'`),E(o==="NWC",()=>`Error in conv1d: got dataFormat of ${o} but only NWC is currently supported.`);let p=F(l,[1,l.shape[0],l.shape[1],l.shape[2]]),m=F(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=nn(m,p,[1,t],n,"NHWC",[1,s],a);return c?F(g,[g.shape[2],g.shape[3]]):F(g,[g.shape[0],g.shape[2],g.shape[3]])}var vu=N({conv1d_:VU});function GU(r,e,t,n,o,s="NHWC",a){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let i=r,l=e,u=!1;e.rank===3&&(u=!0,l=F(e,[1,e.shape[0],e.shape[1],e.shape[2]]),i=[1,r[0],r[1],r[2]]),E(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),E(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),E(t.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${t.rank}`);let c=s==="NHWC"?i[3]:i[1],p=s==="NHWC"?l.shape[3]:l.shape[1];E(c===t.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t.shape[2]}.`),E(p===t.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${t.shape[3]}.`),a!=null&&E(it(o),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`);let m={dy:l,filter:t},f={strides:n,pad:o,dataFormat:s,dimRoundingMode:a,inputShape:i},d=T.runKernel(Lo,m,f);return u?F(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Mp=N({conv2DBackpropInput_:GU});function WU(r,e,t,n,o,s){let a=k(r,"x","conv2dTranspose"),i=k(e,"filter","conv2dTranspose");return Mp(t,a,i,n,o,"NHWC",s)}var Cu=N({conv2dTranspose_:WU});function UU(r,e,t,n,o="NDHWC",s=[1,1,1]){let a=k(r,"x","conv3d"),i=k(e,"filter","conv3d"),l=a,u=!1;a.rank===4&&(u=!0,l=F(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),E(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),E(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),E(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),E($r(t,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),E(o==="NDHWC",()=>`Error in conv3d: got dataFormat of ${o} but only NDHWC is currently supported.`);let c={x:l,filter:i},p={strides:t,pad:n,dataFormat:o,dilations:s},m=T.runKernel(hl,c,p);return u?F(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var If=N({conv3d_:UU});function jU(r,e,t,n,o){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let s=r,a=e,i=!1;e.rank===4&&(i=!0,a=F(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let l=s[4],u=a.shape[4];E(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),E(a.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`),E(t.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${t.rank}`),E(l===t.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${t.shape[3]}.`),E(u===t.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},p={pad:o,strides:n,inputShape:s},m=T.runKernel(Yc,c,p);return i?F(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Yg=N({conv3DBackpropInput_:jU});function HU(r,e,t,n,o){let s=k(r,"x","conv3dTranspose"),a=k(e,"filter","conv3dTranspose");return Yg(t,s,a,n,o)}var H_=N({conv3dTranspose_:HU});function qU(r){let t={x:k(r,"x","cos","float32")};return T.runKernel(zo,t)}var Da=N({cos_:qU});function KU(r){let t={x:k(r,"x","cosh","float32")};return T.runKernel(Bo,t)}var Iu=N({cosh_:KU});function XU(r,e=0,t=!1,n=!1){let s={x:k(r,"x","cumsum")},a={axis:e,exclusive:t,reverse:n};return T.runKernel(Vo,s,a)}var Su=N({cumsum_:XU});function YU(r,e,t,n=!1){let o=k(r,"x","denseBincount"),s=k(e,"weights","denseBincount");E(o.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),E(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let a={x:o,weights:s},i={size:t,binaryOutput:n};return T.runKernel(Zc,a,i)}var q_=N({denseBincount_:YU});function ZU(r,e,t="NHWC"){let n=k(r,"x","depthToSpace","float32"),o=t==="NHWC"?n.shape[1]:n.shape[2],s=t==="NHWC"?n.shape[2]:n.shape[3],a=t==="NHWC"?n.shape[3]:n.shape[1];E(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`),E(o*e>=0,()=>`Negative dimension size caused by overflow when multiplying
${o} and ${e} for depthToSpace with input shape
${n.shape}`),E(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying
${s} and ${e} for depthToSpace with input shape
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p={x:u,filter:l},m={strides:t,pad:n,dataFormat:o,dilations:s,dimRoundingMode:a},f=T.runKernel(Go,p,m);return c?F(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var $s=N({depthwiseConv2d_:JU});function QU(r){let t={x:k(r,"x","diag")};return T.runKernel(ep,t)}var ej=N({diag_:QU});function tj(r,e,t,n,o=[1,1],s="NHWC"){let a=k(r,"x","dilation2d"),i=k(e,"filter","dilation2d");E(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),E(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),E(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=a,u=!1;a.rank===3&&(l=F(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0);let c={x:l,filter:i},p={strides:t,pad:n,dilations:o},m=T.runKernel(gl,c,p);return u?F(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Nf=N({dilation2d_:tj});function rj(r,e){let t=r.length,n=[];for(let o=0;o<t;o++){let 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o=k(r,"sparseIndices","sparseToDense","int32"),s=k(e,"sparseValues","sparseToDense"),a=k(n,"defaultValue","sparseToDense",s.dtype);Z1(o,s,t,a);let i={sparseIndices:o,sparseValues:s,defaultValue:a},l={outputShape:t};return T.runKernel(bp,i,l)}var ox=N({sparseToDense_:Lq});function zq(r,e){let t=k(e,"indices","gatherND","int32"),o={params:k(r,"x","gatherND","string_or_numeric"),indices:t};return T.runKernel(Ji,o)}var J1=N({gatherND_:zq});function Q1(r,e){if(e==null)return r.shape.slice();if(Qr(r.shape,e))return e;if(r.shape.length===e.length){let t=[];for(let n=0;n<r.shape.length;n++)e[n]==null&&r.shape[n]!=null?t.push(r.shape[n]):t.push(e[n]);return t}return e}function Bq(r,e,t,n){let o=k(r,"x","dropout");if(E(o.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${o.dtype} tensor instead.`),E(e>=0&&e<1,()=>`rate must be a float in the range [0, 1), but got ${e}.`),e===0)return r instanceof Le?o.clone():o;let s=Q1(o,t),a=1-e,i=ce(Os(Z(Ms(s,0,1,"float32",n),a)),a);return O(o,i)}var eT=N({dropout_:Bq});function tT(r){return Math.floor(Math.pow(2,Math.ceil(Math.log(r)/Math.log(2))))}function sx(r,e,t){let n=1-r%2,o=new Float32Array(r);for(let s=0;s<r;++s){let a=2*Math.PI*s/(r+n-1);o[s]=e-t*Math.cos(a)}return $t(o,"float32")}async function Vq(r,e,t=1){let n=k(r,"predictions","inTopK"),o=k(e,"targets","inTopK");E(n.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${n.rank}`),E(n.rank-1===o.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${n.rank} and targets rank ${o.rank}`),Rt(n.shape.slice(0,n.shape.length-1),o.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=n.shape[n.shape.length-1];E(t>0&&t<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${t}`);let a=await n.data(),i=await o.data(),[l,u]=[a.length/s,s],c=Uw("bool",l);for(let p=0;p<l;p++){let m=p*u,f=a.subarray(m,m+u),d=[];for(let h=0;h<f.length;h++)d.push({value:f[h],index:h});d.sort((h,g)=>g.value-h.value),c[p]=0;for(let h=0;h<t;h++)if(d[h].index===i[p]){c[p]=1;break}}return r!==n&&n.dispose(),e!==o&&o.dispose(),Dr(c,o.shape,"bool")}var l1e=Vq;var fo={};qe(fo,{conv2d:()=>rT,depthwiseConv2d:()=>nT,matMul:()=>oT});function Gq(r,e,t,n,o,s="NHWC",a){let i=r;r.rank===3&&(i=F(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let l=e;l.rank===3&&(l=F(e,[1,e.shape[0],e.shape[1],e.shape[2]])),E(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),E(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),E(t.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${t}.`);let u=s==="NHWC"?i.shape[3]:i.shape[1],c=s==="NHWC"?l.shape[3]:l.shape[1];E(u===t[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth 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Wq({x:r,filter:e,strides:t,pad:n,dataFormat:o="NHWC",dilations:s=[1,1],dimRoundingMode:a,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",ju(T.state.gradientDepth,l)===!1){let C=nn(r,e,t,n,o,s,a);return i!=null&&(C=Z(C,i)),Uu(C,l,u,c)}let p=k(r,"x","conv2d","float32"),m=k(e,"filter","conv2d","float32"),f=p,d=!1;p.rank===3&&(d=!0,f=F(p,[1,p.shape[0],p.shape[1],p.shape[2]])),E(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),E(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),a!=null&&E(it(n),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`),E(f.shape[3]===m.shape[2],()=>`Error in conv2d: depth of input (${f.shape[3]}) must match input depth for filter ${m.shape[2]}.`),E($r(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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D=(L,G)=>{let[W,j,H,q]=G,X=Gu(F(L,H.shape),H,s),re,J;if(!t&&!n?(re=ze(X,j,!1,!0),J=ze(W,X,!0,!1)):!t&&n?(re=ze(X,j,!1,!1),J=ze(X,W,!0,!1)):t&&!n?(re=ze(j,X,!1,!0),J=ze(W,X,!1,!1)):(re=ze(j,X,!0,!0),J=ze(X,W,!0,!0)),o!=null){let oe=Wu(q,X);return[re,J,oe]}else return[re,J]},R={a:w,b:_,bias:C,preluActivationWeights:A},P={transposeA:t,transposeB:n,activation:s,leakyreluAlpha:i};return o==null?on((G,W,j)=>{let H=T.runKernel(gi,R,P);return j([G,W,H]),{value:F(H,y),gradFunc:D}})(w,_):on((G,W,j,H)=>{let q=T.runKernel(gi,R,P);return H([G,W,q,j]),{value:F(q,y),gradFunc:D}})(w,_,C)}var oT=N({fusedMatMul_:qq});function Kq(r){return sx(r,.54,.46)}var sT=N({hammingWindow_:Kq});function Xq(r){return sx(r,.5,.5)}var lx=N({hannWindow_:Xq});function Yq(r,e,t,n=!1,o=0){let s=0,a=[];for(;s+e<=r.size;)a.push(Oe(r,s,e)),s+=t;if(n)for(;s<r.size;){let i=s+e-r.size,l=tt([Oe(r,s,e-i),Fs([i],o)]);a.push(l),s+=t}return a.length===0?ki([],[0,e]):F(tt(a),[a.length,e])}var ux=N({frame_:Yq});function 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Qq(r){let e=k(r,"image","flipLeftRight","float32");E(e.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${e.rank}.`);let t={image:e};return T.runKernel(Zi,t,{})}var lT=N({flipLeftRight_:Qq});function eK(r){let e=k(r,"image","grayscaleToRGB"),t=e.rank-1,n=e.shape[t];E(e.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${e.rank}.`),E(n===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${n}.`);let o=new Array(e.rank);return o.fill(1,0,t),o[t]=3,vr(e,o)}var uT=N({grayscaleToRGB_:eK});function tK(r,e,t=0,n=.5){let o=k(r,"image","rotateWithOffset","float32");E(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},a={radians:e,fillValue:t,center:n};return T.runKernel(ka,s,a)}var cT=N({rotateWithOffset_:tK});function ho(r,e,t,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let a=r.shape[0];return t=Math.min(t,a),E(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${n}'`),E(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),E(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),E(e.rank===1,()=>"scores must be a 1D tensor"),E(e.shape[0]===a,()=>`scores has incompatible shape with boxes. 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u={boxes:a,scores:i},c={maxOutputSize:t,iouThreshold:n,scoreThreshold:o,softNmsSigma:s},p=T.runKernel(pa,u,c);return{selectedIndices:p[0],selectedScores:p[1]}}var hT=N({nonMaxSuppressionWithScore_:uK});async function cK(r,e,t,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let a=k(r,"boxes","nonMaxSuppressionAsync"),i=k(e,"scores","nonMaxSuppressionAsync"),l=ho(a,i,t,n,o,s);t=l.maxOutputSize,n=l.iouThreshold,o=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([a.data(),i.data()]),c=u[0],p=u[1],{selectedIndices:m,selectedScores:f}=mx(c,p,t,n,o,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:$t(m,"int32"),selectedScores:$t(f)}}var gT=cK;function pK(r,e,t,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let 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s=o,a=!1;o.rank===3&&(a=!0,s=F(o,[1,o.shape[0],o.shape[1],o.shape[2]]));let[]=e,i={images:s},l={alignCorners:t,halfPixelCenters:n,size:e},u=T.runKernel(_l,i,l);return a?F(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var dx=N({resizeNearestNeighbor_:dK});function hK(r,e="binary",t=!1,n=.5){let o=k(r,"image","threshold"),s=.2989,a=.587,i=.114,l=o.shape[0]*o.shape[1],u=O($t([n]),255),c,p,m,f;if(E(o.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${o.rank}.`),E(o.shape[2]===3||o.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${o.shape[2]}.`),E(o.dtype==="int32"||o.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${o.dtype}.`),E(e==="otsu"||e==="binary",()=>`Method must be binary or otsu, but was ${e}`),o.shape[2]===3){[c,p,m]=sr(o,[1,1,1],-1);let g=O(c,s),x=O(p,a),y=O(m,i);f=Z(Z(g,x),y)}else f=r;if(e==="otsu"){let g=vf(Y(Fu(f),"int32"),Dr([]),256);u=gK(g,l)}let d=t?vn(f,u):zt(f,u);return 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wT=N({transform_:xK});function yK(r,e,t){E(e%1==0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(t%1==0,()=>`bandPart(): numUpper must be an integer, got ${t}.`);let n=k(r,"a","bandPart");E(n.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${n.rank}.`);let o=n.shape,[s,a]=n.shape.slice(-2);if(!(e<=s))throw new Error(`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`);if(!(t<=a))throw new Error(`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`);e<0&&(e=s),t<0&&(t=a);let i=F(La(0,s,1,"int32"),[-1,1]),l=La(0,a,1,"int32"),u=le(i,l),c=Cr(vn(u,pe(+e,"int32")),kn(u,pe(-t,"int32"))),p=yt([s,a],n.dtype);return F(Xt(yr(F(n,[-1,s,a])).map(m=>St(c,m,p))),o)}var _T=N({bandPart_:yK});function bK(r){let e;if(Array.isArray(r)){e=!1,E(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let o=r[0].shape[0];for(let s=1;s<r.length;++s)E(r[s].shape[0]===o,()=>`Gram-Schmidt: 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s=k(r,"multiClassLabels","sigmoidCrossEntropy"),a=k(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=k(t,"weights","sigmoidCrossEntropy")),Rt(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),n>0){let u=pe(n),c=pe(1),p=pe(.5);s=Z(O(s,le(c,u)),O(p,u))}let l=TK(s,a);return Vr(l,i,o)}var DT=N({sigmoidCrossEntropy_:EK});function AK(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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s={skipEmpty:t},a={input:n,delimiter:o},i=T.runKernel(_p,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var LT=N({stringSplit_:MK});function LK(r,e){let t=k(r,"input","stringToHashBucketFast","string"),n={numBuckets:e};if(e<=0)throw new Error("Number of buckets must be at least 1");let o={input:t};return T.runKernel(kp,o,n)}var zT=N({stringToHashBucketFast_:LK});var SRe={fft:Ba,ifft:_i,rfft:Va,irfft:zu},DRe={hammingWindow:sT,hannWindow:lx,frame:ux,stft:iT},Cn={flipLeftRight:lT,grayscaleToRGB:uT,resizeNearestNeighbor:dx,resizeBilinear:fx,rotateWithOffset:cT,cropAndResize:aT,nonMaxSuppression:pT,nonMaxSuppressionAsync:dT,nonMaxSuppressionWithScore:hT,nonMaxSuppressionWithScoreAsync:gT,nonMaxSuppressionPadded:xT,nonMaxSuppressionPaddedAsync:yT,threshold:bT,transform:wT},BT={bandPart:_T,gramSchmidt:kT,qr:CT},oFe={absoluteDifference:IT,computeWeightedLoss:Vr,cosineDistance:ST,hingeLoss:NT,huberLoss:TT,logLoss:ET,meanSquaredError:AT,sigmoidCrossEntropy:DT,softmaxCrossEntropy:$T},Kf={sparseFillEmptyRows:RT,sparseReshape:FT,sparseSegmentMean:OT,sparseSegmentSum:PT},hx={stringNGrams:MT,stringSplit:LT,stringToHashBucketFast:zT};var qr=class extends qg{minimize(e,t=!1,n){let{value:o,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return 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qr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=T.registeredVariables[n];if(this.accumulatedGrads[o]==null){let l=!1;this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:V(()=>Fs(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[o].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[o].variable;V(()=>{let l=Z(i,Ve(a));i.assign(l);let u=Z(O(ce(a,bt(Z(l,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&De(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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MA(r,e={}){return vi(r,ee.SerializationMap.getMap().classNameMap,e,"regularizer")}function _t(r){if(r==null)return null;if(typeof r=="string"){let t={className:r in PA?PA[r]:r,config:{}};return MA(t)}else return r instanceof pv?r:MA(r)}var bd=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Me(e);let n=Ir(e);return this.maxValue!=null&&(n=gr(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};bd.className="ReLU";ee.registerClass(bd);var wd=class extends Ge{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=Me(e);return $a(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};wd.className="LeakyReLU";ee.registerClass(wd);var _d=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=dt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(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 z(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Xe(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)t[o-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o<e.length;++o)n[o]=e[o];this.inputSpec=[new Tt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Me(e),Ma(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Nt(this.alphaInitializer),alphaRegularizer:ct(this.alphaRegularizer),alphaConstraint:Bt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};_d.className="PReLU";ee.registerClass(_d);var kd=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ne(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Me(e);return Rs(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};kd.className="ELU";ee.registerClass(kd);var vd=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Me(e);return O(n,Y(zt(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};vd.className="ThresholdedReLU";ee.registerClass(vd);var Cd=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new yd().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Me(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Cd.className="Softmax";ee.registerClass(Cd);function Ll(r,e,t){if(typeof r=="number")return go(r,e);if(r.length!==e)throw new z(`The ${t} argument must be an integer or tuple of ${e} integers. 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z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=mv(r,s);if(o==="causal")throw new Ne("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=If(i,e,n,o==="same"?"same":"valid","NDHWC",a),t!=null&&(i=un(i,t)),s==="channelsFirst"&&(i=Be(i,[0,4,1,2,3])),i})}var cm=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",cm.verifyArgs(t),this.rank=e,Zt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ne(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented 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if(this.dilationRate.length!==2)throw new z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Kn("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!bx(e.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Gs(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ac=class extends cm{constructor(e,t){super(e,t);this.kernel=null,ac.verifyArgs(t),this.filters=t.filters,Zt(this.filters,"filters"),this.kernelInitializer=dt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Vt(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. 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Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=dt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=Vt(t.depthwiseConstraint),this.pointwiseInitializer=dt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=Vt(t.pointwiseConstraint)}build(e){if(e=Xe(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Tt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{e=Me(e);let n;if(this.rank===1)throw new Ne("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Be(e,[0,2,3,1])),n=zf(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=un(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Be(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=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.pointwiseRegularizer=ct(this.pointwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseConstraint),e.pointwiseConstraint=Bt(this.pointwiseConstraint),e}};fv.className="SeparableConv";var Td=class extends fv{constructor(e){super(2,e)}};Td.className="SeparableConv2D";ee.registerClass(Td);var lc=class extends ac{constructor(e){super(1,e);lc.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"&&!bx(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};lc.className="Conv1D";ee.registerClass(lc);var Ed=class extends Ge{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Me(e),this.dataFormat==="channelsLast"){let n=rd(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return rd(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=rd(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return rd(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}};Ed.className="Cropping2D";ee.registerClass(Ed);var Ad=class extends Ge{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,X2(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 V(()=>{let n=Me(e),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Be(n,[0,2,3,1]);let s=this.size[0]*o[2],a=this.size[1]*o[3],i=this.interpolation==="nearest"?Cn.resizeNearestNeighbor(n,[s,a]):Cn.resizeBilinear(n,[s,a]);return Be(i,[0,3,1,2])}else{let s=this.size[0]*o[1],a=this.size[1]*o[2];return this.interpolation==="nearest"?Cn.resizeNearestNeighbor(n,[s,a]):Cn.resizeBilinear(n,[s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ad.className="UpSampling2D";ee.registerClass(Ad);function J5(r,e,t=[1,1],n="valid",o,s){return V(()=>{o==null&&(o=an()),Ot(o);let a=Id(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=$s(a,e,t,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(a=Be(a,[0,3,1,2])),a})}var Dd=class extends cm{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=dt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Vt(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=Xe(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Me(e);let n=J5(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=un(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=Nn(t,this.kernelSize[0],this.padding,this.strides[0]),a=Nn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],o,s,a]:[e[0],s,a,o]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseRegularizer),e}};Dd.className="DepthwiseConv2D";ee.registerClass(Dd);function dv(r,e,t,n){if(Array.isArray(r)){if(e!=null||t!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(t=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return e=o(e),t=o(t),{inputs:r,initialState:e,constants:t}}function hv(r,e,t,n=!1,o,s,a=!1,i=!1){return V(()=>{let l=e.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Xr(2,l));if(e=Be(e,u),s!=null)throw new Ne("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=Y(Y(o,"bool"),"float32"),o.rank===l-1&&(o=mr(o,-1)),o=Be(o,u)),n&&(e=er(e,0),o!=null&&(o=er(o,0)));let c=[],p,m=t,f=e.shape[0],d=yr(e),h;o!=null&&(h=yr(o));for(let x=0;x<f;++x){let y=d[x],w=V(()=>r(y,m));if(o==null)p=w[0],m=w[1];else{let _=V(()=>{let C=h[x],A=le(fr(C),C),D=Z(O(w[0],C),O(m[0],A)),R=m.map((P,L)=>Z(O(w[1][L],C),O(P,A)));return{output:D,newStates:R}});p=_.output,m=_.newStates}i&&c.push(p)}let g;return i&&(g=Xt(c,1)),[p,g,m]})}var Ln=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new fm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("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 Tt({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 Xr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Tx(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],o;if(this.returnSequences?o=[e[0],e[1],n]:o=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[o].concat(s)}else return o}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Ne("Constants support is not implemented in RNN yet.");Tx(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,o=e.slice(2);this.inputSpec[0]=new Tt({shape:[n,null,...o]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Ne("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!b.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new z(`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=a.map(i=>new Tt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Mn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>yt([n,o])):this.states_=[yt([n,this.cell.stateSize])];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>yt([n,o])):this.states_[0]=yt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):De(this.states_);for(let o=0;o<this.states_.length;++o){let s=e[o],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,i=[n,a];if(!b.arraysEqual(s.shape,i))throw new z(`State ${o} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Ft(o.clone()))})}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=dv(e,n,o,this.numConstants);e=s.inputs,n=s.initialState,o=s.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Tt({shape:u.shape}));i=i.concat(this.stateSpec)}if(o!=null&&(t.constants=o,a=a.concat(o),this.numConstants=o.length),a[0]instanceof cn){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;e=Me(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new z(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:o},u=hv((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return V(()=>{let t=yt(e.shape);return t=me(t,[1,2]),t=ja(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?vx(t,[1,n]):t):this.cell.stateSize>1?[vx(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()===Ln.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let o=t.cell,s=pn(o,n);return new e(Object.assign(t,{cell:s}))}};Ln.className="RNN";ee.registerClass(Ln);var Vl=class extends Ge{},pm=class extends Vl{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,Zt(this.units,"units"),this.activation=Ws(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Qu([1,Bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qu([1,Bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Xe(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 V(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let o=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>fr(e),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>fr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=_o(O(e,a),this.kernel.read()):s=_o(e,this.kernel.read()),this.bias!=null&&(s=un(s,this.bias.read())),i!=null&&(n=O(n,i));let l=Z(s,_o(n,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Gs(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(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)}};pm.className="SimpleRNNCell";ee.registerClass(pm);var $d=class extends Ln{constructor(e){e.cell=new pm(e);super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return new e(t)}};$d.className="SimpleRNN";ee.registerClass($d);var mm=class extends Vl{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 z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Zt(this.units,"units"),this.activation=Ws(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ws(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Qu([1,Bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qu([1,Bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Xe(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 V(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,o=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>fr(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>fr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let c=_o(e,this.kernel.read());this.useBias&&(c=un(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=O(o,a[0]));let p=this.recurrentKernel.read(),[m,f]=sr(p,[2*this.units,this.units],p.rank-1),d=_o(o,m),[h,g,x]=sr(c,3,c.rank-1),[y,w]=sr(d,2,d.rank-1);i=this.recurrentActivation.apply(Z(h,y)),l=this.recurrentActivation.apply(Z(g,w));let _=_o(O(l,o),f);u=this.activation.apply(Z(x,_));let C=Z(O(i,o),O(Z(1,He(i)),u));return[C,C]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Gs(this.activation),recurrentActivation:Gs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};mm.className="GRUCell";ee.registerClass(mm);var Rd=class extends Ln{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new mm(e);super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Rd.className="GRU";ee.registerClass(Rd);var Gl=class extends Vl{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,Zt(this.units,"units"),this.activation=Ws(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ws(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Qu([1,Bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qu([1,Bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Xe(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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;o=new(t=class extends In{apply(l,u){let c=s.apply([a]),p=new rc().apply([a]),m=s.apply([a*2]);return Ok(Ok(c,p),m)}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>fr(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>fr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let m=_o(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=O(o,i[0])),m=Z(m,_o(o,this.recurrentKernel.read())),this.useBias&&(m=un(m,this.bias.read()));let[f,d,h,g]=sr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=Z(O(u,s),O(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=O(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Gs(this.activation),recurrentActivation:Gs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Gl.className="LSTMCell";ee.registerClass(Gl);var Fd=class extends Ln{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Gl(e);super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Fd.className="LSTM";ee.registerClass(Fd);var fm=class extends Vl{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 V(()=>{e=e;let n=e.slice(1),o=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?o.push(n.splice(0,i.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];n=o[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=l.call(a,t),s.push(a.slice(1))}n=[];for(let i of s.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){Tx(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,o)=>{zs(`RNNCell_${o}`,()=>{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=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(t)};return Object.assign({},e,o)}static fromConfig(e,t,n={}){let o=[];for(let s of t.cells)o.push(pn(s,n));return new e({cells:o})}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 cd(e)}setWeights(e){let t=[];for(let n of this.cells){let o=n.weights.length,s=e.splice(o);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}om(t)}};fm.className="StackedRNNCells";ee.registerClass(fm);function Ka(r){let{ones:e,rate:t,training:n=!1,count:o=1,dropoutFunc:s}=r,a=()=>s!=null?s(e(),t):Ix(e(),t),i=()=>$l(a,e,n);return!o||o<=1?Ft(i().clone()):Array(o).fill(void 0).map(i).map(u=>Ft(u.clone()))}var Q5=function(r,e){var t={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&e.indexOf(n)<0&&(t[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)e.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(t[n[o]]=r[n[o]]);return t};var gv=class extends Ln{constructor(e){if(e.unroll)throw new Ne("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ne("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Tt({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}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 V(()=>{let{stateSize:t}=this.cell,n=e.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],a=yt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Mn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("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(()=>yt(s)):this.states_=[yt(s)];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>yt(s)):this.states_[0]=yt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):De(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!b.arraysEqual(l.shape,u))throw new z(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>Ft(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:o,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=Nn(u,o[0],s,a[0],i[0]),m=Nn(c,o[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[n,p,m]:[p,m,n]]}};gv.className="ConvRNN2D";var dm=class extends Gl{constructor(e){let{filters:t,kernelSize:n,strides:o,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Zt(this.filters,"filters"),this.kernelSize=Ll(n,2,"kernelSize"),this.kernelSize.forEach(l=>Zt(l,"kernelSize")),this.strides=Ll(o||1,2,"strides"),this.strides.forEach(l=>Zt(l,"strides")),this.padding=s||"valid",ln(this.padding),this.dataFormat=a||"channelsLast",Ot(this.dataFormat),this.dilationRate=Ll(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Zt(l,"dilationRate"))}build(e){var t;e=Xe(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[n]}`);let o=e[n],s=4,a=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends In{apply(m,f){let d=u.apply([c]),h=or([c]),g=u.apply([c*2]);return Kp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,o=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>fr(o),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let l=this.dropoutMask,u=(se,ne,fe)=>!ne||!ne[fe]?se:O(ne[fe],se),c=u(o,l,0),p=u(o,l,1),m=u(o,l,2),f=u(o,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>fr(s),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),y=u(s,d,3),w=3,[_,C,A,D]=sr(this.kernel.read(),i,w),[R,P,L,G]=this.useBias?sr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,R,this.padding),p=this.inputConv(p,C,P,this.padding),m=this.inputConv(m,A,L,this.padding),f=this.inputConv(f,D,G,this.padding);let[W,j,H,q]=sr(this.recurrentKernel.read(),i,w);h=this.recurrentConv(h,W),g=this.recurrentConv(g,j),x=this.recurrentConv(x,H),y=this.recurrentConv(y,q);let X=this.recurrentActivation.apply(Z(c,h)),re=this.recurrentActivation.apply(Z(p,g)),J=Z(O(re,a),O(X,this.activation.apply(Z(m,x)))),oe=O(this.recurrentActivation.apply(Z(f,y)),this.activation.apply(J));return[oe,oe,J]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Q5(e,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,o)}inputConv(e,t,n,o){let s=nn(e,t,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?un(s,n,this.dataFormat):s}recurrentConv(e,t){return nn(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};dm.className="ConvLSTM2DCell";ee.registerClass(dm);var Od=class extends gv{constructor(e){let t=new dm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Od.className="ConvLSTM2D";ee.registerClass(Od);var hm=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?t[o]:this.noiseShape[o]);return n}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Me(e);if(0<this.rate&&this.rate<1){let o=t.training==null?!1:t.training,s=this.getNoiseShape(n);return $l(()=>Ix(n,this.rate,s,this.seed),()=>n,o)}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()}};hm.className="Dropout";ee.registerClass(hm);var Pd=class extends hm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Pd.className="SpatialDropout1D";ee.registerClass(Pd);var Md=class extends Ge{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,Zt(this.units,"units"),this.activation=Ws(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Vt(e.kernelConstraint),this.biasConstraint=Vt(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Xe(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=Xe(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Me(e),o=wx(this.activation.getClassName()),s;return o!=null?s=_o(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=_o(n,this.kernel.read()),this.bias!=null&&(s=un(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Gs(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Md.className="Dense";ee.registerClass(Md);var Ld=class extends Ge{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Xe(e);for(let t of e.slice(1))if(t==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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V(()=>(e=Me(e),Q2(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Bd.className="RepeatVector";ee.registerClass(Bd);var Vd=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",o=t.slice(),s=1,a=null;for(let l=0;l<o.length;++l){let u=o[l];if(this.isUnknown(u))if(a===null)a=l;else throw new z("Can only specifiy one unknown dimension.");else s*=u}let i=wo(e);if(a!==null){if(s===0||i%s!=0)throw new z(n);o[a]=i/s}else if(i!==s)throw new z(n);return o}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Me(e),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return F(n,s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Vd.className="Reshape";ee.registerClass(Vd);var Gd=class extends Ge{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Xr(1,e.dims.length+1);if(!b.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Tt({ndim:this.dims.length+1})]}computeOutputShape(e){e=Xe(e);let t=e.slice();return this.dims.forEach((n,o)=>{t[o+1]=e[n]}),t}call(e,t){return Be(Me(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Gd.className="Permute";ee.registerClass(Gd);var Wd=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Me(e),o=-1;return El(mo(n,this.maskValue),o)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Me(e),o=-1,s=!0,a=El(mo(n,this.maskValue),o,s);return O(n,Y(a,n.dtype))})}};Wd.className="Masking";ee.registerClass(Wd);var Ud=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let 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t=wt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let o=0;o<t.length;++o){let s=t[o],a=e[o+1];if(s!=null&&a!=null&&s!==a)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Me(e);n.dtype!=="int32"&&(n=ec(n,"int32"));let o=Cx(this.embeddings.read(),F(n,[n.size]));return F(o,Xe(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Nt(this.embeddingsInitializer),embeddingsRegularizer:ct(this.embeddingsRegularizer),activityRegularizer:ct(this.activityRegularizer),embeddingsConstraint:Bt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Ud.className="Embedding";ee.registerClass(Ud);var Wl=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ne}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let o=0;o<t.length;++o){let s=e[e.length-t.length+o],a=t[o];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Xe(e)]),e=e,e.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Yd.className="Concatenate";ee.registerClass(Yd);function Zd(r,e){for(;r<0;)r+=e;return r}function e8(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Ne("batchDot is not implemented for tensors of 4D or higher rank yet");if(b.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),b.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Ne("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=e.shape.length;t==null&&(t=[n-1,o-2]);let s=t;return V(()=>{let a;if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);e=F(e,e.shape.concat(l))}else if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);r=F(r,r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=me(O(r,e),s[0]):i=me(O(Be(r,[1,0]),e),s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=ze(r,e,l,u)}if(a>0){let l;n>o?l=n+o-3:l=n-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=Br(i,u)}return i.shape.length===1&&(i=mr(i,1)),i})}var Jd=class extends Wl{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){b.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);if(t[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${t[o[0]]} !== ${n[o[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,a)=>Zd(s,e[a].shape.length)):o=[Zd(this.axes,t.shape.length),Zd(this.axes,n.shape.length)],this.normalize&&(t=pd(t,o[0]),n=pd(n,o[1])),e8(t,n,o)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Zd(this.axes,e.length),Zd(this.axes,t.length)],n}computeOutputShape(e){b.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);t.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=t.concat(n);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Jd.className="Dot";ee.registerClass(Jd);var Qd=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Me(e);return $l(()=>Z(Xp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Qd.className="GaussianNoise";ee.registerClass(Qd);var eh=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.rate>0&&this.rate<1?$l(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return O(n,Xp(n.shape,1,s))},()=>n,t.training||!1):n})}};eh.className="GaussianDropout";ee.registerClass(eh);var th=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Me(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return $l(()=>{let s=Me(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=kn(Ms(n),this.rate);u=ec(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate,m=Z(O(s,u),O(Z(u,-1),l));return Z(O(m,c),p)},()=>Me(e),t.training||!1)}return e})}};th.className="AlphaDropout";ee.registerClass(th);function rh(r,e,t,n,o,s=.001){let a;if(r.rank===2)a=L_(r,e,t,n,o,s);else if(r.rank===3)a=z_(r,e,t,n,o,s);else if(r.rank===4)a=B_(r,e,t,n,o,s);else throw new Ne(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function t8(r,e,t,n,o=.001){return V(()=>{let s=zp(r,n),a=s.mean,i=s.variance;return[rh(r,a,i,t,e,o),a,i]})}function r8(r,e,t,n,o=.001){return V(()=>{let s=zp(r,n),a=s.mean,i=s.variance,l=[];for(let d of Xr(0,r.rank))n.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=F(a,l),c=F(i,l),p=e==null?null:F(e,l),m=t==null?null:F(t,l);return[rh(r,u,c,m,p,o),a,i]})}function n8(r,e,t,n,o=.001){return b.arraysEqual(n.slice().sort(),Xr(0,r.rank-1))?t8(r,e,t,n,o):r8(r,e,t,n,o)}var nh=class extends Ge{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=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.movingMeanInitializer=dt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=dt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Vt(e.betaConstraint),this.gammaConstraint=Vt(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=Xe(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Tt({ndim:e.length,axes:{[t]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training,o=Me(e),s=o.shape,a=s.length,i=Xr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=go(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!b.arraysEqual(c,Xr(0,a).slice(0,a-1)),m=()=>{if(p){let y=F(this.movingMean.read(),u),w=F(this.movingVariance.read(),u),_=this.center?F(this.beta.read(),u):null,C=this.scale?F(this.gamma.read(),u):null;return rh(o,y,w,_,C,this.epsilon)}else return rh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=n8(o,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(y,w,_)=>{V(()=>{let C=1-_,A=y.read(),D=O(le(A,w),C);y.write(le(A,D))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),movingMeanInitializer:Nt(this.movingMeanInitializer),movingVarianceInitializer:Nt(this.movingVarianceInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer),betaConstraint:Bt(this.betaConstraint),gammaConstraint:Bt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};nh.className="BatchNormalization";ee.registerClass(nh);var oh=class extends Ge{constructor(e){e==null&&(e={});super(e);if(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=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Xe(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==bo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(e,t){let n=Me(e),o=n.shape,s=o.length;return V(()=>{let a=!0,{mean:i,variance:l}=zp(n,this.axis,a),u=go(1,s);for(let h of this.axis)u[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?F(h,u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(o[h]),d.push(1)):(f.push(1),d.push(o[h]));return i=vr(i,f),l=vr(l,f),p=vr(p,d),m=vr(m,d),rh(n,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};oh.className="LayerNormalization";ee.registerClass(oh);function o8(r,e,t){return V(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=an()),t!=="channelsLast"&&t!=="channelsFirst")throw new z(`Unknown data format: ${t}. 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length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Tt({ndim:4})]}computeOutputShape(e){e=Xe(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return V(()=>o8(Me(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};sh.className="ZeroPadding2D";ee.registerClass(sh);function jx(r,e,t,n,o,s){return V(()=>{Ot(o),Rk(s),ln(n),t==null&&(t=[1,1]),n==null&&(n="valid"),o==null&&(o=an()),s==null&&(s="max"),r=Id(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=Oa(r,e,t,i):a=Ta(r,e,t,i),o==="channelsFirst"&&(a=Be(a,[0,3,1,2])),a})}function zA(r,e,t,n,o,s){return V(()=>{Ot(o),Rk(s),ln(n),t==null&&(t=[1,1,1]),n==null&&(n="valid"),o==null&&(o=an()),s==null&&(s="max"),r=mv(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=Of(r,e,t,i):a=kf(r,e,t,i),o==="channelsFirst"&&(a=Be(a,[0,4,1,2,3])),a})}var xv=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Zt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Zt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ln(this.padding),this.inputSpec=[new Tt({ndim:3})]}computeOutputShape(e){e=Xe(e);let t=Nn(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=ja(Me(e),2);let n=this.poolingFunction(Me(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Br(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},ih=class extends xv{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),ln(o),jx(e,t,n,o,s,"max")}};ih.className="MaxPooling1D";ee.registerClass(ih);var ah=class extends xv{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),ln(o),jx(e,t,n,o,s,"avg")}};ah.className="AveragePooling1D";ee.registerClass(ah);var yv=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Zt(this.poolSize,"poolSize"),Zt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),ln(this.padding),this.inputSpec=[new Tt({ndim:4})]}computeOutputShape(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Nn(t,this.poolSize[0],this.padding,this.strides[0]),n=Nn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},lh=class extends yv{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),ln(o),jx(e,t,n,o,s,"max")}};lh.className="MaxPooling2D";ee.registerClass(lh);var uh=class extends yv{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),ln(o),jx(e,t,n,o,s,"avg")}};uh.className="AveragePooling2D";ee.registerClass(uh);var bv=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Zt(this.poolSize,"poolSize"),Zt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),ln(this.padding),this.inputSpec=[new Tt({ndim:5})]}computeOutputShape(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Nn(t,this.poolSize[0],this.padding,this.strides[0]),n=Nn(n,this.poolSize[1],this.padding,this.strides[1]),o=Nn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,o]:[e[0],t,n,o,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},ch=class extends bv{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),ln(o),zA(e,t,n,o,s,"max")}};ch.className="MaxPooling3D";ee.registerClass(ch);var ph=class extends bv{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),ln(o),zA(e,t,n,o,s,"avg")}};ph.className="AveragePooling3D";ee.registerClass(ph);var wv=class extends Ge{constructor(e){super(e);this.inputSpec=[new Tt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ne}},mh=class extends wv{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Me(e);return xt(n,1)})}};mh.className="GlobalAveragePooling1D";ee.registerClass(mh);var fh=class extends wv{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Me(e);return Rr(n,1)})}};fh.className="GlobalMaxPooling1D";ee.registerClass(fh);var _v=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),this.inputSpec=[new Tt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ne}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},dh=class extends _v{call(e,t){return V(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?xt(n,[1,2]):xt(n,[2,3])})}};dh.className="GlobalAveragePooling2D";ee.registerClass(dh);var hh=class extends _v{call(e,t){return V(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?Rr(n,[1,2]):Rr(n,[2,3])})}};hh.className="GlobalMaxPooling2D";ee.registerClass(hh);var kv=class extends Ge{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let o=t.layer,s=pn(o,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},gh=class extends kv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Xe(e),e.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Xe(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),o=e[1];return[n[0],o].concat(n.slice(1))}call(e,t){return V(()=>(e=Me(e),hv((a,i)=>[Me(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};gh.className="TimeDistributed";ee.registerClass(gh);function s8(r){Ci(K2,"BidirectionalMergeMode",r)}var i8="concat",xh=class extends kv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=pn(n),t.goBackwards=t.goBackwards!==!0;let o={};if(o.className=e.layer.getClassName(),o.config=t,this.backwardLayer=pn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?i8:e.mergeMode,s8(this.mergeMode),e.weights)throw new Ne("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,o,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Sr(o)}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=dv(e,n,o,this.numConstants);if(e=s.inputs,n=s.initialState,o=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&o==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(p=>new 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n=v("strides",r,e,t),o=yh(r,e,t),s=v("dataFormat",r,e,t).toUpperCase(),a=v("dilations",r,e,t);return[nn(v("x",r,e,t),v("filter",r,e,t),[n[1],n[2]],o,s,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:n,pad:o,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=lD(r,e,t);return[fo.conv2d({x:v("x",r,e,t),filter:v("filter",r,e,t),strides:[n[1],n[2]],pad:o,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:o,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=lD(r,e,t);return[fo.depthwiseConv2d({x:v("x",r,e,t),filter:v("filter",r,e,t),strides:[n[1],n[2]],pad:o,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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xD=(r,e,t)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[ze(v("a",r,e,t),v("b",r,e,t),v("transposeA",r,e,t),v("transposeB",r,e,t))];case"Einsum":return[X_(v("equation",r,e,t),...v("tensors",r,e,t))];case"Transpose":return[Be(v("x",r,e,t),v("perm",r,e,t))];case"_FusedMatMul":let[n,o]=v("fusedOps",r,e,t),s=n==="biasadd",a=o==="prelu",i=v("numArgs",r,e,t),l=v("leakyreluAlpha",r,e,t);if(s){if(a&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=v("args",r,e,t);return[fo.matMul({a:v("a",r,e,t),b:v("b",r,e,t),transposeA:v("transposeA",r,e,t),transposeB:v("transposeB",r,e,t),bias:u,activation:o,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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ID(r,e,t){let{usedNodes:n,inputs:o}=t,s=[],a=Object.keys(o).map(c=>fn(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{n.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var s7=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],i7=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],a7=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function t0(r){return s7.indexOf(r.op)>=0}function l7(r){return i7.indexOf(r.op)>=0}function u7(r){return a7.indexOf(r.op)>=0}var xm=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 xm(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(o=>o.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}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(s=>s.name).sort(),o=t.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(e,t){let n=e0(e,t,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:a}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(o.length>0){let i=t.map(u=>u.name),l=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${l}]. Missing the following inputs: [${o}]`)}return ID(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let o=n.map(p=>this.graph.nodes[fn(p)[0]]),s=t.map(p=>fn(p)[0]),a=s.map(p=>this.graph.nodes[p]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(o,a),l=this.compiledMap.get(i);l==null&&(l=this.compile(e,a),this.compiledMap.set(i,l));let u={},c={};return V(()=>{let p=new ay(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);Object.keys(e).forEach(h=>{let[g,x]=fn(h),y=[];y[x]=e[h],m[g]=y});let f=this.getFrozenTensorIds(m),d={};for(let h=0;h<l.length;h++){let g=l[h];if(!m[g.name]){let x=Qv(g,m,p,this._resourceManager);if(b.isPromise(x))throw new Error(`The execution of the op '${g.op}' returned a promise. 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c}processChildNodes(e,t,n,o,s,a){e.children.forEach(i=>{let[l]=js(i.name,n);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!br(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!br(u,o,n))&&(s[l]=!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],[o]=fn(t),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((l,u)=>a[u]===-1||a[u]===l);b.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&b.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let o=this._signature.inputs[n];t[o.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[o]=fn(n);return this.graph.nodes[o]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=fn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}};var r0=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var c7="?tfjs-format=file",p7="model.json",ly=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new r0}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Lr.browserHTTPRequest(e,this.loadOptions);else{let t=Lr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Lr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let o=Lr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new xm(Xx.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Xx.Instance.transformGraph(e.modelInitializer);this.initializer=new xm(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Lr.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Le)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,o)=>(t[n]=e[o],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function m7(r,e={}){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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this.set(t,this.pop()),n}};var ym=class extends bh{constructor(){super(ym.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let o=0;o<n;o++)t[o]=this.get(this.wrap(this.begin+o));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};ym.INITIAL_CAPACITY=32;function m0(r){return new YD(r)}function wh(r){return new ZD(r)}function KD(r,e){return new d0(r,e)}function XD(r,e=Xa.FAIL){return new i$(r,e)}var tr=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new o$(this,e)}filter(e){return new r$(this,e)}map(e){return new n$(this,e)}mapAsync(e){return new f0(this,e)}serialMapAsync(e){return new f0(this,e).serial()}flatmap(e){return new s$(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new t$(this,e,t)}columnMajorBatch(e,t=!0,n=p0){return this.rowMajorBatch(e,t).map(s=>WD(s,n))}concatenate(e,t){return new d0(m0([this,e]),t)}take(e){return e<0||e==null?this:new e$(this,e)}skip(e){return e<0||e==null?this:new QD(this,e)}prefetch(e){return new h0(this,e)}shuffle(e,t){return new a$(this,e,t)}serial(){return new JD(this)}},YD=class extends tr{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:HD(e),done:!1}}},ZD=class extends tr{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},JD=class extends tr{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},QD=class extends tr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;De(e.value)}return this.upstream.next()}},e$=class extends tr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},t$=class extends tr{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},r$=class extends tr{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;De(e.value)}}},n$=class extends tr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ao.getTensorsInContainer(e.value),n=this.transform(e.value),o=ao.getTensorsInContainer(n);for(let s of t)ao.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},o$=class extends tr{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},f0=class extends tr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ao.getTensorsInContainer(e.value),n=await this.transform(e.value),o=ao.getTensorsInContainer(n);for(let s of t)ao.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},bm=class extends tr{constructor(){super();this.outputQueue=new ym,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},s$=class extends bm{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ao.getTensorsInContainer(e.value),n=this.transform(e.value),o=ao.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of t)ao.isTensorInList(s,o)||s.dispose();return!0}},d0=class extends tr{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Xa;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Xa||(Xa={}));var i$=class extends tr{constructor(e,t=Xa.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function o(a){return a instanceof tr?{value:a.next().then(l=>(t++,l.done&&n++,l.value)),recurse:!1}:{value:null,recurse:!0}}let s=await py(this.iterators,o);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Xa.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Xa.SHORTEST:return{value:null,done:!0};case Xa.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},h0=class extends tr{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new bh(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},a$=class extends h0{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=qD.alea(n||b.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}};var Ti=class{constructor(){this.size=null}batch(e,t=!0){let n=this;b.assert(e>0,()=>`batchSize needs to be positive, but it is
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`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var my='"',kh=Symbol("out"),p$=Symbol("field"),fy=Symbol("quote"),g0=Symbol("quoteafterquote"),m$=Symbol("quoteinquote"),vh=class extends Ti{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new _h(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(b.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&b.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(t).filter(o=>t[o]>1);if(b.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let l=t[s],u=null;if(l==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let c=Number(l);if(isNaN(c))i&&i.dtype==="bool"?u=this.getBoolean(l):u=l;else if(!i||!i.dtype)u=c;else switch(i.dtype){case"float32":u=c;break;case"int32":u=Math.floor(c);break;case"bool":u=this.getBoolean(l);break;default:u=c}}i&&i.isLabel?o[a]=u:n[a]=u}}return Object.keys(o).length===0?n:{xs:n,ys:o}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],o=0,s=e.length,a=kh;for(let i=0;i<s;i++)switch(a){case kh:switch(e.charAt(i)){case my:o=i+1,a=fy;break;case this.delimiter:if(o=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=kh;break;default:a=p$,o=i;break}break;case p$:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(o,i)),a=kh,o=i+1;break;default:}break;case fy:switch(e.charAt(i)){case my:a=g0;break;default:}break;case g0:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(o,i-1)),a=kh,o=i+1;break;case my:a=fy;break;default:a=m$;break}break;case m$:switch(e.charAt(i)){case my:a=fy;break;default:}break;default:}if(a===g0?n.push(e.substring(o,s-1)):n.push(e.substring(o)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}};var Ch=class extends tr{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(U().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Ch(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 o=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(o,[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(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({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(s),o({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((o,s)=>n.set(o,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(b.sizeFromShape(t));return n.set(e,n.length-e.length),Dr(n,t)}};var Ih=class extends tr{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=$t([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-o)/2,i=s+n,l=o+a;this.cropBox=ki([a,s,l,i],[1,4])}else this.cropBox=ki([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(U().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 Ih(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&b.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=Gg.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: 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============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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SR={kernelName:Uc,backendName:"cpu",kernelFunc:cY};function pY(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;te([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=I.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,y=c.effectiveFilterWidth,w=y-1-c.padInfo.left,_=x-1-c.padInfo.top,C=Ie(a.shape,"float32"),A=1/(f*d),D=t.data.get(o.dataId).values,R=Ie(o.shape,"float32",D);for(let P=0;P<c.batchSize;++P)for(let L=0;L<c.inChannels;++L)for(let G=0;G<c.inHeight;++G)for(let W=0;W<c.inWidth;++W){let j=G-_,H=W-w,q=0;for(let X=0;X<x;X+=h){let re=(j+X)/p;if(!(re<0||re>=c.outHeight||Math.floor(re)!==re))for(let J=0;J<y;J+=g){let oe=(H+J)/m;if(oe<0||oe>=c.outWidth||Math.floor(oe)!==oe)continue;q+=R.get(P,re,oe,L)}}C.set(q*A,P,G,W,L)}return t.makeTensorInfo(C.shape,C.dtype,C.values)}var NR={kernelName:Wc,backendName:"cpu",kernelFunc:pY};function 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t.makeTensorInfo(o.shape,o.dtype,h)}var TR={kernelName:Ko,backendName:"cpu",kernelFunc:mY};function fY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;te([o],"batchToSpaceND");let i=s.reduce((x,y)=>x*y),l=I.getReshaped(o.shape,s,i),u=I.getPermuted(l.length,s.length),c=I.getReshapedPermuted(o.shape,s,i),p=I.getSliceBeginCoords(a,s.length),m=I.getSliceSize(c,a,s.length),f=Je({inputs:{x:o},backend:t,attrs:{shape:l}}),d=rr({inputs:{x:f},backend:t,attrs:{perm:u}}),h=Je({inputs:{x:d},backend:t,attrs:{shape:c}}),g=So({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var ER={kernelName:ri,backendName:"cpu",kernelFunc:fY};function dY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.data.get(o.dataId).values,l=t.data.get(s.dataId).values,u=km(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var AR={kernelName:jc,backendName:"cpu",kernelFunc:dY};function hY(r){let{inputs:e,backend:t}=r,{s0:n,s1:o}=e,s=t.data.get(n.dataId).values,a=t.data.get(o.dataId).values,i=I.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var DR={kernelName:Hc,backendName:"cpu",kernelFunc:hY};var gY=$e(to,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r<t.clipValueMin?t.clipValueMin:r}),$R={kernelName:to,backendName:"cpu",kernelFunc:gY};var xY=r=>{let{x:e}=r.inputs,t=r.backend,n=new Float32Array(b.sizeFromShape(e.shape)),o=t.data.get(e.dataId),s=o.complexTensorInfos.real,a=o.complexTensorInfos.imag,i=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],p=l[u];n[u]=Math.hypot(c,p)}return t.makeOutput(n,e.shape,"float32")},RR={kernelName:dl,backendName:"cpu",kernelFunc:xY};function 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h.forEach(_=>t.disposeIntermediateTensorInfo(_)),g.forEach(_=>t.disposeIntermediateTensorInfo(_)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(y),w}let u=i.map(h=>{let g=b.sizeFromShape(h.shape.slice(s));return Je({inputs:{x:h},backend:t,attrs:{shape:[-1,g]}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));a=I.computeOutShape(u.map(h=>h.shape),1);let p=u[0].shape[0]===1,m=mc(c,a,e[0].dtype,p),f=I.computeOutShape(i.map(h=>h.shape),s),d=t.makeTensorInfo(f,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var OR={kernelName:ni,backendName:"cpu",kernelFunc:jl};function K0(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n;te([o,s],"conv2d");let p=I.convertConv2DDataFormat(l),m=I.computeConv2DInfo(o.shape,s.shape,a,u,i,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,y=m.padInfo.top,w=m.dataFormat==="channelsLast",_=new 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yY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=n;te([o,s],"conv2dBackpropFilter");let p=I.convertConv2DDataFormat(l),m=I.computeConv2DInfo(o.shape,c,a,1,i,u,!1,p),{strideHeight:f,strideWidth:d,filterHeight:h,filterWidth:g}=m,x=m.dataFormat==="channelsLast",y=new mt(m.filterShape,"float32"),w=m.padInfo.left,_=m.padInfo.top,C=t.data.get(o.dataId).values,A=t.data.get(s.dataId).values,D=new mt(o.shape,o.dtype,C),R=new mt(s.shape,s.dtype,A);for(let P=0;P<h;++P){let L=Math.max(0,Math.ceil((_-P)/f)),G=Math.min(m.outHeight,(m.inHeight+_-P)/f);for(let W=0;W<g;++W){let j=Math.max(0,Math.ceil((w-W)/d)),H=Math.min(m.outWidth,(m.inWidth+w-W)/d);for(let q=0;q<m.inChannels;++q)for(let X=0;X<m.outChannels;++X){let re=0;for(let J=0;J<m.batchSize;++J)for(let oe=L;oe<G;++oe){let se=P+oe*f-_;for(let ne=j;ne<H;++ne){let fe=W+ne*d-w;x?re+=D.get(J,se,fe,q)*R.get(J,oe,ne,X):re+=D.get(J,q,se,fe)*R.get(J,X,oe,ne)}}y.set(re,P,W,q,X)}}}return t.makeTensorInfo(y.shape,y.dtype,y.values)}var MR={kernelName:Kc,backendName:"cpu",kernelFunc:yY};function bY(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=n;te([o,s],"conv2dBackpropInput");let p=b.computeStrides(s.shape),m=b.computeStrides(o.shape),f=I.convertConv2DDataFormat(u),d=I.computeConv2DInfo(a,s.shape,i,1,l,c,!1,f),h=new mt(d.inShape,"float32"),g=h.values,x=t.data.get(o.dataId).values,y=t.data.get(s.dataId).values,[w,_,C]=p,{batchSize:A,filterHeight:D,filterWidth:R,inChannels:P,inHeight:L,inWidth:G,outChannels:W,outHeight:j,outWidth:H,strideHeight:q,strideWidth:X}=d;f=d.dataFormat;let re=D-1-d.padInfo.top,J=R-1-d.padInfo.left,oe=f==="channelsLast",se=h.strides[0],ne=oe?h.strides[1]:h.strides[2],fe=oe?h.strides[2]:1,ae=oe?1:h.strides[1],ge=m[0],de=oe?m[1]:m[2],ye=oe?m[2]:1,_e=oe?1:m[1];for(let Re=0;Re<A;++Re)for(let Ee=0;Ee<P;++Ee)for(let Fe=0;Fe<L;++Fe){let 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G=L*R[0],W=L*_.strides[0];for(let j=0;j<u.outDepth;++j){let H=W+j*_.strides[1],q=j*u.strideDepth-x;for(let X=0;X<c;++X){let re=q+X*f;if(re<0||re>=u.inDepth)continue;let J=X*P[0],oe=G+re*R[1];for(let se=0;se<u.outHeight;++se){let ne=H+se*_.strides[2],fe=se*u.strideHeight-w;for(let ae=0;ae<p;++ae){let ge=fe+ae*d;if(ge<0||ge>=u.inHeight)continue;let de=J+ae*P[1],ye=oe+ge*R[2];for(let _e=0;_e<u.outWidth;++_e){let Re=ne+_e*u.outChannels,Ee=_e*u.strideWidth-y;for(let Fe=0;Fe<m;++Fe){let Ye=Ee+Fe*h;if(Ye<0||Ye>=u.inWidth)continue;let ut=de+Fe*P[2],At=ye+Ye*u.inChannels,Dt=ut;for(let ft=0;ft<u.inChannels;++ft){let Ke=C[At+ft];for(let ht=0;ht<u.outChannels;++ht)D[Re+ht]+=Ke*A[Dt+ht];Dt+=u.outChannels}}}}}}}}return t.makeTensorInfo(_.shape,_.dtype,_.values)}var zR={kernelName:hl,backendName:"cpu",kernelFunc:wY};function _Y(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,filterShape:l}=n;te([o,s],"conv3dBackpropFilterV2");let u=b.computeStrides(o.shape),c=b.computeStrides(s.shape),p=I.computeConv3DInfo(o.shape,l,a,1,i),m=p.strideDepth,f=p.strideHeight,d=p.strideWidth,h=p.filterDepth,g=p.filterHeight,x=p.filterWidth,y=new mt(p.filterShape,"float32"),w=y.values,[_,C,A,D]=y.strides,R=t.data.get(s.dataId).values,[P,L,G,W]=c,j=t.data.get(o.dataId).values,[H,q,X,re]=u,J=p.padInfo.front,oe=p.padInfo.left,se=p.padInfo.top;for(let ne=0;ne<h;++ne){let fe=Math.max(0,Math.ceil((J-ne)/m)),ae=Math.min(p.outDepth,(p.inDepth+J-ne)/m),ge=ne*_;for(let de=0;de<g;++de){let ye=Math.max(0,Math.ceil((se-de)/f)),_e=Math.min(p.outHeight,(p.inHeight+se-de)/f),Re=de*C+ge;for(let Ee=0;Ee<x;++Ee){let Fe=Math.max(0,Math.ceil((oe-Ee)/d)),Ye=Math.min(p.outWidth,(p.inWidth+oe-Ee)/d),ut=Ee*A+Re;for(let At=0;At<p.inChannels;++At){let Dt=At*D+ut;for(let ft=0;ft<p.outChannels;++ft){let Ke=0;for(let ht=0;ht<p.batchSize;++ht){let Mt=ht*H,Dn=ht*P;for(let lr=fe;lr<ae;++lr){let Or=(ne+lr*m-J)*q+Mt,To=lr*L+Dn;for(let Zr=ye;Zr<_e;++Zr){let 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At=ut-_e,Dt=Math.max(0,Math.ceil(At/ge)),ft=Math.min(ne,(j+At)/ge);for(let Ke=0;Ke<J;++Ke){let ht=Ke-Re,Mt=Math.max(0,Math.ceil(ht/de)),Dn=Math.min(fe,(H+ht)/de);for(let lr=0;lr<oe;++lr){let gn=lr-Ee,Or=Math.max(0,Math.ceil(gn/ye)),To=Math.min(ae,(q+gn)/ye),Zr=0;for(let Tr=Dt;Tr<ft;++Tr){let Gn=Tr*ge-At;for(let ur=Mt;ur<Dn;++ur){let xn=ur*de-ht;for(let $n=Or;$n<To;++$n){let Jl=$n*ye-gn,Ql=w*Fe+_*Tr+C*ur+A*$n,Eo=R*(j-1-Gn)+P*(H-1-xn)+L*(q-1-Jl)+G*Ye;for(let Ri=0;Ri<se;++Ri){let zm=y[Ql+Ri],Ec=D[Eo+Ri];Zr+=zm*Ec}}}}f[d*Fe+h*ut+g*Ke+x*lr+Ye]=Zr}}}return t.makeTensorInfo(m.shape,m.dtype,m.values)}var VR={kernelName:Yc,backendName:"cpu",kernelFunc:kY};var vY=$e(zo,r=>Math.cos(r)),GR={kernelName:zo,backendName:"cpu",kernelFunc:vY};var CY=$e(Bo,r=>Math.cosh(r)),WR={kernelName:Bo,backendName:"cpu",kernelFunc:CY};function IY(r){let{inputs:e,backend:t,attrs:n}=r,{image:o,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=i,x=Ie([d,h,g,f],"float32"),y=t.data.get(s.dataId).values,w=t.data.get(a.dataId).values,_=t.data.get(o.dataId).values,C=b.computeStrides(o.shape),A=b.computeStrides(x.shape);for(let D=0;D<d;D++){let R=D*4,P=y[R],L=y[R+1],G=y[R+2],W=y[R+3],j=w[D];if(j>=c)continue;let H=h>1?(G-P)*(p-1)/(h-1):0,q=g>1?(W-L)*(m-1)/(g-1):0;for(let X=0;X<h;X++){let re=h>1?P*(p-1)+X*H:.5*(P+G)*(p-1);if(re<0||re>p-1){for(let J=0;J<g;J++)for(let oe=0;oe<f;oe++){let se=oe+J*A[2]+X*A[1]+D*A[0];x.values[se]=u}continue}if(l==="bilinear"){let J=Math.floor(re),oe=Math.ceil(re),se=re-J;for(let ne=0;ne<g;ne++){let fe=g>1?L*(m-1)+ne*q:.5*(L+W)*(m-1);if(fe<0||fe>m-1){for(let ye=0;ye<f;ye++){let _e=ye+ne*A[2]+X*A[1]+D*A[0];x.values[_e]=u}continue}let ae=Math.floor(fe),ge=Math.ceil(fe),de=fe-ae;for(let ye=0;ye<f;ye++){let _e=ye+ae*C[2]+J*C[1]+j*C[0],Re=_[_e];_e=ye+ge*C[2]+J*C[1]+j*C[0];let Ee=_[_e];_e=ye+ae*C[2]+oe*C[1]+j*C[0];let Fe=_[_e];_e=ye+ge*C[2]+oe*C[1]+j*C[0];let Ye=_[_e],ut=Re+(Ee-Re)*de,At=Fe+(Ye-Fe)*de;_e=ye+ne*A[2]+X*A[1]+D*A[0],x.values[_e]=ut+(At-ut)*se}}}else for(let J=0;J<g;++J){let oe=g>1?L*(m-1)+J*q:.5*(L+W)*(m-1);if(oe<0||oe>m-1){for(let fe=0;fe<f;fe++){let ae=fe+J*A[2]+X*A[1]+D*A[0];x.values[ae]=u}continue}let se=Math.round(oe),ne=Math.round(re);for(let fe=0;fe<f;fe++){let ae=fe+se*C[2]+ne*C[1]+j*C[0],ge=fe+J*A[2]+X*A[1]+D*A[0];x.values[ge]=_[ae]}}}}return t.makeTensorInfo(x.shape,x.dtype,x.values)}var UR={kernelName:Hi,backendName:"cpu",kernelFunc:IY};function SY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n;te(o,"cumsum");let l=I.getAxesPermutation([s],o.shape.length),u=o;l!=null&&(u=rr({inputs:{x:o},backend:t,attrs:{perm:l}}));let c=I.getInnerMostAxes(1,o.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an 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t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var HR={kernelName:Zc,backendName:"cpu",kernelFunc:NY};function TY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n;b.assert(a==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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EY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=n;te([o,s],"depthwiseConv2dNativeBackpropFilter");let p=I.computeConv2DInfo(o.shape,c,a,i,l,u,!0),{strideHeight:m,strideWidth:f,filterHeight:d,filterWidth:h}=p,g=new mt(p.filterShape,"float32"),x=p.padInfo.left,y=p.padInfo.top,w=p.outChannels/p.inChannels,_=t.data.get(o.dataId).values,C=new mt(o.shape,o.dtype,_),A=t.data.get(s.dataId).values,D=new mt(s.shape,s.dtype,A);for(let R=0;R<d;++R){let P=Math.max(0,Math.ceil((y-R)/m)),L=Math.min(p.outHeight,(p.inHeight+y-R)/m);for(let G=0;G<h;++G){let W=Math.max(0,Math.ceil((x-G)/f)),j=Math.min(p.outWidth,(p.inWidth+x-G)/f);for(let H=0;H<p.outChannels;++H){let q=Math.trunc(H/w),X=H%w,re=0;for(let J=0;J<p.batchSize;++J)for(let oe=P;oe<L;++oe){let se=R+oe*m-y;for(let ne=W;ne<j;++ne){let fe=G+ne*f-x;re+=C.get(J,se,fe,q)*D.get(J,oe,ne,H)}}g.set(re,R,G,q,X)}}}return t.makeTensorInfo(g.shape,g.dtype,g.values)}var XR={kernelName:Jc,backendName:"cpu",kernelFunc:EY};function AY(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=n;te([o,s],"depthwiseConv2DNativeBackpropInput");let p=b.computeStrides(o.shape),m=b.computeStrides(s.shape),f=I.computeConv2DInfo(c,s.shape,a,i,l,u,!0),d=new mt(f.inShape,"float32"),h=d.values,[g,x,y]=d.strides,w=t.data.get(o.dataId).values,[_,C,A]=p,D=t.data.get(s.dataId).values,[R,P,L]=m,{batchSize:G,filterHeight:W,filterWidth:j,inChannels:H,inHeight:q,inWidth:X,outChannels:re,outHeight:J,outWidth:oe,strideHeight:se,strideWidth:ne}=f,fe=W-1-f.padInfo.top,ae=j-1-f.padInfo.left,ge=re/H;for(let de=0;de<G;++de)for(let ye=0;ye<H;++ye)for(let _e=0;_e<q;++_e){let Re=_e-fe,Ee=Math.max(0,Math.ceil(Re/se)),Fe=Math.min(J,(W+Re)/se);for(let Ye=0;Ye<X;++Ye){let ut=Ye-ae,At=Math.max(0,Math.ceil(ut/ne)),Dt=Math.min(oe,(j+ut)/ne),ft=0;for(let Ke=Ee;Ke<Fe;++Ke){let ht=Ke*se-Re;for(let Mt=At;Mt<Dt;++Mt){let Dn=Mt*ne-ut,lr=_*de+C*Ke+A*Mt,gn=R*(W-1-ht)+P*(j-1-Dn)+L*ye;for(let Or=0;Or<ge;++Or){let To=ye*ge+Or,Zr=w[lr+To],Tr=D[gn+Or];ft+=Zr*Tr}}}h[g*de+x*_e+y*Ye+ye]=ft}}return t.makeTensorInfo(d.shape,d.dtype,d.values)}var YR={kernelName:Qc,backendName:"cpu",kernelFunc:AY};function DY(r){let{inputs:e,backend:t}=r,{x:n}=e,o=b.sizeFromShape(n.shape),s=t.data.get(n.dataId).values,a=Ie([o,o],n.dtype),i=a.values;for(let u=0;u<s.length;u++)i[u*o+u]=s[u];let l=[...n.shape,...n.shape];return t.makeTensorInfo(l,a.dtype,a.values)}var ZR={kernelName:ep,backendName:"cpu",kernelFunc:DY};var JR={kernelName:gl,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:n,filter:o}=r,{strides:s,pad:a,dilations:i}=t,l=e,u=l.data.get(n.dataId).values,c=n.shape.length,p=l.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:y,padInfo:w,strideHeight:_,strideWidth:C,filterHeight:A,filterWidth:D,dilationHeight:R,dilationWidth:P,outShape:L}=I.computeDilation2DInfo(n.shape,o.shape,s,a,"NHWC",i),G=b.sizeFromShape(L),W=L.length,j=b.getArrayFromDType(n.dtype,G);for(let q=0;q<f;++q)for(let X=0;X<x;++X){let re=X*_-w.top;for(let J=0;J<y;++J){let oe=J*C-w.left;for(let se=0;se<g;++se){let ne=Number.MIN_SAFE_INTEGER;for(let ae=0;ae<A;++ae){let ge=re+ae*R;if(ge>=0&&ge<d)for(let de=0;de<D;++de){let ye=oe+de*P;if(ye>=0&&ye<h){let _e=b.locToIndex([q,ge,ye,se],c,b.computeStrides(n.shape)),Re=b.locToIndex([ae,de,se],m,b.computeStrides(o.shape)),Ee=u[_e]+p[Re];Ee>ne&&(ne=Ee)}}}let fe=b.locToIndex([q,X,J,se],W,b.computeStrides(L));j[fe]=ne}}}return{dataId:l.write(b.toTypedArray(j,n.dtype),L,n.dtype),shape:L,dtype:n.dtype}}};var QR={kernelName:rf,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:n,filter:o,dy:s}=r,{strides:a,pad:i,dilations:l}=t,u=e,c=b.toNestedArray(n.shape,u.data.get(n.dataId).values),p=b.toNestedArray(o.shape,u.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:y,strideHeight:w,strideWidth:_,filterHeight:C,filterWidth:A,dilationHeight:D,dilationWidth:R,outShape:P}=I.computeDilation2DInfo(n.shape,o.shape,a,i,"NHWC",l);b.assert(s.rank===P.length,()=>`Error in ${rf}, dy must have the same rank as output ${P.length}, but got ${s.rank}`);let L=b.toNestedArray(P,u.data.get(s.dataId).values),G=b.makeZerosNestedTypedArray(o.shape,o.dtype);for(let j=0;j<m;++j)for(let H=0;H<g;++H){let q=H*w-y.top;for(let X=0;X<x;++X){let re=X*_-y.left;for(let J=0;J<h;++J){let 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l=i?o:J0({inputs:{logits:o},backend:t,attrs:{dim:-1}}),u=l.shape[0],c=l.shape[1],p=t.data.get(l.dataId).values,m=[u,s],f=b.makeZerosTypedArray(b.sizeFromShape(m),"int32");for(let d=0;d<u;++d){let h=d*c,g=new Float32Array(c-1);g[0]=p[h];for(let w=1;w<g.length;++w)g[w]=g[w-1]+p[h+w];let x=MF.alea(a.toString()),y=d*s;for(let w=0;w<s;++w){let _=x();f[y+w]=g.length;for(let C=0;C<g.length;C++)if(_<g[C]){f[y+w]=C;break}}}return i||t.disposeIntermediateTensorInfo(l),t.makeTensorInfo(m,"int32",f)}var LF={kernelName:pp,backendName:"cpu",kernelFunc:_9};var k9=Gr.nonMaxSuppressionV3Impl;function v9(r){let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=n;te(o,"NonMaxSuppression");let u=t.data.get(o.dataId).values,c=t.data.get(s.dataId).values,{selectedIndices:p}=k9(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var zF={kernelName:ua,backendName:"cpu",kernelFunc:v9};var C9=Gr.nonMaxSuppressionV4Impl;function 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e.makeTensorInfo([i.length],s,i)}var KF={kernelName:wl,backendName:"cpu",kernelFunc:$9};var R9=$e(fa,r=>1/r),XF={kernelName:fa,backendName:"cpu",kernelFunc:R9};function F9(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n;te(o,"resizeBilinear");let l=b.computeStrides(o.shape),[u,c]=i,[p,m,f,d]=o.shape,h=t.data.get(o.dataId).values,g=new Float32Array(b.sizeFromShape([p,u,c,d])),x=[s&&u>1?m-1:m,s&&c>1?f-1:f],y=[s&&u>1?u-1:u,s&&c>1?c-1:c],w=0,_=x[0]/y[0],C=x[1]/y[1];for(let A=0;A<p;A++)for(let D=0;D<u;D++){let R;a?R=_*(D+.5)-.5:R=_*D;let P=Math.max(0,Math.floor(R)),L=R-P,G=Math.min(m-1,Math.ceil(R)),W=A*l[0]+P*l[1],j=A*l[0]+G*l[1];for(let H=0;H<c;H++){let q;a?q=C*(H+.5)-.5:q=C*H;let X=Math.max(0,Math.floor(q)),re=q-X,J=Math.min(f-1,Math.ceil(q)),oe=W+X*l[2],se=j+X*l[2],ne=W+J*l[2],fe=j+J*l[2];for(let ae=0;ae<d;ae++){let ge=h[oe+ae],de=h[se+ae],ye=h[ne+ae],_e=h[fe+ae],Re=ge+(ye-ge)*re,Ee=de+(_e-de)*re,Fe=Re+(Ee-Re)*L;g[w++]=Fe}}}return t.makeTensorInfo([p,u,c,d],"float32",g)}var YF={kernelName:ps,backendName:"cpu",kernelFunc:F9};function O9(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n;te([s,o],"resizeBilinearGrad");let i=b.computeStrides(o.shape),[l,u,c,p]=o.shape,[,m,f]=s.shape,d=new Float32Array(l*u*c*p),h=[a&&m>1?u-1:u,a&&f>1?c-1:c],g=[a&&m>1?m-1:m,a&&f>1?f-1:f],x=h[0]/g[0],y=h[1]/g[1],w=t.data.get(s.dataId).values,_=0;for(let C=0;C<l;C++){let A=C*i[0];for(let D=0;D<m;D++){let R=D*x,P=Math.floor(R),L=Math.min(Math.ceil(R),u-1),G=A+P*i[1],W=A+L*i[1],j=R-P,H=1-j;for(let q=0;q<f;q++){let X=q*y,re=Math.floor(X),J=Math.min(Math.ceil(X),c-1),oe=X-re,se=1-oe,ne=G+re*i[2],fe=G+J*i[2],ae=W+re*i[2],ge=W+J*i[2],de=H*se,ye=H*oe,_e=j*se,Re=j*oe;for(let Ee=0;Ee<p;Ee++){let Fe=w[_++];d[ne+Ee]+=Fe*de,d[fe+Ee]+=Fe*ye,d[ae+Ee]+=Fe*_e,d[ge+Ee]+=Fe*Re}}}}return t.makeTensorInfo([l,c,u,p],"float32",d)}var ZF={kernelName:dp,backendName:"cpu",kernelFunc:O9};function 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return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
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}
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#define round(value) newRound(value)
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}
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}
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#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,u=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
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}
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const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`;var{getBroadcastDims:VO}=I;function GO(r,e,t){let n=[];if(r.forEach(f=>{let d=b.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),t.enableShapeUniforms){let{uniformShape:h}=Wy(t.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),t.enableShapeUniforms){switch(e.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}t.customUniforms&&t.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(`
`),s=r.map(f=>MZ(f,e,t.packedInputs,t.enableShapeUniforms)).join(`
`),a=e.texShape,i=Gt(),l=BZ(i),u,c,p=WZ(i);return e.isPacked?(u=LZ(e.logicalShape,a,t.enableShapeUniforms),c=GZ(i)):(u=zZ(e.logicalShape,a,t.enableShapeUniforms),c=VZ(i)),t.packedInputs&&(p+=qZ),[p,l,c,o,u,s,t.userCode].join(`
`)}function $m(r,e=!1){let t=r.shapeInfo.logicalShape;switch(t.length){case 0:return sJ(r,e);case 1:return aJ(r,e);case 2:return uJ(r,e);case 3:return pJ(r,e);case 4:return fJ(r,e);case 5:return dJ(r);case 6:return hJ(r);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function WO(r,e){switch(r.shapeInfo.logicalShape.length){case 0:return oJ(r);case 1:return iJ(r,e);case 2:return lJ(r,e);case 3:return cJ(r,e);default:return mJ(r,e)}}function MZ(r,e,t=!1,n){let o="";t?o+=WO(r,n):o+=$m(r,n);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?o+=gJ(r,e):o+=xJ(r,e)),o}function LZ(r,e,t){switch(r.length){case 0:return UO();case 1:return KZ(r,e,t);case 2:return rJ(r,e,t);case 3:return YZ(r,e,t);default:return JZ(r,e,t)}}function zZ(r,e,t){switch(r.length){case 0:return UO();case 1:return XZ(r,e,t);case 2:return nJ(r,e,t);case 3:return ZZ(r,e,t);case 4:return QZ(r,e,t);case 5:return eJ(r,e);case 6:return tJ(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function BZ(r){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${r.texture2D}(textureSampler, uv).r;
}
`}function VZ(r){return`
void setOutput(float val) {
${r.output} = vec4(val, 0, 0, 0);
}
`}function GZ(r){return`
void setOutput(vec4 val) {
${r.output} = val;
}
`}function WZ(r){return`${r.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${r.varyingFs} vec2 resultUV;
${r.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${r.defineSpecialNaN}
${r.defineSpecialInf}
${r.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${UZ}
${jZ}
${HZ}
`}var UZ=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,jZ=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,HZ=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,qZ=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function UO(){return`
int getOutputCoords() {
return 0;
}
`}function KZ(r,e,t){let n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return n[0]===1?t?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?t?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:t?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function XZ(r,e,t){return e[0]===1?t?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${e[1]}.0);
}
`:e[1]===1?t?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${e[0]}.0);
}
`:t?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
return resTexRC.x * ${e[1]} + resTexRC.y;
}
`}function YZ(r,e,t){if(t)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec3(b, r, c);
}
`}function ZZ(r,e,t){if(t)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${yc(["r","c","d"],r)}
return ivec3(r, c, d);
}
`;let n=Ks(["r","c","d"],r);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function JZ(r,e,t){if(t)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),a=s,i="",l="b, r, c";for(let u=2;u<r.length-1;u++)a*=r[r.length-u-1],i=`
int b${u} = index / ${a};
index -= b${u} * ${a};
`+i,l=`b${u}, `+l;return`
ivec${r.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${i}
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec${r.length}(${l});
}
`}function QZ(r,e,t){if(t)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${yc(["r","c","d","d2"],r)}
return ivec4(r, c, d, d2);
}
`;let n=Ks(["r","c","d","d2"],r);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function eJ(r,e){let t=Ks(["r","c","d","d2","d3"],r);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function tJ(r,e){let t=Ks(["r","c","d","d2","d3","d4"],r);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function rJ(r,e,t){let n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(b.arraysEqual(r,e))return t?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let o=Math.ceil(r[1]/2);return t?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec2(r, c);
}
`}function nJ(r,e,t){return b.arraysEqual(r,e)?t?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
}
`:r[1]===1?t?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:r[0]===1?t?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(0, index);
}
`:t?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
int r = index / ${r[1]};
int c = index - r * ${r[1]};
return ivec2(r, c);
}
`}function bc(r){return`offset${r}`}function oJ(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),n=Gt();return`
vec4 ${t}() {
return ${n.texture2D}(${e}, halfCR);
}
`}function sJ(r,e){let t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;let a=bc(t);if(e)return`
float ${n}() {
vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], ${a});
return sampleTexture(${t}, uv);
}
`;let[i,l]=r.shapeInfo.texShape;return`
float ${n}() {
vec2 uv = uvFromFlat(${i}, ${l}, ${a});
return sampleTexture(${t}, uv);
}
`}function iJ(r,e){let t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape,s=Gt();if(e)return`
vec4 ${n}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${s.texture2D}(${t}, uv);
}
`;let a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${a[0]}, ${a[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function aJ(r,e){let t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(r.shapeInfo.isUniform)return`
float ${n}(int index) {
${Rm(r)}
}
`;let o=r.shapeInfo.texShape,s=o[0],a=o[1];if(a===1&&s===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;let i=bc(t);return a===1?e?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t}TexShape[0]));
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${s}.0);
return sampleTexture(${t}, uv);
}
`:s===1?e?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t}TexShape[1]), 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${a}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:e?`
float ${n}(int index) {
vec2 uv = uvFromFlat(${t}TexShape[0], ${t}TexShape[1], index + ${i});
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${s}, ${a}, index + ${i});
return sampleTexture(${t}, uv);
}
`}function lJ(r,e){let t=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,a=s[0],i=s[1],l=Gt();if(s!=null&&b.arraysEqual(t,s))return e?`
vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return ${l.texture2D}(${n}, uv);
}
`:`
vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${a}.0);
return ${l.texture2D}(${n}, uv);
}
`;if(e)return`
vec4 ${o}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${n}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${n}, uv);
}
`;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],c=Math.ceil(t[1]/2);return`
vec4 ${o}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
return ${l.texture2D}(${n}, uv);
}
`}function uJ(r,e){let t=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&b.arraysEqual(t,s)){if(e)return`
float ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`;let m=s[0],f=s[1];return`
float ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:a,keptDims:i}=b.squeezeShape(t),l=a;if(l.length<t.length){let m=Fm(r,l),f=["row","col"];return`
${$m(m,e)}
float ${o}(int row, int col) {
return ${o}(${Om(f,i)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${Rm(r)}
}
`;let u=s[0],c=s[1],p=bc(n);return c===1?e?`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
return sampleTexture(${n}, uv);
}
`:u===1?e?`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:e?`
float ${o}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n}Shape[1] + col + ${p};
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${p};
vec2 uv = uvFromFlat(${u}, ${c}, index);
return sampleTexture(${n}, uv);
}
`}function cJ(r,e){let t=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(t[0]===1){let m=t.slice(1),f=[1,2],d=Fm(r,m),h=["b","row","col"];return`
${WO(d,e)}
vec4 ${o}(int b, int row, int col) {
return ${o}(${Om(h,f)});
}
`}let i=Gt();if(e)return`
vec4 ${o}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${i.texture2D}(${n}, uv);
}
`;let l=a[0],u=a[1],c=Math.ceil(t[2]/2),p=c*Math.ceil(t[1]/2);return`
vec4 ${o}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${u}, ${p}, ${c}, b, row, col);
return ${i.texture2D}(${n}, uv);
}
`}function pJ(r,e){let t=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[1]*t[2],a=t[2],{newShape:i,keptDims:l}=b.squeezeShape(t),u=i;if(u.length<t.length){let h=Fm(r,u),g=["row","col","depth"];return`
${$m(h,e)}
float ${o}(int row, int col, int depth) {
return ${o}(${Om(g,l)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${s}, ${a}, 1)));
${Rm(r)}
}
`;let c=r.shapeInfo.texShape,p=c[0],m=c[1],f=r.shapeInfo.flatOffset;if(m===s&&f==null)return e?`
float ${o}(int row, int col, int depth) {
int stride1 = ${n}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(m===a&&f==null)return e?`
float ${o}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let d=bc(n);return e?`
float ${o}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${n}Shape[1] * ${n}Shape[2];
int stride1 = ${n}Shape[2];
int index = row * ${s} + col * ${a} + depth + ${d};
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s} + col * ${a} + depth + ${d};
vec2 uv = uvFromFlat(${p}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function mJ(r,e){let t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=Gt();if(e)return`
vec4 ${n}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${t}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${t}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${t}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${t}TexShape[0]) / 2.0), ceil(float(${t}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${o.texture2D}(${t}, uv);
}
`;let s=r.shapeInfo.logicalShape,a=s.length,i=r.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],u=l[0],c=l[1],p=Math.ceil(s[a-1]/2),m=p*Math.ceil(s[a-2]/2),f="int b, int row, int col",d=`b * ${m} + (row / 2) * ${p} + (col / 2)`;for(let h=2;h<a-1;h++)f=`int b${h}, `+f,m*=s[a-h-1],d=`b${h} * ${m} + `+d;return`
vec4 ${n}(${f}) {
int index = ${d};
int texR = index / ${c};
int texC = index - texR * ${c};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
return ${o.texture2D}(${t}, uv);
}
`}function fJ(r,e){let t=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[3],a=t[2]*s,i=t[1]*a,{newShape:l,keptDims:u}=b.squeezeShape(t);if(l.length<t.length){let y=Fm(r,l),w=["row","col","depth","depth2"];return`
${$m(y,e)}
float ${o}(int row, int col, int depth, int depth2) {
return ${o}(${Om(w,u)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${a}, ${s}, 1)));
${Rm(r)}
}
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1],d=`int stride2 = ${n}Shape[3];`,h=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(f===i&&c==null)return e?`
float ${o}(int row, int col, int depth, int depth2) {
${d}
${h}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${a}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;if(f===s&&c==null)return e?`
float ${o}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n}Shape[1] * ${n}Shape[2], ${n}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;let x=bc(n);return e?`
float ${o}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${d}
${h}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${x});
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${a} +
depth * ${s} + depth2;
vec2 uv = uvFromFlat(${m}, ${f}, index + ${x});
return sampleTexture(${n}, uv);
}
`}function dJ(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[4],s=e[3]*o,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=b.squeezeShape(e);if(l.length<e.length){let h=Fm(r,l),g=["row","col","depth","depth2","depth3"];return`
${$m(h)}
float ${n}(int row, int col, int depth, int depth2, int depth3) {
return ${n}(${Om(g,u)});
}
`}if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${a}, ${s}, ${o})) +
depth3;
${Rm(r)}
}
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${a}, ${s}, ${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;if(f===o&&c==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]},
${e[2]*e[3]}, ${e[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;let d=bc(t);return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${a} + depth * ${s} +
depth2 * ${o} + depth3 + ${d};
vec2 uv = uvFromFlat(${m}, ${f}, index);
return sampleTexture(${t}, uv);
}
`}function hJ(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:o,keptDims:s}=b.squeezeShape(e);if(o.length<e.length){let g=Fm(r,o),x=["row","col","depth","depth2","depth3","depth4"];return`
${$m(g)}
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${n}(${Om(x,s)});
}
`}let a=e[5],i=e[4]*a,l=e[3]*i,u=e[2]*l,c=e[1]*u;if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${u}, ${l}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${a}, 1)));
${Rm(r)}
}
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${i}, ${a})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${t}, uv);
}
`;if(d===a&&p==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]*e[4]},
${e[2]*e[3]*e[4]},
${e[3]*e[4]},
${e[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${t}, uv);
}
`;let h=bc(t);return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${u} + depth * ${l} +
depth2 * ${i} + depth3 * ${a} + depth4 + ${h};
vec2 uv = uvFromFlat(${f}, ${d}, index);
return sampleTexture(${t}, uv);
}
`}function Rm(r){let e=r.name,t=b.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
for (int i = 0; i < ${t}; i++) {
if (i == index) {
return ${e}[i];
}
}
`}function gJ(r,e){let t=r.name,n=t.charAt(0).toUpperCase()+t.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=VO(r.shapeInfo.logicalShape,e.logicalShape),l=Ue(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(y=>`coords.${p[y+u]} = 0;`).join(`
`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((y,w)=>`coords.${p[w+u]}`).join(", ");let f="return outputValue;",h=b.sizeFromShape(r.shapeInfo.logicalShape)===1,x=b.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)f=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(h&&!x)a===1?f=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:f=`
return vec4(outputValue.x);
`;else if(i.length){let y=s-2,w=s-1;i.indexOf(y)>-1&&i.indexOf(w)>-1?f="return vec4(outputValue.x);":i.indexOf(y)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(w)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${o}() {
${l} coords = getOutputCoords();
${c}
vec4 outputValue = get${n}(${m});
${f}
}
`}function xJ(r,e){let t=r.name,n=t.charAt(0).toUpperCase()+t.slice(1),o="get"+n+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&b.arraysEqual(a,s))return`
float ${o}() {
return sampleTexture(${t}, resultUV);
}
`;let u=Ue(l),c=VO(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
`);let d="";return l<2&&i>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
float ${o}() {
${u} coords = getOutputCoords();
${m}
return get${n}(${d});
}
`}function Ue(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Wy(r,e,t){let{newShape:n,keptDims:o}=b.squeezeShape(e),s=e.length,a=r&&s===3&&e[0]===1,i=a?e.slice(1):n,l=!r&&s>1&&!b.arraysEqual(e,t)&&n.length<s||a;return{useSqueezeShape:l,uniformShape:l?i:e,keptDims:o}}function Fm(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function Om(r,e){return e.map(t=>r[t]).join(", ")}function jO(r,e,t,n){let o=t.map((w,_)=>{let C={logicalShape:w.shape,texShape:w.isUniform?null:w.texData.texShape,isUniform:w.isUniform,isPacked:w.isUniform?!1:w.texData.isPacked,flatOffset:null};return w.texData!=null&&w.texData.slice!=null&&w.texData.slice.flatOffset>0&&(C.flatOffset=w.texData.slice.flatOffset),{name:e.variableNames[_],shapeInfo:C}}),s=o.map(w=>w.shapeInfo),a={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},i=GO(o,a,e),l=r.createProgram(i),u=null,c=r.getUniformLocation(l,"NAN",!1);U().getNumber("WEBGL_VERSION")===1&&(u=r.getUniformLocation(l,"INFINITY",!1));let p=!1,m={},f={},d={};for(let w=0;w<e.variableNames.length;w++){let _=e.variableNames[w];m[_]=r.getUniformLocation(l,_,p),m[`offset${_}`]=r.getUniformLocation(l,`offset${_}`,p),e.enableShapeUniforms&&(f[`${_}Shape`]=r.getUniformLocation(l,`${_}Shape`,p),d[`${_}TexShape`]=r.getUniformLocation(l,`${_}TexShape`,p))}let h,g,x;e.enableShapeUniforms&&(h=r.getUniformLocation(l,"outShape",p),x=r.getUniformLocation(l,"outShapeStrides",p),g=r.getUniformLocation(l,"outTexShape",p));let y=[];return e.customUniforms&&e.customUniforms.forEach((w,_)=>{y[_]=r.getUniformLocation(l,w.name,p)}),{program:e,source:i,webGLProgram:l,uniformLocations:m,customUniformLocations:y,inShapeInfos:s,outShapeInfo:a,infLoc:u,nanLoc:c,inShapesLocations:f,inTexShapesLocations:d,outShapeLocation:h,outShapeStridesLocation:x,outTexShapeLocation:g}}function HO(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,n)=>{let o=t.logicalShape,s=e[n],a=s.shape;if(!b.arraysEqual(o,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,l=s.isUniform?null:s.texData.texShape;if(!b.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function qO(r,e,t,n,o){e.program.enableShapeUniforms||(HO(e.inShapeInfos,t),HO([e.outShapeInfo],[n]));let s=n.texData.texture,a=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),U().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,1/0),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((l,u)=>{let c=e.program.variableNames[u],p=e.uniformLocations[c],m=e.uniformLocations[`offset${c}`],f=e.inShapesLocations[`${c}Shape`],d=e.inTexShapesLocations[`${c}TexShape`];if(f){let{uniformShape:h}=Wy(e.program.packedInputs,l.shape,l.texData.texShape);switch(h.length){case 1:r.gl.uniform1iv(f,new Int32Array(h));break;case 2:r.gl.uniform2iv(f,new Int32Array(h));break;case 3:r.gl.uniform3iv(f,new Int32Array(h));break;case 4:r.gl.uniform4iv(f,new Int32Array(h));break;default:break}}if(d&&r.gl.uniform2i(d,l.texData.texShape[0],l.texData.texShape[1]),p!=null){if(l.isUniform){if(b.sizeFromShape(l.shape)<2)r.gl.uniform1f(p,l.uniformValues[0]);else{let h=l.uniformValues;h instanceof Float32Array||(h=new Float32Array(h)),r.gl.uniform1fv(p,h)}return}l.texData.slice!=null&&m!=null&&r.gl.uniform1i(m,l.texData.slice.flatOffset),r.setInputMatrixTexture(l.texData.texture,p,u)}});let i=e.outShapeLocation;if(i)switch(n.shape.length){case 1:r.gl.uniform1iv(i,new Int32Array(n.shape));break;case 2:r.gl.uniform2iv(i,new Int32Array(n.shape));break;case 3:r.gl.uniform3iv(i,new Int32Array(n.shape));break;case 4:r.gl.uniform4iv(i,new Int32Array(n.shape));break;default:break}if(e.outShapeStridesLocation){let l=b.computeStrides(n.shape);switch(n.shape.length){case 2:r.gl.uniform1iv(e.outShapeStridesLocation,new Int32Array(l));break;case 3:r.gl.uniform2iv(e.outShapeStridesLocation,new Int32Array(l));break;case 4:r.gl.uniform3iv(e.outShapeStridesLocation,new Int32Array(l));break;default:break}}e.outTexShapeLocation&&r.gl.uniform2i(e.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),e.program.customUniforms&&o&&e.program.customUniforms.forEach((l,u)=>{let c=e.customUniformLocations[u],p=o[u];if(l.type==="float")r.gl.uniform1fv(c,p);else if(l.type==="vec2")r.gl.uniform2fv(c,p);else if(l.type==="vec3")r.gl.uniform3fv(c,p);else if(l.type==="vec4")r.gl.uniform4fv(c,p);else if(l.type==="int")r.gl.uniform1iv(c,p);else if(l.type==="ivec2")r.gl.uniform2iv(c,p);else if(l.type==="ivec3")r.gl.uniform3iv(c,p);else if(l.type==="ivec4")r.gl.uniform4iv(c,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}),r.executeProgram()}function KO(r,e,t){let n="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let l=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:p}=Wy(r.packedInputs,a.shape,l),m="",f="",d="";if(c.length===1&&r.packedInputs){let C=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];m=`${C[0]>1}_${C[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let C=b.computeStrides(c);d=`${C[0]===l[1]}_${C[C.length-1]===l[1]}`}let h=a.shape.length,g=c.length===2&&b.arraysEqual(a.shape,l),x=b.sizeFromShape(a.shape)===1,y=I.getBroadcastDims(a.shape,t.shape),w=!r.packedInputs&&h===t.shape.length&&b.arraysEqual(l,t.texData.texShape),_=r.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${h}_${w}_${u?p:""}_${c.length}_${x}_${y}_${g}_${m}_${f}_${d}_${_}_${i}`}else{let l=a.isUniform?"uniform":a.texData.texShape;n+=`${a.shape}_${l}_${i}`}});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o+`${U().getNumber("WEBGL_VERSION")}`,s}function jt(r){return U().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var vC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Hl.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gt();this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?yc(["r","c","d"],e):Ks(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}};var CC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Hl.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gt();this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?yc(["r","c","d"],e):Ks(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}};var IC=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Ur.DOWNLOAD;let t=Gt();this.outputShape=e,this.userCode=`
${Gy}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}};var SC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Ur.DOWNLOAD;let t=Gt();this.outputShape=e,this.userCode=`
${Gy}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}};var NC=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gt();this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length);let o="result";t&&(o="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?Dm():Am(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${o}, 0., 0., 0.);
}
`}};var TC=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gt();this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length);let o="",s="result";t&&(s="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let l=a*2+i;o+=`
localCoords = coords;
if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${i};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${l}] = values[0];
} else if (offset == 1) {
result[${l}] = values[1];
} else if (offset == 2) {
result[${l}] = values[2];
} else {
result[${l}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?Dm():Am(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${o}
${n.output} = ${s};
}
`}};var XO={};qe(XO,{bindVertexProgramAttributeStreams:()=>MC,createBufferFromOutputTexture:()=>BC,createFloat16MatrixTexture:()=>RC,createFloat16PackedMatrixTexture:()=>PC,createFloat32MatrixTexture:()=>$C,createIndexBuffer:()=>DC,createPackedMatrixTexture:()=>OC,createUnsignedBytesMatrixTexture:()=>FC,createVertexBuffer:()=>AC,createVertexShader:()=>EC,downloadByteEncodedFloatMatrixFromOutputTexture:()=>GC,downloadFloat32MatrixFromBuffer:()=>VC,downloadMatrixFromPackedOutputTexture:()=>UC,downloadPackedMatrixFromBuffer:()=>WC,getInternalFormatForFloat16MatrixTexture:()=>jy,getInternalFormatForFloat16PackedMatrixTexture:()=>Ky,getInternalFormatForFloat32MatrixTexture:()=>Uy,getInternalFormatForPackedMatrixTexture:()=>qy,getInternalFormatForUnsignedBytesMatrixTexture:()=>Hy,uploadDenseMatrixToTexture:()=>LC,uploadPixelDataToTexture:()=>zC});function EC(r){let e=Gt(),t=`${e.version}
precision highp float;
${e.attribute} vec3 clipSpacePos;
${e.attribute} vec2 uv;
${e.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return nC(r,t)}function AC(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return aC(r,e)}function DC(r){let e=new Uint16Array([0,1,2,2,1,3]);return lC(r,e)}function Wh(r,e,t,n,o,s){cC(e,t);let a=uC(r),i=r.TEXTURE_2D;return ke(r,()=>r.bindTexture(i,a)),ke(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),ke(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),ke(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),ke(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),ke(r,()=>r.texImage2D(i,0,n,e,t,0,o,s,null)),ke(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function Uy(r){return r.internalFormatFloat}function $C(r,e,t,n){let[o,s]=xc(e,t);return Wh(r,o,s,Uy(n),n.textureFormatFloat,r.FLOAT)}function jy(r){return r.internalFormatHalfFloat}function RC(r,e,t,n){let[o,s]=xc(e,t);return Wh(r,o,s,jy(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Hy(r){return r.downloadTextureFormat}function FC(r,e,t,n){let[o,s]=xc(e,t);return Wh(r,o,s,Hy(n),r.RGBA,r.UNSIGNED_BYTE)}function qy(r){return r.internalFormatPackedFloat}function OC(r,e,t,n){let[o,s]=Ai(e,t);return Wh(r,o,s,qy(n),r.RGBA,r.FLOAT)}function Ky(r){return r.internalFormatPackedHalfFloat}function PC(r,e,t,n){let[o,s]=Ai(e,t);return Wh(r,o,s,Ky(n),r.RGBA,n.textureTypeHalfFloat)}function MC(r,e,t){let n=0,o=3*4,s=3*4+2*4;return ke(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Py(r,e,"clipSpacePos",t,3,s,n)&&Py(r,e,"uv",t,2,s,o)}function LC(r,e,t,n,o,s){ke(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;o instanceof Uint8Array?(a=new Uint8Array(t*n*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*n*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(o),ke(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,n,0,r.RGBA,i,a)),ke(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function zC(r,e,t){ke(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?ke(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):ke(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),ke(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function BC(r,e,t,n){let o=r.createBuffer();ke(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));let i=4*4*e*t;return ke(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),ke(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),ke(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function VC(r,e,t){let n=r,o=new Float32Array(t);return n.bindBuffer(n.PIXEL_PACK_BUFFER,e),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function GC(r,e,t,n){let[o,s]=xc(e,t),a=4,i=new Uint8Array(RO(e*t,a));return ke(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function WC(r,e,t,n,o,s,a,i){let l=r,u=new Float32Array(FO(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function UC(r,e,t){let n=new Float32Array(e*t*4);return ke(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,n)),n}var Xy=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=U().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,tC(t,e)):this.gl=Zn(t);let n="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(U().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Tm(this.gl,s),Bn(this.gl,a))this.textureHalfFloatExtension=Tm(this.gl,a);else if(U().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Bn(this.gl,o))this.colorBufferHalfFloatExtension=Tm(this.gl,o);else if(U().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Bn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Bn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=AC(this.gl),this.indexBuffer=DC(this.gl),this.framebuffer=pC(this.gl),this.textureConfig=zh(this.gl,this.textureHalfFloatExtension)}get debug(){return U().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),PC(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),OC(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(My(this.gl,this.framebuffer),this.outputTexture=null),ke(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>GC(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,o,s,a){return WC(this.gl,e,t,n,o,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return VC(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let o=BC(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(U().getBool("WEBGL_FENCE_API_ENABLED")){let o=e,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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ke(t,()=>t.attachShader(o,this.vertexShader)),ke(t,()=>t.attachShader(o,n)),iC(t,o),this.debug&&Bh(t,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=MC(t,this.program,this.vertexBuffer)),o}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ke(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Bh(this.gl,this.program),ke(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?mC(this.gl,e,t):fC(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ke(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),dC(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[o,s]=Ai(t,n);this.setOutputMatrixTextureDriver(e,o,s)}setOutputMatrixWriteRegion(e,t,n,o){this.setOutputMatrixWriteRegionDriver(n,e,o,t)}setOutputPackedMatrixWriteRegion(e,t,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Bh(this.gl,this.program),Em(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ke(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ke(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Tm(this.gl,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await b.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=yJ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&b.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Vh(this.gl,e,this.framebuffer),this.debug&&Em(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Vh(this.gl,this.outputTexture,this.framebuffer),this.debug&&Em(this.gl)):My(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let o=this.gl;Vh(o,e,this.framebuffer),this.debug&&Em(o),this.outputTexture=e,ke(o,()=>o.viewport(0,0,t,n)),ke(o,()=>o.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,o){this.throwIfDisposed(),ke(this.gl,()=>this.gl.scissor(e,t,n,o))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function yJ(r){let e=0;for(;e<r.length&&r[e]();++e);return 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HC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let n=Jt("rc",t),o=Ue(t),s=wJ(t,e,n),a=_J(t,e[e.length-1],e[e.length-2],n),i=kJ(e,n);this.userCode=`
void main() {
${o} rc = getOutputCoords();
if(${s}) {
setOutput(vec4(0));
} else {
${a}
setOutput(vec4(${i}));
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}
`}}};function bJ(r,e){let t=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function wJ(r,e,t){if(r===1)return`rc > ${e[0]}`;let n="";for(let o=r-2;o<r;o++)n+=`${t[o]} >= ${e[o]}`,o<r-1&&(n+="||");return n}function _J(r,e,t,n){if(r===1)return"";let o=n.slice(-2);return`
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int cp1 = c + 1;
bool cEdge = cp1 >= ${e};
bool rEdge = rp1 >= ${t};
`}function kJ(r,e){let t=r.length,n=bJ(t,e);return t===1?`getA(rc),
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cEdge ? 0. : getA(${n[1]}),
rEdge ? 0. : getA(${n[2]}),
rEdge || cEdge ? 0. : getA(${n[3]})`}var Uh=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2==1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=`
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${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
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vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${o}] =
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${o>0?"}":""}
`}this.userCode=`
${vJ(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?Dm():Am(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function vJ(r,e){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${e?BO(["r","c","d"],"inputShape"):Ks(["r","c","d"],r)}
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}
`}var qC=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let o=PP(t,n),s=MP(e,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=OP(e,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return o===Fr.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===Fr.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===Fr.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===Fr.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===Fr.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,o){if(this.freeTextures==null)return;let s=PP(n,o),a=MP(t,s,o);a in this.freeTextures||(this.freeTextures[a]=[]);let i=OP(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),l=U().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function CJ(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;throw new Error(`Unknown internal format ${e}`)}function OP(r,e,t,n,o){let s=IJ(e,n),a;if(o){let[l,u]=Ai(r[0],r[1]);a=l*u}else{let[l,u]=xc(r[0],r[1]);a=l*u}let i=CJ(t,s);return a*i}function IJ(r,e){switch(r){case Fr.PACKED_2X2_FLOAT32:return qy(e);case Fr.PACKED_2X2_FLOAT16:return Ky(e);case Fr.UNPACKED_FLOAT32:return Uy(e);case Fr.UNPACKED_FLOAT16:return jy(e);case Fr.PACKED_4X1_UNSIGNED_BYTE:return Hy(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function SJ(r){return U().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Fr.PACKED_2X2_FLOAT32:Fr.UNPACKED_FLOAT32:r?Fr.PACKED_2X2_FLOAT16:Fr.UNPACKED_FLOAT16}function PP(r,e){if(r===Ur.UPLOAD)return Fr.PACKED_2X2_FLOAT32;if(r===Ur.RENDER||r==null)return SJ(e);if(r===Ur.DOWNLOAD||r===Ur.PIXELS)return Fr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function MP(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var En=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length),this.userCode=`
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${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
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`,VP=Nr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
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vec4 result;
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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;
`,jP=`
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;
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`,HP=`
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;
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vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}};var XC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=Jt("rc",t),o=Ue(t),s=FP(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${i}));
}
`}};var NJ=Gr.whereImpl,TJ=1e-7,EJ=1e-4,Qy={};function AJ(r){return r in Qy||(Qy[r]={}),Qy[r]}var DJ=U().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),$J=600;function RJ(){return U().global.screen==null?1024:U().global.screen.height*U().global.screen.width*window.devicePixelRatio*$J/1024/1024}var _c=class extends Js{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!U().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Zn(U().getNumber("WEBGL_VERSION"));this.binaryCache=AJ(U().getNumber("WEBGL_VERSION")),this.gpgpu=new Xy(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new qC(this.gpgpu),this.numMBBeforeWarning=RJ(),this.texData=new pl(this,Es())}nextDataId(){return _c.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((U().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||U().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:t,dtype:n,values:e,usage:Ur.UPLOAD,refCount:1}),o}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,o,s){if(U().getBool("DEBUG")&&this.checkNumericalProblems(t),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:o,values:t,usage:Ur.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:o,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new Xs(i,jh):m=new En(i,jh);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(e);if(o==="string")return n;let u=this.activeTimers!=null,c;u&&(c=b.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=I.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=b.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:n,shape:o,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Xs(o,jh):d=new En(o,jh);let h=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(e);if(!U().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&U().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&U().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...Lh(o))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=I.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=b.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let d=this.gpgpu.gl;ke(d,()=>d.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Es().removeDataId(e,this),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(o=>b.decodeString(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ie(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!rC(n))throw U().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:o}=this.texData.get(e),s=b.sizeFromShape(t);if(U().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...Lh(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=U().getBool("WEBGL_PACK")&&o===!0,i=a?Gh(t):t,l=a?new SC(i):new IC(i),u=this.runWebGLProgram(l,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}timerAvailable(){return U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=b.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=b.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,o&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=b.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.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 U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:b.now(),endMs:null}}endTimer(e){return U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=b.now(),e)}async getQueryTime(e){if(U().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:o,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(t,o,s,a)));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=DJ){return U().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&b.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){I.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return NJ(e.shape,t)}packedUnaryOp(e,t,n){let o=new Xs(e.shape,t),s=this.compileAndRun(o,[e],n);return Es().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let o=Zy(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,o)}if(U().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,KC,e.dtype);let t=new En(e.shape,KC),n=this.compileAndRun(t,[e]);return Es().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let o;if(t==="string"&&n!=null&&n.length>0&&b.isString(n[0])){let s=n.map(a=>b.encodeString(a));o=this.write(s,e,t)}else o=this.write(n,e,t);return this.texData.get(o).usage=null,{dataId:o,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:o}=this.makeTensorInfo(e,t,n);return Es().makeTensorFromDataId(o,e,t,this)}unpackTensor(e){let t=new XC(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new HC(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Qa(e.shape),...el(e.shape)],o={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Qa(t),...el(t)],a=new Uh(s,n),i=!0,l=[n],u=this.runWebGLProgram(a,[o],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:o,dtype:s}=t,a=Gh(o),i,l=Lh(a);n?i=new CC(a):i=new vC(a);let u=!0,c=[l],p=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,c,u);return{dtype:s,shape:o,dataId:p.dataId}}runWebGLProgram(e,t,n,o,s=!1){let a=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Hl.DENSE){let g=Lh(e.outputShape);i.texShape=g.map(x=>x*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),b.sizeFromShape(a.shape)===0)return i.values=b.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let x=this.texData.get(g.dataId);if(x.texture==null){if(!e.packedInputs&&b.sizeFromShape(g.shape)<=U().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:x.values};e.packedInputs&&(x.isPacked=!0,x.shape=g.shape)}else if(!!x.isPacked!=!!e.packedInputs)g=x.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),x=this.texData.get(g.dataId);else if(x.isPacked&&!ql(x.shape,g.shape)){let y=g,w=g.shape;g.shape=x.shape,g=this.packedReshape(g,w),l.push(g),x=this.texData.get(g.dataId),y.shape=w}return this.uploadToGPU(g.dataId),{shape:g.shape,texData:x,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=KO(e,u,c),m=this.getAndSaveBinary(p,()=>jO(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;f&&(d=this.startTimer()),qO(this.gpgpu,m,u,c,o),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)}));let h=U().get("WEBGL_FLUSH_THRESHOLD");if(h>0){let g=b.now();g-this.lastGlFlushTime>h&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!U().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let g=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),g}return a}compileAndRun(e,t,n,o,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,o,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(U().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),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=V(()=>{if(!U().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=U().getBool("DEBUG");U().set("DEBUG",!1);let t=this.abs(pe(1e-8)).dataSync()[0];if(U().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?TJ:EJ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:o,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=b.now());let p=t.texShape;if(p==null&&(p=hC(n,l),t.texShape=p),s!=null){let m=Gh(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;l?([d,h]=Ai(p[0],p[1]),f=new TC(m,g)):f=new NC(m,g);let x=this.makeTensorInfo([h,d],o);g?this.texData.get(x.dataId).usage=Ur.PIXELS:this.texData.get(x.dataId).usage=Ur.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),d,h,s);let y=[[h,d]],w=!0,_=this.runWebGLProgram(f,[x],o,y,w),C=this.texData.get(_.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(x),this.texData.delete(_.dataId),t.values=null,u&&(this.uploadWaitMs+=b.now()-c)}else{let m=this.acquireTexture(p,i,o,l);t.texture=m}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:o}=n;return this.releaseGPUData(e),t!=null&&(n.values=FJ(t,o)),n.values}acquireTexture(e,t,n,o){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,o)}computeBytes(e,t){return e[0]*e[1]*b.bytesPerElement(t)}};_c.nextDataId=0;function FJ(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;n<t.length;++n)t[n]=Math.round(r[n]);return t}else throw new Error(`Unknown dtype ${e}`)}var KP="3.10.0";function XP(){U().set("WEBGL_FORCE_F16_TEXTURES",!0)}xu.isBrowser()&&Op("webgl",()=>new _c,2);var LTt={forceHalfFloat:XP};var eb=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`;var No=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=I.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=jt(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}};var Kl=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;var Ys=class{constructor(e,t,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=I.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length;this.enableShapeUniforms=jt(s);let a="";if(o)if(s===0||b.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${Ue(s)} coords = getOutputCoords();
`,s===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let l=Jt("coords",s);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${l[s-2]} + 1) >= outShape[${s} - 2];
bool nextColOutOfBounds =
(${l[s-1]} + 1) >= outShape[${s} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${l[s-2]} + 1) >= ${this.outputShape[s-2]};
bool nextColOutOfBounds =
(${l[s-1]} + 1) >= ${this.outputShape[s-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function Qt(r){let{inputs:e,backend:t}=r,{x:n}=e;return t.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var YP={kernelName:ro,backendName:"webgl",kernelFunc:Qt};function An(r){let{inputs:e,backend:t}=r,{real:n,imag:o}=e,s=t.makeTensorInfo(n.shape,"complex64"),a=t.texData.get(s.dataId),i=Qt({inputs:{x:n},backend:t}),l=Qt({inputs:{x:o},backend:t});return a.complexTensorInfos={real:i,imag:l},s}var ZP={kernelName:qc,backendName:"webgl",kernelFunc:An};var YC="return (a < 0.) ? b * a : a;",ZC=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function OJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{alpha:s}=n,a=t.makeTensorInfo([],"float32",b.createScalarValue(s,"float32")),i=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ys(ZC,o.shape,a.shape):new No(YC,o.shape,a.shape),l=t.runWebGLProgram(i,[o,a],"float32");return t.disposeIntermediateTensorInfo(a),l}var JP={kernelName:Yo,backendName:"webgl",kernelFunc:OJ};var JC="return (a < 0.) ? b * a : a;",QC=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function PJ(r){let{inputs:e,backend:t}=r,{x:n,alpha:o}=e,s=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ys(QC,n.shape,o.shape):new No(JC,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],"float32")}var QP={kernelName:us,backendName:"webgl",kernelFunc:PJ};var tb="if (isnan(x)) return x;",eM=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,tM=`
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 ve({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:n}){return({inputs:o,backend:s})=>{let{x:a}=o,i=s,l=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=U().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Xs(a.shape,e):c=new En(a.shape,r),i.runWebGLProgram(c,[a],l)}}function at({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(n&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[_,C]=w,A={dataId:_.dataId,dtype:_.dtype,shape:l.shape},D={dataId:C.dataId,dtype:C.dtype,shape:u.shape},R=new No(r,l.shape,u.shape);return c.runWebGLProgram(R,[A,D],hr(_.dtype,C.dtype))}),y=An({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),y}let p=s||hr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&o!=null){let d=c.texData.get(l.dataId).values,h=c.texData.get(u.dataId).values,g=l.dtype==="string"?I.fromUint8ToStringArray(d):d,x=l.dtype==="string"?I.fromUint8ToStringArray(h):h,[y,w]=o(l.shape,u.shape,g,x,p),_=c.makeTensorInfo(w,p),C=c.texData.get(_.dataId);return C.values=y,_}let m=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Ys(e,l.shape,u.shape,t):f=new No(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function Xl(r,e=!1){if(r==="linear")return e?WP:LP;if(r==="relu")return e?jP:BP;if(r==="elu")return e?UP:zP;if(r==="relu6")return e?HP:VP;if(r==="prelu")return e?QC:JC;if(r==="leakyrelu")return e?ZC:YC;if(r==="sigmoid")return e?qP:GP;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Hh=class{constructor(e,t,n,o=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=jt(this.outputShape.length);let c=o?e[1]:e[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(l?g=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:u?g=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:g=`vec4 activation(vec4 x) {
${i}
}`,x="result = activation(result);");let y=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let w="rc.x",_="rc.x";e[0]<t[0]?w=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${g}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${p}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${p}; i++) {
int batchA = ${w};
int batchB = ${_};
vec4 a = getMatrixA(batchA, ${m});
vec4 b = getMatrixB(batchB, ${f});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${d[0]} * ${h[0]});
result += (${d[1]} * ${h[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${x}
setOutput(result);
}
`}};var eI={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},rb=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=I.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}};var rM="return a * b;";function qh(r){let{inputs:e,backend:t}=r,{a:n,b:o}=e,s=I.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),u=new rb(eI.REAL,n.shape,o.shape),c=new rb(eI.IMAG,n.shape,o.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:n.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:o.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:o.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=An({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([n,o])){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),[u,c]=hP(n.shape,o.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Ys(rM,n.shape,o.shape):a=new No(rM,n.shape,o.shape),t.runWebGLProgram(a,[n,o],s)}var nM={kernelName:ss,backendName:"webgl",kernelFunc:qh};function oM(r,e,t){let n=[Qa(r.shape),...el(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Qa(e),...el(e)],a=new Uh(s,n),i=!0,l=[n],u=t.runWebGLProgram(a,[o],r.dtype,l,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}function ue(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{shape:s}=n,a=t,i=b.sizeFromShape(o.shape),l=b.inferFromImplicitShape(s,i),u=b.sizeFromShape(l);b.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${o.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(o.dataId);return c.isPacked&&!ql(o.shape,l)&&!(c.texture!==null&&ql(c.shape,l))?oM(o,l,a):(a.incRef(o.dataId),{dataId:o.dataId,shape:l,dtype:o.dtype})}var sM={kernelName:ui,backendName:"webgl",kernelFunc:ue};var nb=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i=Math.floor(n/4)*4,l=n%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${b.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${s}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${u}
}
int inIdx = inOffset + ${i};
if (${l===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${u}
} else if (${l===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${u}
} else if (${l===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${u}
}
setOutput(sumValue);
}
`}};var tI=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${l}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${l}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,f="vec4";t==="all"?(i="1.0",m=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,f="bvec4"):t==="any"&&(i="0.0",m=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,f="bvec4");let d="";s%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${m}
}
int inIdx = inOffset + ${c};
if (${p===1}) {
${f} values = ${f}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${m}
} else if (${p===2}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${m}
} else if (${p===3}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${m}
}
setOutput(${u});
}
`}};function MJ(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],n=I.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:n,outSize:Math.ceil(t/n)})}return e}function Vn(r,e,t,n){let o=MJ(r.shape),s=r;for(let a=0;a<o.length;a++){let{inSize:i,windowSize:l,outSize:u}=o[a],c,p;t==="mean"?c=a===0?new nb({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new nb({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new tI({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=n.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var rI=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let o=Ue(this.rank),s=LJ(t);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function LJ(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(e);for(let o=0;o<r.length;o++)n[r[o]]=t[o];return n.join()}var nI=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Ue(this.rank),s=jC("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${l}) {
result[1] = ${u};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${u};
if(${l}) {
result[3] = ${u};
}
}
setOutput(result);
}
`}};function Yl(r,e,t){let n=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nI(r.shape,e):new rI(r.shape,e);return t.runWebGLProgram(n,[r],r.dtype)}function iM(r,e,t,n){let o=e,s=r.shape.length,a=b.parseAxisParam(o,r.shape),i=a,l=I.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=Yl(r,l,n),i=I.getInnerMostAxes(i.length,s)),I.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=I.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=I.expandShapeToKeepDim(p,a));let d=b.sizeFromShape(m),g=b.sizeFromShape(r.shape)/d,x=ue({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),y=hu(r.dtype),w=Vn(x,y,"sum",n),_=ue({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),u&&n.disposeIntermediateTensorInfo(c),_}function kc(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;return iM(o,s,a,t)}var aM={kernelName:bs,backendName:"webgl",kernelFunc:kc};function Pt(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{perm:s}=n,a=t,i=o.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=o.shape[s[c]];let u;if(a.shouldExecuteOnCPU([o])){let p=a.texData.get(o.dataId).values,m=wc(p,o.shape,o.dtype,s,l);u=a.makeTensorInfo(l,o.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=Yl(o,s,a);return u}var lM={kernelName:Is,backendName:"webgl",kernelFunc:Pt};var oI=1e3;function vc({a:r,b:e,transposeA:t,transposeB:n,backend:o,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=n?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=n?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=b.sizeFromShape(h),y=b.sizeFromShape(g),w=x===y||x===1||y===1;b.assert(u>=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let C=(x>y?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);b.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${n} must match.`);let A=t?[x,p,f]:[x,f,p],D=n?[y,d,m]:[y,m,d],R=ue({inputs:{x:r},backend:o,attrs:{shape:A}}),P=ue({inputs:{x:e},backend:o,attrs:{shape:D}}),L=[R,P],G=Math.max(x,y),W=t?R.shape[1]:R.shape[2],j=s!=null,H=a!=null,q=l==="leakyrelu",X=l!=null?Xl(l,!0):null,re=j||H||q||X!=null,J;if((f===1||d===1)&&W>oI&&re===!1){let se=R,ne=P;t&&(se=Pt({inputs:{x:R},backend:o,attrs:{perm:[0,2,1]}}),L.push(se)),n&&(ne=Pt({inputs:{x:P},backend:o,attrs:{perm:[0,2,1]}}),L.push(ne));let fe=d!==1,ae=d===1,ge=se;fe&&(ge=ue({inputs:{x:se},backend:o,attrs:{shape:[G,W,1]}}),L.push(ge));let de=d===1?2:1,ye=ne;ae&&(ye=ue({inputs:{x:ne},backend:o,attrs:{shape:[G,1,W]}}),L.push(ye));let _e=qh({inputs:{a:ge,b:ye},backend:o});J=kc({inputs:{x:_e},backend:o,attrs:{axis:de,keepDims:!0}}),L.push(_e)}else{let se=hr(r.dtype,e.dtype),ne=new Hh(A,D,[G,f,d],t,n,j,X,H,q),fe=[R,P];if(s!=null&&fe.push(s),H&&fe.push(a),q){let ae=o.makeTensorInfo([],"float32",b.createScalarValue(i,"float32"));fe.push(ae),L.push(ae)}J=o.runWebGLProgram(ne,fe,se)}let oe=ue({inputs:{x:J},backend:o,attrs:{shape:C}});L.push(J);for(let se of L)o.disposeIntermediateTensorInfo(se);return oe}function zJ(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=n;return vc({a:o,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var uM={kernelName:gi,backendName:"webgl",kernelFunc:zJ};var cM="return abs(x);";function BJ(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=t.texData.get(n.dataId),a=Zy(s.values);return t.makeTensorInfo(n.shape,n.dtype,a)}let o;return U().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Xs(n.shape,cM):o=new En(n.shape,cM),t.runWebGLProgram(o,[n],n.dtype)}var pM={kernelName:ti,backendName:"webgl",kernelFunc:BJ};var VJ=Nr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,GJ=ve({opSnippet:VJ}),mM={kernelName:Mi,backendName:"webgl",kernelFunc:GJ};var WJ=Nr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,UJ=ve({opSnippet:WJ}),fM={kernelName:Li,backendName:"webgl",kernelFunc:UJ};var dM="return a + b;",jJ=at({opSnippet:dM,packedOpSnippet:dM,supportsComplex:!0,cpuKernelImpl:YO}),hM={kernelName:jn,backendName:"webgl",kernelFunc:jJ};var sI=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${o};
setOutput(result);
}
`}};var iI=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${o};
setOutput(result);
}
`}};function ob(r){let{inputs:e,backend:t}=r,n=e;if(n.length===1)return Qt({inputs:{x:n[0]},backend:t});if(n.length>U().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(n.length/2),u=ob({inputs:n.slice(0,l),backend:t}),c=ob({inputs:n.slice(l),backend:t});return ob({inputs:[u,c],backend:t})}let o=n.map(l=>l.dtype).reduce((l,u)=>hr(l,u)),s=n.map(l=>l.shape),i=U().getBool("WEBGL_PACK")?new iI(n[0].shape,s):new sI(n[0].shape,s);return t.runWebGLProgram(i,n,o)}var gM={kernelName:$o,backendName:"webgl",kernelFunc:ob};function HJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=b.parseAxisParam(s,o.shape),u=l,c=I.getAxesPermutation(u,i),p=o;c!=null&&(p=Pt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=I.getInnerMostAxes(u.length,i)),I.assertAxesAreInnerMostDims("all",u,i);let[m,f]=I.computeOutAndReduceShapes(p.shape,u),d=b.sizeFromShape(f),h=ue({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Vn(h,h.dtype,"all",t),x;if(a){let y=I.expandShapeToKeepDim(m,l);x=ue({inputs:{x:g},backend:t,attrs:{shape:y}})}else x=ue({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var xM={kernelName:zi,backendName:"webgl",kernelFunc:HJ};function qJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=b.parseAxisParam(s,o.shape),u=l,c=I.getAxesPermutation(u,i),p=o;c!=null&&(p=Pt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=I.getInnerMostAxes(u.length,i)),I.assertAxesAreInnerMostDims("any",u,i);let[m,f]=I.computeOutAndReduceShapes(p.shape,u),d=b.sizeFromShape(f),h=ue({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Vn(h,h.dtype,"any",t),x;if(a){let y=I.expandShapeToKeepDim(m,l);x=ue({inputs:{x:g},backend:t,attrs:{shape:y}})}else x=ue({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var yM={kernelName:Bi,backendName:"webgl",kernelFunc:qJ};var aI=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${o};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${o}; i++) {
int inIdx = ${l};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}};var lI=class{constructor(e,t,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,b.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),o||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Ue(l),c=Jt("coords",l),p,m;if(a===1){m=l+1;let R=Ue(m);p=`
${R} sourceLocR = ${R}(${c.join()}, 0);
++${c[l-1]};
${R} sourceLocG = ${R}(${c.join()}, 0);
++${c[l-2]};
${R} sourceLocA = ${R}(${c.join()}, 0);
--${c[l-1]};
${R} sourceLocB = ${R}(${c.join()}, 0);
--${c[l-2]};`}else m=l,p=`
${u} sourceLocR = coords;
++${c[l-1]};
${u} sourceLocG = coords;
++${c[l-2]};
${u} sourceLocA = coords;
--${c[l-1]};
${u} sourceLocB = coords;
--${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(R=>"int "+R),g=Jt("sourceLocR",m-1).concat("inIdx.r"),x=Jt("sourceLocG",m-1).concat("inIdx.g"),y=Jt("sourceLocB",m-1).concat("inIdx.b"),w=Jt("sourceLocA",m-1).concat("inIdx.a"),_=n==="max"?"greaterThan":"lessThan",C=o?"":`
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${x.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${w.join()})));`,A=`vec4(
getAChannel(${g.join()}),
hasNextCol ? getAChannel(${x.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,D=o?"":`
float getBestIndicesAChannel(${h.join()}) {
return getChannel(getBestIndicesA(${f.join()}),
vec2(${f.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${h.join()}) {
return getChannel(getA(${f.join()}),
vec2(${f.slice(-2).join()}));
}
${D}
void main() {
${u} coords = getOutputCoords();
bool hasNextCol = ${c[l-1]} < ${i[l-1]-1};
bool hasNextRow = ${c[l-2]} < ${i[l-2]-1};
${p}
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
sourceLocB${d}, sourceLocA${d}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${A};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${C}
vec4 candidate = ${A};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${_}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function bM(r,e,t,n=null){let o=e.shape[0],s=e.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let a=I.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:o,outSize:Math.ceil(s/a)},l=new aI(i,t,n==null),u=[e];n!=null&&u.push(n);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=bM(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function wM(r,e,t,n=null){let o=n!=null?n.shape:e.shape,s=o[o.length-1],a=I.computeOptimalWindowSize(s),i=new lI(o,a,t,n==null),l=n==null?[e]:[e,n],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=wM(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function sb(r,e,t,n){let o=[t];if(I.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,e.shape.length),!U().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,l=e;i&&(l=r.unpackTensor(e),s.push(l));let[u,c]=I.computeOutAndReduceShapes(l.shape,o),p=b.sizeFromShape(c),m=ue({inputs:{x:l},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=bM(r,m,n);s.push(f);let d=ue({inputs:{x:f},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return wM(r,e,n)}function KJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=b.parseAxisParam(s,o.shape),i=I.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Pt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=I.getInnerMostAxes(a.length,l.shape.length)),I.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=sb(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var _M={kernelName:Ro,backendName:"webgl",kernelFunc:KJ};function XJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=b.parseAxisParam(s,o.shape),i=I.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Pt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=I.getInnerMostAxes(a.length,l.shape.length)),I.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=sb(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var kM={kernelName:ml,backendName:"webgl",kernelFunc:XJ};var YJ=Nr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,ZJ=ve({opSnippet:YJ}),vM={kernelName:Vi,backendName:"webgl",kernelFunc:ZJ};var JJ=Nr+"return log(x + sqrt(x * x + 1.0));",QJ=ve({opSnippet:JJ}),CM={kernelName:Gi,backendName:"webgl",kernelFunc:QJ};var eQ=Nr+`
return atan(x);
`,tQ=ve({opSnippet:eQ}),IM={kernelName:Wi,backendName:"webgl",kernelFunc:tQ};var rQ=eM+`
return atan(a, b);
`,nQ=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+tM+`
return result;
`,oQ=at({opSnippet:rQ,packedOpSnippet:nQ}),SM={kernelName:ji,backendName:"webgl",kernelFunc:oQ};var sQ=Nr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,iQ=ve({opSnippet:sQ}),NM={kernelName:Ui,backendName:"webgl",kernelFunc:iQ};var Di=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(h||(y="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${f}, ${d});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
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 ${R} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let w="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let C=Math.floor(a/4)*4,A=a%4,D=`
if (${h}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${w}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${f}, ${d});
const float initializationValue = ${y};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${C}; 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)
);
${D}
}
int xC = xCCorner + ${C};
if (${A===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${D}
} else if (${A===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${D}
} else if (${A===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${D}
}
}
setOutput(${_});
}
`}},Cc=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let w=t==="avg",_="0.0";if(w||(_="-1.0 / 1e-20"),n){let L=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${l}, ${u});
const ivec3 pads = ivec3(${g}, ${x}, ${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 < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${m}) {
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 ${L} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
wR * ${h} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let C="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / count");let D=Math.floor(a/4)*4,R=a%4,P=`
if (${w}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${C}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${l}, ${u});
const ivec3 pads = ivec3(${g}, ${x}, ${y});
const float initializationValue = ${_};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${_});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${D}; wC += 4) {
int xC = xCCorner + wC * ${m};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
getValue(batch, xD, xR, xC + 3 * ${m}, ch)
);
${P}
}
int xC = xCCorner + ${D};
if (${R===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${P}
} else if (${R===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
initializationValue,
initializationValue
);
${P}
} else if (${R===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
initializationValue
);
${P}
}
}
setOutput(${A});
}
}
`}};function aQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;qs(o,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;b.assert(I.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=I.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&b.arraysEqual(c.inShape,c.outShape))return Qt({inputs:{x:o},backend:t});let p=new Di(c,"avg",!1);return t.runWebGLProgram(p,[o],"float32")}var TM={kernelName:Fo,backendName:"webgl",kernelFunc:aQ};function lQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=n,c=[1,1,1],p=I.computePool3DInfo(o.shape,s,a,c,i,l,u),m=new Cc(p,"avg",!1);return t.runWebGLProgram(m,[o],"float32")}var EM={kernelName:fl,backendName:"webgl",kernelFunc:lQ};var uI=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${p});
const float avgMultiplier = float(${m});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${l};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},cI=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*n*o);this.userCode=`
const ivec3 pads = ivec3(${d}, ${h}, ${g});
const float avgMultiplier = float(${x});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${p};
wD += ${l}) {
float dyD = float(dyDCorner + wD) / ${s}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${m};
wR += ${u}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${f};
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 uQ(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=I.computePool3DInfo(a.shape,i,l,p,u,c),f=new cI(m);return t.runWebGLProgram(f,[o],a.dtype)}var AM={kernelName:Uc,backendName:"webgl",kernelFunc:uQ};function cQ(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;qs([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=I.computePool2DInfo(a.shape,i,l,1,u),p=new uI(c);return t.runWebGLProgram(p,[o],a.dtype)}var DM={kernelName:Wc,backendName:"webgl",kernelFunc:cQ};function pQ(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s}=e,{transposeA:a,transposeB:i}=n;return vc({a:o,b:s,transposeA:a,transposeB:i,backend:t})}var $M={kernelName:Oo,backendName:"webgl",kernelFunc:pQ};var pI=class{constructor(e,t,n,o,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],I.assertAndGetBroadcastShape(e,t),I.assertAndGetBroadcastShape(e,n);let i="0.0";o!=null&&(I.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(I.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${l};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}};var mI=class{constructor(e,t,n,o,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],I.assertAndGetBroadcastShape(e,t),I.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";o!=null&&(I.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(I.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${l};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}};var mQ=({inputs:r,backend:e,attrs:t})=>{let{x:n,mean:o,variance:s,offset:a,scale:i}=r;b.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),b.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),b.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[n,o,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=U().getBool("WEBGL_PACK_NORMALIZATION")?new mI(n.shape,o.shape,s.shape,c,p,l):new pI(n.shape,o.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},RM={kernelName:Ko,backendName:"webgl",kernelFunc:mQ};var fI=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Ue(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=fQ(this.rank),o,s=e.map((a,i)=>`sourceLoc.${dI[i]} = start[${i}] + coords.${dI[i]};`);o=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
void main() {
${o}
setOutput(getSource(${n}));
}
`}},dI=["x","y","z","w","u","v"];function fQ(r){if(r===1)return"sourceLoc";if(r<=6)return dI.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var hI=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=Ue(this.rank),n=Jt("coords",this.rank),o=Jt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,a=`getChannel(getSource(${o.join()}), ${s})`,i=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${o[this.rank-1]};
result.y = ${a};
--${o[this.rank-1]};
}
`,l=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${o[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${o[this.rank-1]};
result.w = ${a};
}
}
`,u=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${u}
vec4 result = vec4(0.);
${i}
${l}
setOutput(result);
}
`}};function dQ(r,e,t,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(t,r.dtype),a=n.texData.get(s.dataId);Object.assign(a,o),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=pr.computeFlatOffset(e,b.computeStrides(r.shape));o.slice&&(i+=o.slice.flatOffset),a.slice={flatOffset:i,origDataId:o.slice&&o.slice.origDataId||r.dataId};let l=n.dataRefCount.get(a.slice.origDataId)||1;return n.dataRefCount.set(a.slice.origDataId,l+1),s}function Zs(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,size:a}=n,[i,l]=pr.parseSliceParams(o,s,a);if(pr.assertParamsValid(o,i,l),b.sizeFromShape(l)===0)return t.makeTensorInfo(l,o.dtype,[]);if(t.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=t.texData.get(o.dataId),m=kP(p.values,i,l,o.shape,o.dtype);return t.makeTensorInfo(l,o.dtype,m)}let{isPacked:u}=t.texData.get(o.dataId),c=pr.isSliceContinous(o.shape,i,l);if(u||!c){let p=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hI(l):new fI(l),m=[i];return t.runWebGLProgram(p,[o],o.dtype,m)}return t.uploadToGPU(o.dataId),dQ(o,i,l,t)}var FM={kernelName:pi,backendName:"webgl",kernelFunc:Zs};var hQ=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;b.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((y,w)=>y*w),l=I.getReshaped(o.shape,s,i),u=I.getPermuted(l.length,s.length),c=I.getReshapedPermuted(o.shape,s,i),p=I.getSliceBeginCoords(a,s.length),m=I.getSliceSize(c,a,s.length),f=[],d=ue({inputs:{x:o},backend:t,attrs:{shape:l}}),h=Pt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=ue({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Zs({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(y=>t.disposeIntermediateTensorInfo(y)),x},OM={kernelName:ri,backendName:"webgl",kernelFunc:hQ};function gQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.readSync(o.dataId),l=t.readSync(s.dataId),u=Yy(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var PM={kernelName:jc,backendName:"webgl",kernelFunc:gQ};function xQ(r){let{inputs:e,backend:t}=r,{s0:n,s1:o}=e,s=t.readSync(n.dataId),a=t.readSync(o.dataId),i=I.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var MM={kernelName:Hc,backendName:"webgl",kernelFunc:xQ};var yQ="return float(a != b);",gI=at({opSnippet:yQ,cpuKernelImpl:xP,dtype:"bool"}),LM={kernelName:la,backendName:"webgl",kernelFunc:gI};function tl(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return Qt({inputs:{x:o.complexTensorInfos.real},backend:t})}var zM={kernelName:mp,backendName:"webgl",kernelFunc:tl};var bQ="return float(int(x));";function BM(r,e){let t=new En(r.shape,bQ),n=e.runWebGLProgram(t,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function xI(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return Qt({inputs:{x:o},backend:t});let a=yt(o.shape),i=xI({inputs:{x:o},backend:t,attrs:{dtype:"float32"}}),l=An({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(o.dtype==="complex64"){let a=tl({inputs:{input:o},backend:t}),i=xI({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!b.hasEncodingLoss(o.dtype,s)){let a=Qt({inputs:{x:o},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return BM(o,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",b.getTypedArrayFromDType("bool",1)),l=gI({inputs:{a:o,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var VM={kernelName:eo,backendName:"webgl",kernelFunc:xI};var GM="return ceil(x);",wQ=ve({opSnippet:GM,packedOpSnippet:GM,cpuKernelImpl:JO}),WM={kernelName:Po,backendName:"webgl",kernelFunc:wQ};var yI=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}};var bI=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function _Q(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{clipValueMin:s,clipValueMax:a}=n,i;U().getBool("WEBGL_PACK_CLIP")?i=new bI(o.shape):i=new yI(o.shape);let l=[[s],[a]];return t.runWebGLProgram(i,[o],o.dtype,l)}var UM={kernelName:to,backendName:"webgl",kernelFunc:_Q};var wI=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function jM(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function kQ(r){let{inputs:e,backend:t}=r,{x:n}=e,o=t.texData.get(n.dataId),s=new wI(n.shape),a=[jM(n,o.complexTensorInfos.real),jM(n,o.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var HM={kernelName:dl,backendName:"webgl",kernelFunc:kQ};var _I=class{constructor(e){this.outputShape=[],this.outputShape=I.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let o=t.length,s=t[t.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}};var kI=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=I.computeOutShape(e,t);let n=this.outputShape,o=n.length,s=Ue(o),a=Jt("coords",o),i=["x","y","z","w","u","v"].slice(0,o);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
return getChannel(
getT0(${p}), vec2(${c.join()}));
}`;for(let h=1;h<l.length;h++){let g=l[h-1];m+=`
if (${u} < ${l[h]} && ${u} >= ${l[h-1]}) {
return getChannel(
getT${h}(${ib(i,u,g)}),
vec2(${ib(c,u,g)}));
}`}let f=l.length,d=l[l.length-1];m+=`
return getChannel(
getT${f}(${ib(i,u,d)}),
vec2(${ib(c,u,d)}));`,this.userCode=`
float getValue(${i.map(h=>"int "+h)}) {
${m}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[o-1]} = ${a[o-1]} + 1;
if (${a[o-1]} < ${n[o-1]}) {
result.g = getValue(${a});
}
${a[o-2]} = ${a[o-2]} + 1;
if (${a[o-2]} < ${n[o-2]}) {
result.a = getValue(${a});
}
${a[o-1]} = ${a[o-1]} - 1;
if (${a[o-2]} < ${n[o-2]} &&
${a[o-1]} < ${n[o-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function ib(r,e,t){let n=r.indexOf(e);return r.map((s,a)=>a===n?`${s} - ${t}`:s).join()}function Ic(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return Qt({inputs:{x:o.complexTensorInfos.imag},backend:t})}var qM={kernelName:sp,backendName:"webgl",kernelFunc:Ic};function Sc(r,e,t){let n=r[0].dtype;if(n==="complex64"){let c=r.map(h=>tl({inputs:{input:h},backend:t})),p=r.map(h=>Ic({inputs:{input:h},backend:t})),m=Sc(c,e,t),f=Sc(p,e,t),d=An({inputs:{real:m,imag:f},backend:t});return c.forEach(h=>t.disposeIntermediateTensorInfo(h)),p.forEach(h=>t.disposeIntermediateTensorInfo(h)),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}let o=t.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let c=r.map(x=>{let y=b.sizeFromShape(x.shape.slice(e));return ue({inputs:{x},backend:t,attrs:{shape:[-1,y]}})}),p=c.map(x=>({vals:t.readSync(x.dataId),shape:x.shape})),m=I.computeOutShape(c.map(x=>x.shape),1),f=c[0].shape[0]===1,d=QO(p,m,n,f),h=I.computeOutShape(r.map(x=>x.shape),e),g=t.makeTensorInfo(h,n,d);return c.forEach(x=>t.disposeIntermediateTensorInfo(x)),g}if(r.length>U().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(r.length/2),p=Sc(r.slice(0,c),e,t),m=Sc(r.slice(c),e,t),f=Sc([p,m],e,t);return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let c=new kI(r.map(p=>p.shape),e);return t.runWebGLProgram(c,r,n)}let{tensors2D:s,outShape:a}=vQ(r,e,t),i=new _I(s.map(c=>c.shape)),l=t.runWebGLProgram(i,s,n);s.forEach(c=>t.disposeIntermediateTensorInfo(c));let u=ue({inputs:{x:l},attrs:{shape:a},backend:t});return t.disposeIntermediateTensorInfo(l),u}function vQ(r,e,t){let n=I.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>ue({inputs:{x:s},attrs:{shape:[-1,b.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:n}}function vI(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n,s=b.parseAxisParam(o,e[0].shape)[0],a=I.computeOutShape(e.map(u=>u.shape),s);if(b.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>b.sizeFromShape(u.shape)>0);if(i.length===1)return Qt({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return I.assertParamsConsistent(l,s),Sc(i,s,t)}var KM={kernelName:ni,backendName:"webgl",kernelFunc:vI};var Kh=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,y=g?2:3,w=g?3:1,_="",C="";n&&(o?_=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?_=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:_=`
float activation(float x) {
${n}
}
`,C="result = activation(result);");let A=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${_}
const ivec2 strides = ivec2(${l}, ${u});
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${w}];
ivec2 xRCCorner =
ivec2(coords[${x}], coords[${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 < ${m}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${g}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${h===1}) {
if (${g}) {
dotProd +=
getX(batch, xR, xC, ${d}) *
getW(wR, wC, ${d}, d2);
} else {
dotProd +=
getX(batch, ${d}, xR, xC) *
getW(wR, wC, ${d}, d2);
}
} else if (${h===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2)
);
if (${g}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${h===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2),
getW(wR, wC, ${d} + 2, d2)
);
if (${g}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1),
getX(batch, xR, xC, ${d} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC),
getX(batch, ${d} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${A}
${C}
setOutput(result);
}
`}},CI=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,o=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${a}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${o});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${p}; wF++) {
int xF = xFCorner + wF * ${l};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${h===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${d}) *
getW(wF, wR, wC, ${d}, d2);
} else if (${h===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${h===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1),
getX(batch, xF, xR, xC, ${d} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2),
getW(wF, wR, wC, ${d} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}};var II=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=jt(this.outputShape.length);let{dataFormat:n}=t,o=Gt(),s=n==="channelsLast",a=s?0:1,i=s?1:2,l=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u="";for(let c=0;c<=1;c++)for(let p=0;p<=1;p++)u+=`
blockIndex = rc.y + ${p};
pos = rc.x + ${c};
${l}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${i}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${s}) {
innerDims = vec2(d1, ch);
result[${c*2+p}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${c*2+p}] = 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;
${u}
${o.output} = result;
}
`}};function ab({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=n.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(!((p===1||m===1)&&c>oI)&&u.isPacked&&f&&u.texture!=null&&l[2]%2!=0&&b.arraysEqual(u.shape.slice(-3),l.slice(-3))){let _=l[0]*l[1]*(l[2]+1),C={dataId:r.dataId,shape:[1,_,t.inChannels],dtype:r.dtype},A=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,b.assert(ql(u.shape,C.shape),()=>`packed reshape ${u.shape} to ${C.shape} isn't free`);let D=ue({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(D);let R=vc({a:C,b:D,backend:n,transposeA:d,transposeB:h,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),P=n.texData.get(R.dataId);b.assert(P.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=A,P.shape=t.outShape,g=Qt({inputs:{x:R},backend:n}),g.shape=t.outShape,x.push(R)}else{let _=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],C=ue({inputs:{x:r},backend:n,attrs:{shape:[1,_,t.inChannels]}}),A=ue({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}}),D=vc({a:C,b:A,transposeA:d,transposeB:h,backend:n,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=ue({inputs:{x:D},backend:n,attrs:{shape:t.outShape}}),x.push(C),x.push(A),x.push(D)}for(let _ of x)n.disposeIntermediateTensorInfo(_);return g}function lb({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,x=[h,g],y=!0,w=!1,_=[],C=ue({inputs:{x:r},backend:n,attrs:{shape:r.shape.slice(1)}}),A=ue({inputs:{x:e},backend:n,attrs:{shape:[1,h,b.sizeFromShape(e.shape)/h]}});_.push(C),_.push(A);let D=new II(x,t),R=[C.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],P=n.runWebGLProgram(D,[C],"float32",R),L=ue({inputs:{x:P},backend:n,attrs:{shape:[1,x[0],x[1]]}});_.push(P),_.push(L);let G=o!=null,W=s!=null,j=i==="leakyrelu",H=i?Xl(i,!0):null,q=new Hh(L.shape,A.shape,[1,g,t.outChannels],y,w,G,H,W,j),X=[L,A];if(o&&X.push(o),W&&X.push(s),j){let se=n.makeTensorInfo([],"float32",b.createScalarValue(a,"float32"));X.push(se),_.push(se)}let re=n.runWebGLProgram(q,X,"float32"),J=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],oe=ue({inputs:{x:re},backend:n,attrs:{shape:J}});_.push(re);for(let se of _)n.disposeIntermediateTensorInfo(se);return oe}function CQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=I.convertConv2DDataFormat(l),m=I.computeConv2DInfo(o.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=ab({x:o,filter:s,convInfo:m,backend:t});else if(U().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)f=lb({x:o,filter:s,convInfo:m,backend:t});else{let h=new Kh(m);f=t.runWebGLProgram(h,[o,s],"float32")}let d=ue({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var XM={kernelName:Mo,backendName:"webgl",kernelFunc:CQ};var SI=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=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} - ${o};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
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);
}
`}},NI=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=n-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${p}];
ivec2 dyCorner = ivec2(coords[${u}], coords[${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) / ${o}.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) / ${s}.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 < ${e.outChannels}; d2++) {
if (${a}) {
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);
}
`}},TI=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.padInfo.front,a=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} - ${s};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},EI=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=n-1-e.padInfo.top,c=o-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${l}, ${u}, ${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) / ${s}.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) / ${a}.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 < ${o}; 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 = ${o} - 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 IQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=n,p=I.convertConv2DDataFormat(l),m=I.computeConv2DInfo(o.shape,c,a,1,i,u,!1,p),f=new SI(m);return t.runWebGLProgram(f,[o,s],"float32")}var YM={kernelName:Kc,backendName:"webgl",kernelFunc:IQ};function SQ(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=n,p=I.convertConv2DDataFormat(u),m=I.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new NI(m);return t.runWebGLProgram(f,[o,s],"float32")}var ZM={kernelName:Lo,backendName:"webgl",kernelFunc:SQ};function NQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=I.computeConv3DInfo(o.shape,s.shape,a,l,i),c=new CI(u);return t.runWebGLProgram(c,[o,s],"float32")}var JM={kernelName:hl,backendName:"webgl",kernelFunc:NQ};function TQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,filterShape:l}=n,u=I.computeConv3DInfo(o.shape,l,a,1,i),c=new TI(u);return t.runWebGLProgram(c,[o,s],"float32")}var QM={kernelName:Xc,backendName:"webgl",kernelFunc:TQ};function EQ(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{pad:a,strides:i,inputShape:l}=n,u=I.computeConv3DInfo(l,s.shape,i,1,a),c=new EI(u);return t.runWebGLProgram(c,[o,s],"float32")}var eL={kernelName:Yc,backendName:"webgl",kernelFunc:EQ};var AQ=tb+`
return cos(x);
`,DQ=ve({opSnippet:AQ}),tL={kernelName:zo,backendName:"webgl",kernelFunc:DQ};var $Q=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,RQ=ve({opSnippet:$Q}),rL={kernelName:Bo,backendName:"webgl",kernelFunc:RQ};var AI=class{constructor(e,t,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=n;this.outputShape=[c,p,m,u];let f=o==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,_,C]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
const float height_ratio = float(${g});
const float width_ratio = float(${w});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${x};
float width_scale = ${_};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${d} ) {
setOutput(float(${s}));
return;
}
float in_x = ${C};
if( in_x < 0.0 || in_x > ${h} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${f} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}};var FQ=r=>{let{inputs:e,backend:t,attrs:n}=r,{image:o,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=n,c=new AI(o.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[o,s,a],"float32")},nL={kernelName:Hi,backendName:"webgl",kernelFunc:FQ};var ub=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let o=e.length,s=t?"0.0":`getX(${oL(o,"coords")})`,a=e[e.length-1],i="",l="";t?(i=n?`end != ${a-1}`:"end != 0",l=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",l=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${Ue(o)} coords = getOutputCoords();
int end = ${sL(o,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${l};
${sL(o,"coords")} = idx;
val += getX(${oL(o,"coords")});
}
setOutput(val);
}
`}};function oL(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function sL(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function OQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n,l=o.shape.length,u=I.getAxesPermutation([s],l),c=o;u!=null&&(c=Pt({inputs:{x:o},backend:t,attrs:{perm:u}}));let p=I.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${o.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=Qt({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new ub(c.shape,!1,i),g=[[d]],x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new ub(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=I.getUndoAxesPermutation(u),h=Pt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var iL={kernelName:Vo,backendName:"webgl",kernelFunc:OQ};function PQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a,binaryOutput:i}=n;if(o.shape.length===1){let l=t.readSync(o.dataId),u=t.readSync(s.dataId),c=Yy(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(o.shape.length===2){let l=t.bufferSync(o),u=t.bufferSync(s),c=ZO(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var aL={kernelName:Zc,backendName:"webgl",kernelFunc:PQ};var DI=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 MQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n,i=o.shape[0],l=a==="NHWC"?o.shape[1]:o.shape[2],u=a==="NHWC"?o.shape[2]:o.shape[3],c=a==="NHWC"?o.shape[3]:o.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new DI(d,s,a);return t.runWebGLProgram(h,[o],o.dtype)}var lL={kernelName:qi,backendName:"webgl",kernelFunc:MQ};var Xh=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=jt(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,l=e.outChannels/e.inChannels,u="",c="";n&&(o?u=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?u=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:u=`
float activation(float x) {
${n}
}
`,c="result = activation(result);");let p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${u}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${l};
int q = d2 - d1 * ${l};
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 < ${a}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${i}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${p}
${c}
setOutput(result);
}
`}};var Yh=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=jt(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,l=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,p=e.filterWidth,m=p,f=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x<p;x++)f+=`
vec4 xTexelC${x*2};
int xTexelC${x*2}Ready;
vec4 xTexelC${x*2+1};
int xTexelC${x*2+1}Ready;
vec4 xC${x};`;f+=`
for (int r = 0; r < ${c}; r++) {
`;for(let x=0;x<p;x++)f+=`
xTexelC${x*2} = vec4(0.0);
xTexelC${x*2}Ready = 0;
xTexelC${x*2+1} = vec4(0.0);
xTexelC${x*2+1}Ready = 0;
xC${x} = vec4(0.0);`;f+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let x=0;x<(m+1)/2;x++){let y=x*2;if(f+=`
xC = xCCorner + ${y*u};
`,l===1){if(y<p&&(i%2==1?(f+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
`,u===1&&y>0?f+=`
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
`:f+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${y} = vec4(previous.zw, xTexelC${y}.xy);
} else {
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
}
`):f+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xC${y} = xTexelC${y};
`,y+1<p)){let w=i%2==0?b.nearestLargerEven(u):u;u%2==0&&i%2==1||u%2!=0&&i%2!=1?(f+=`
xCOffset = xC + imod(pads[1], 2) + ${w};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
`,u>1&&(f+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
xTexelC${y}Ready = 1;
}
`),f+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
`):w===1?f+=`
xC${y+1} = xTexelC${y};
`:f+=`
xCOffset = xC + ${w};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y+1} = xTexelC${y+1};
`}}else y<p&&(i%2==1?(f+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`,y+1<p&&(f+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
`)):(f+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(
xTexelC${y}.xy, xTexelC${y+1}.xy);
`,y+1<p&&(f+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`)));y<p&&(f+=`
wTexel = getW(r, ${y}, d1, q);
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
`,y+1<p&&(f+=`
wTexel = getW(r, ${y+1}, d1, q);
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
`))}f+=`
}
`,f+=`
}
`;let d="",h="";n&&(o?d=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?d=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:d=`vec4 activation(vec4 x) {
${n}
}`,h="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${d}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${a};
int q = d2 - d1 * ${a};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${f}
vec4 result = dotProd - vec4(0.000000000000001);
${g}
${h}
setOutput(result);
}
`}};function LQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=n,c=l;c==null&&(c=[1,1]),b.assert(I.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=I.computeConv2DInfo(o.shape,s.shape,a,c,i,u,!0),m;U().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new Yh(p):m=new Xh(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return t.runWebGLProgram(m,[o,s],"float32",f)}var uL={kernelName:Go,backendName:"webgl",kernelFunc:LQ};var $I=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=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 * ${a} + 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} - ${o};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},RI=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${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) / ${o}.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) / ${s}.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 < ${l}; dm++) {
int d2 = d1 * ${l} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function zQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=n,p=I.computeConv2DInfo(o.shape,c,a,i,l,u,!0),m=new $I(p);return t.runWebGLProgram(m,[o,s],"float32")}var cL={kernelName:Jc,backendName:"webgl",kernelFunc:zQ};function BQ(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=n,p=I.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new RI(p);return t.runWebGLProgram(m,[o,s],"float32")}var pL={kernelName:Qc,backendName:"webgl",kernelFunc:BQ};var FI=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function VQ(r){let{inputs:e,backend:t}=r,{x:n}=e,o=[...n.shape,...n.shape],s=b.sizeFromShape(n.shape),a=ue({inputs:{x:n},backend:t,attrs:{shape:[s]}}),i=new FI(s),l=t.runWebGLProgram(i,[a],a.dtype),u=ue({inputs:{x:l},backend:t,attrs:{shape:o}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var mL={kernelName:ep,backendName:"webgl",kernelFunc:VQ};var OI=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:o,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=o;this.userCode=`
const ivec2 strides = ivec2(${s}, ${a});
const ivec2 pads = ivec2(${p}, ${m});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${u};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${l}; 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 GQ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=I.computeDilation2DInfo(o.shape,s.shape,a,i,"NHWC",l),c,p=new OI(u);c=t.runWebGLProgram(p,[o,s],"float32");let m=ue({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var fL={kernelName:gl,backendName:"webgl",kernelFunc:GQ};function WQ(r){let{inputs:e,backend:t,attrs:n}=r,{equation:o}=n,s=e,{allDims:a,summedDims:i,idDims:l}=I.decodeEinsumEquation(o,s.length);I.checkEinsumDimSizes(a.length,l,s);let{path:u,steps:c}=I.getEinsumComputePath(i,l),p=c.length,m=null,f=a.length,d=[];for(let h=0;h<p;++h){for(let g of c[h]){let{permutationIndices:x,expandDims:y}=I.getEinsumPermutation(f,l[g]),w;I.isIdentityPermutation(x)?w=s[g]:(w=Pt({inputs:{x:s[g]},backend:t,attrs:{perm:x}}),d.push(w));let _=w.shape.slice();for(let C=0;C<y.length;++C)_.splice(y[C],0,1);b.arraysEqual(w.shape,_)||(w=ue({inputs:{x:w},backend:t,attrs:{shape:_}}),d.push(w)),m===null?m=w:(m=qh({inputs:{a:w,b:m},backend:t}),d.push(m))}h<p-1&&(u[h]>=0&&(m=kc({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var dL={kernelName:tp,backendName:"webgl",kernelFunc:WQ};var UQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",jQ=`
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;
`,HQ=ve({opSnippet:UQ,packedOpSnippet:jQ}),hL={kernelName:Uo,backendName:"webgl",kernelFunc:HQ};var qQ="return (b >= 1.0) ? a : a * (b + 1.0);",KQ=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,XQ=r=>{let{inputs:e,backend:t}=r,{dy:n,y:o}=e,s=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ys(KQ,n.shape,o.shape):new No(qQ,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)},gL={kernelName:rp,backendName:"webgl",kernelFunc:XQ};var YQ=`
return vec4(equal(a, b));
`,ZQ="return float(a == b);",JQ=at({opSnippet:ZQ,packedOpSnippet:YQ,dtype:"bool",cpuKernelImpl:eP}),xL={kernelName:Xi,backendName:"webgl",kernelFunc:JQ};var QQ=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${I.ERF_P};
float a1 = ${I.ERF_A1};
float a2 = ${I.ERF_A2};
float a3 = ${I.ERF_A3};
float a4 = ${I.ERF_A4};
float a5 = ${I.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,eee=ve({opSnippet:QQ}),yL={kernelName:Ki,backendName:"webgl",kernelFunc:eee};var bL="return exp(x);",PI=ve({opSnippet:bL,packedOpSnippet:bL,cpuKernelImpl:tP,dtype:"float32"}),wL={kernelName:jo,backendName:"webgl",kernelFunc:PI};function cb(r){let{inputs:e,attrs:t,backend:n}=r,{dim:o}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=o;return o<0&&(b.assert(-(a+1)<=o,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+o+1),i.splice(l,0,1),ue({inputs:{x:s},backend:n,attrs:{shape:i}})}var _L={kernelName:oi,backendName:"webgl",kernelFunc:cb};var kL="return exp(x) - 1.0;",tee=ve({opSnippet:kL,packedOpSnippet:kL,cpuKernelImpl:rP}),vL={kernelName:Yi,backendName:"webgl",kernelFunc:tee};var pb=class{constructor(e,t,n){this.variableNames=["real","imag"];let o=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${o}.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 = ${s};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${o});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${o}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function mb(r,e,t){let n=t.texData.get(r.dataId),o=b.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=o/s,i=ue({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new pb("real",l,e),c=new pb("imag",l,e),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=An({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=ue({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function ree(r){let{inputs:e,backend:t}=r,{input:n}=e;return mb(n,!1,t)}var CL={kernelName:np,backendName:"webgl",kernelFunc:ree};var MI=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function rl(r){let{backend:e,attrs:t}=r,{shape:n,value:o}=t,{dtype:s}=t;if(s=s||b.inferDtype(o),s==="string"){let a=b.getArrayFromDType(s,b.sizeFromShape(n));return a.fill(o),e.makeTensorInfo(n,s,a)}else{let a=new MI(n,o),i=[[o]];return e.runWebGLProgram(a,[],s,i)}}var IL={kernelName:xl,backendName:"webgl",kernelFunc:rl};var LI=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}};var SL={kernelName:Zi,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,n=e,o=new LI(t.shape);return n.runWebGLProgram(o,[t],t.dtype)}};var NL="return floor(x);",nee=ve({opSnippet:NL,packedOpSnippet:NL,cpuKernelImpl:nP}),TL={kernelName:Ho,backendName:"webgl",kernelFunc:nee};var oee=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,see=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,iee=at({opSnippet:oee,packedOpSnippet:see,dtype:"int32"}),EL={kernelName:qo,backendName:"webgl",kernelFunc:iee};var zI=class{constructor(e){this.variableNames=["A"];let t=Gt(),[n,o]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}};var BI=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Gt(),[n,o]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${o}.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;
}
`}};var AL={kernelName:nf,backendName:"webgl",kernelFunc:aee},Pm;function aee(r){let{inputs:e,backend:t,attrs:n}=r,{pixels:o}=e,{numChannels:s}=n,a=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[l,u]=a?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[u,l],p=[u,l,s];(i||a)&&(Pm==null&&(Pm=document.createElement("canvas").getContext("2d")),Pm.canvas.width=l,Pm.canvas.height=u,Pm.drawImage(o,0,0,l,u),o=Pm.canvas);let m=t.makeTensorInfo(c,"int32");t.texData.get(m.dataId).usage=Ur.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),o);let f=U().getBool("WEBGL_PACK")?new BI(p):new zI(p),d=t.runWebGLProgram(f,[m],"int32");return t.disposeData(m.dataId),d}function lee(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=I.convertConv2DDataFormat(c),g=I.computeConv2DInfo(o.shape,s.shape,l,p,u,m,!1,h),x,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=ab({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(U().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)x=lb({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let _=a!=null,C=i!=null,A=f==="leakyrelu",D=f?Xl(f,!1):null,R=new Kh(g,_,D,C,A),P=[o,s];if(a&&P.push(a),i&&P.push(i),A){let L=t.makeTensorInfo([],"float32",b.createScalarValue(d,"float32"));P.push(L),y.push(L)}x=t.runWebGLProgram(R,P,"float32")}let w=ue({inputs:{x},backend:t,attrs:{shape:g.outShape}});return y.push(x),y.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var DL={kernelName:xi,backendName:"webgl",kernelFunc:lee};function uee(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),b.assert(I.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=I.computeConv2DInfo(o.shape,s.shape,l,h,u,p,!0),x=U().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,y=m?Xl(m,x):null,w=[o,s],_=a!=null,C=i!=null,A=m==="leakyrelu";if(_&&w.push(a),C&&w.push(i),A){let L=t.makeTensorInfo([],"float32",b.createScalarValue(f,"float32"));w.push(L),d.push(L)}let D;x?D=new Yh(g,_,y,C,A):D=new Xh(g,_,y,C,A);let R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],P=t.runWebGLProgram(D,w,"float32",R);return d.forEach(L=>t.disposeIntermediateTensorInfo(L)),P}var $L={kernelName:yi,backendName:"webgl",kernelFunc:uee};var VI=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let o=Ue(t.length),s=Ue(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${this.strides});
void main() {
${s} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function cee(r){let{inputs:e,backend:t}=r,{params:n,indices:o}=e,s=o.shape,a=s[s.length-1],i=b.sizeFromShape(n.shape),[l,u,c,p]=I.prepareAndValidate(n,o),m=ue({inputs:{x:o},backend:t,attrs:{shape:[u,a]}}),f=ue({inputs:{x:n},backend:t,attrs:{shape:[b.sizeFromShape(n.shape)/c,c]}});if(t.shouldExecuteOnCPU([n,o])||n.dtype==="string"){let x=t.readSync(o.dataId),y=t.bufferSync(n),w=oP(x,y,n.dtype,u,a,c,p,n.shape,i);return t.makeTensorInfo(l,n.dtype,w.values)}let d=new VI(a,p,[u,c]),h=t.runWebGLProgram(d,[f,m],f.dtype),g=ue({inputs:{x:h},backend:t,attrs:{shape:l}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var RL={kernelName:Ji,backendName:"webgl",kernelFunc:cee};var GI=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=Ue(this.rank),o=pee(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${o}));
}
`}};function pee(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("int(getIndices(resRC.x, resRC.z))"):n.push(`${t[o]}`);return n.join()}function WI(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,indices:s}=e,{axis:a,batchDims:i}=n,l=b.parseAxisParam(a,o.shape)[0],u=t.readSync(s.dataId),c=o.shape[l];for(let _=0;_<u.length;++_){let C=u[_];b.assert(C<=c-1&&C>=0,()=>`GatherV2: the index value ${C} is not in [0, ${c-1}]`)}let p=I.segment_util.collectGatherOpShapeInfo(o,s,l,i),m=b.sizeFromShape(s.shape),f=[],d=ue({inputs:{x:o},backend:t,attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]}}),h=ue({inputs:{x:s},backend:t,attrs:{shape:[p.batchSize,m/p.batchSize]}});f.push(d),f.push(h);let g=[p.batchSize,p.outerSize,m/p.batchSize,p.sliceSize];if(t.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let _=t.bufferSync(h),C=t.bufferSync(d),A=sP(C,_,g);return f.forEach(D=>t.disposeIntermediateTensorInfo(D)),t.makeTensorInfo(p.outputShape,A.dtype,A.values)}let x=new GI(d.shape,g),y=t.runWebGLProgram(x,[d,h],d.dtype);f.push(y);let w=ue({inputs:{x:y},backend:t,attrs:{shape:p.outputShape}});return f.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var FL={kernelName:si,backendName:"webgl",kernelFunc:WI};var mee="return float(a > b);",fee=`
return vec4(greaterThan(a, b));
`,dee=at({opSnippet:mee,packedOpSnippet:fee,cpuKernelImpl:iP,dtype:"bool"}),OL={kernelName:Qi,backendName:"webgl",kernelFunc:dee};var hee="return float(a >= b);",gee=`
return vec4(greaterThanEqual(a, b));
`,xee=at({opSnippet:hee,packedOpSnippet:gee,dtype:"bool",cpuKernelImpl:aP}),PL={kernelName:Xo,backendName:"webgl",kernelFunc:xee};function yee(r){let{inputs:e,backend:t}=r,{input:n}=e;return mb(n,!0,t)}var ML={kernelName:op,backendName:"webgl",kernelFunc:yee};var bee="return float(!isnan(x) && !isinf(x));",wee=ve({opSnippet:bee,dtype:"bool"}),LL={kernelName:ea,backendName:"webgl",kernelFunc:wee};var _ee="return float(isinf(x));",kee=ve({opSnippet:_ee,dtype:"bool"}),zL={kernelName:ta,backendName:"webgl",kernelFunc:kee};var vee="return float(isnan(x));",Cee=ve({opSnippet:vee,dtype:"bool"}),BL={kernelName:ra,backendName:"webgl",kernelFunc:Cee};var Iee="return float(a < b);",See=`
return vec4(lessThan(a, b));
`,Nee=at({opSnippet:Iee,packedOpSnippet:See,cpuKernelImpl:lP,dtype:"bool"}),VL={kernelName:na,backendName:"webgl",kernelFunc:Nee};var Tee="return float(a <= b);",Eee=`
return vec4(lessThanEqual(a, b));
`,Aee=at({opSnippet:Tee,packedOpSnippet:Eee,cpuKernelImpl:uP,dtype:"bool"}),GL={kernelName:oa,backendName:"webgl",kernelFunc:Aee};function Dee(r){let{backend:e,attrs:t}=r,{start:n,stop:o,num:s}=t,a=cP(n,o,s);return e.makeTensorInfo([a.length],"float32",a)}var WL={kernelName:ip,backendName:"webgl",kernelFunc:Dee};var $ee=`if (x < 0.0) return NAN;
return log(x);`,Ree=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,Fee=ve({opSnippet:$ee,packedOpSnippet:Ree,cpuKernelImpl:pP}),UL={kernelName:Zo,backendName:"webgl",kernelFunc:Fee};var Oee="return log(1.0 + x);",Pee=ve({opSnippet:Oee}),jL={kernelName:sa,backendName:"webgl",kernelFunc:Pee};var Mee="return float(a >= 1.0 && b >= 1.0);",Lee=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,zee=at({opSnippet:Mee,packedOpSnippet:Lee,dtype:"bool"}),HL={kernelName:ia,backendName:"webgl",kernelFunc:zee};var Bee="return float(!(x >= 1.0));",Vee=ve({opSnippet:Bee}),qL={kernelName:au,backendName:"webgl",kernelFunc:Vee};var Gee="return float(a >= 1.0 || b >= 1.0);",Wee=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Uee=at({opSnippet:Gee,packedOpSnippet:Wee,dtype:"bool"}),KL={kernelName:lu,backendName:"webgl",kernelFunc:Uee};var UI=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${l};
setOutput(val);
}
`}};var jI=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${a};
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 = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${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 * ${l};
setOutput(result);
}
`}};var jee=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=n,u=U().getBool("WEBGL_PACK_NORMALIZATION")?new jI(o.shape,s,a,i,l):new UI(o.shape,s,a,i,l);return t.runWebGLProgram(u,[o],o.dtype)},XL={kernelName:yl,backendName:"webgl",kernelFunc:jee};var HI=class{constructor(e,t,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${o}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${o})
* float(${s})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${s});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}};var Hee=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=n,p=new HI(o.shape,i,l,u,c);return t.runWebGLProgram(p,[o,s,a],o.dtype)},YL={kernelName:ap,backendName:"webgl",kernelFunc:Hee};function ZL(r,e,t,n){let o=b.sizeFromShape(e),a=b.sizeFromShape(r.shape)/o,i=ue({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=Vn(i,r.dtype,"max",n),u=ue({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}function qI(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reductionIndices:s,keepDims:a}=n,i=o.shape.length,l=b.parseAxisParam(s,o.shape),u=l,c=I.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=t.texData.get(f.dataId).values,_=new Array(i);for(let D=0;D<_.length;D++)_[D]=o.shape[c[D]];let C=wc(w,o.shape,o.dtype,c,_);f=t.makeTensorInfo(_,o.dtype);let A=t.texData.get(f.dataId);A.values=C}else f=Yl(o,c,t);u=I.getInnerMostAxes(u.length,i)}I.assertAxesAreInnerMostDims("max",u,i);let[d,h]=I.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=I.expandShapeToKeepDim(d,l));let x;if(m){let w=t.texData.get(f.dataId).values,_=mP(w,b.sizeFromShape(h),g,o.dtype);x=t.makeTensorInfo(g,o.dtype);let C=t.texData.get(x.dataId);C.values=_}else x=ZL(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),x}var JL={kernelName:Jo,backendName:"webgl",kernelFunc:qI};var qee=eb+`
return max(a, b);
`,Kee=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Kl+`
return result;
`,Xee=at({opSnippet:qee,packedOpSnippet:Kee,cpuKernelImpl:fP}),QL={kernelName:Qo,backendName:"webgl",kernelFunc:Xee};function Yee(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;qs(o,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;b.assert(I.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=I.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&b.arraysEqual(c.inShape,c.outShape))return Qt({inputs:{x:o},backend:t});let p=new Di(c,"max",!1);return t.runWebGLProgram(p,[o],o.dtype)}var ez={kernelName:es,backendName:"webgl",kernelFunc:Yee};function Zee(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=n,c=[1,1,1],p=I.computePool3DInfo(o.shape,s,a,c,i,u,l),m=new Cc(p,"max",!1);return t.runWebGLProgram(m,[o],o.dtype)}var tz={kernelName:bl,backendName:"webgl",kernelFunc:Zee};var KI=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,o=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${s};
wR += ${o}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; 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 = ${u} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},XI=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
const ivec3 pads = ivec3(${p}, ${m}, ${f});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${l};
wD += ${s}) {
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 < ${u};
wR += ${a}) {
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) / ${o}.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 = ${d} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${u} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function Jee(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=I.computePool3DInfo(a.shape,i,l,p,u,c),f=new Cc(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new XI(m),g=t.runWebGLProgram(h,[o,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var rz={kernelName:up,backendName:"webgl",kernelFunc:Jee};function Qee(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s,output:a}=e,i=s;qs([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=n,m=I.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Di(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new KI(m),x=t.runWebGLProgram(g,[o,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var nz={kernelName:lp,backendName:"webgl",kernelFunc:Qee};function oz(r,e,t,n){let o=new Di(t,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new Di(t,"max",!0,!0,e);let a=n.runWebGLProgram(o,[r],"float32");return[s,a]}var sz={kernelName:cp,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{filterSize:o,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;b.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];b.assert(I.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=I.computePool2DInfo(n.shape,o,s,u,a),[p,m]=oz(n,i,c,l);return[p,m]}};function iz(r,e,t,n){let o=b.sizeFromShape(e),a=b.sizeFromShape(r.shape)/o,i=ue({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=Vn(i,"float32","mean",n),u=ue({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var az={kernelName:ts,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{keepDims:o,axis:s}=e,a=t,i=n.shape.length,l=b.parseAxisParam(s,n.shape),u=l,c=I.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let _=a.texData.get(d.dataId).values,C=new Array(i);for(let R=0;R<C.length;R++)C[R]=n.shape[c[R]];let A=wc(_,n.shape,n.dtype,c,C);d=a.makeTensorInfo(C,n.dtype);let D=a.texData.get(d.dataId);D.values=A}else d=Yl(n,c,a);f.push(d),u=I.getInnerMostAxes(u.length,i)}I.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=I.computeOutAndReduceShapes(d.shape,u),x=h;o&&(x=I.expandShapeToKeepDim(h,l));let y=iz(d,g,x,a);for(let w of f)a.disposeIntermediateTensorInfo(w);return y}};function ete(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=b.parseAxisParam(s,o.shape),u=l,c=I.getAxesPermutation(u,i),p=o;c!=null&&(p=Pt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=I.getInnerMostAxes(u.length,o.shape.length)),I.assertAxesAreInnerMostDims("min",u,i);let[m,f]=I.computeOutAndReduceShapes(p.shape,u),d=b.sizeFromShape(f),h=ue({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Vn(h,h.dtype,"min",t),x;if(a){let y=I.expandShapeToKeepDim(m,l);x=ue({inputs:{x:g},backend:t,attrs:{shape:y}})}else x=ue({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var lz={kernelName:rs,backendName:"webgl",kernelFunc:ete};var tte=eb+`
return min(a, b);
`,rte=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Kl+`
return result;
`,nte=at({opSnippet:tte,packedOpSnippet:rte,cpuKernelImpl:dP}),uz={kernelName:ns,backendName:"webgl",kernelFunc:nte};var YI=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let o=e.length,s=Ue(o),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),u=n==="reflect"?0:1;if(o===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${u};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${u};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${i});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${o}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${u};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
}
}
${s} coords = outC - start;
setOutput(getX(${l}));
}
`}};var ZI=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let o=e.length,s=Ue(o),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Jt("rc",o),u=Jt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=`
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${m};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${m};
}
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${u.join()}), ${p});
${l[o-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${u.join()}), ${p});
}
`}else{let d=`
${s} source = rc;
${s} lt = ${s}(lessThan(source, start));
${s} gte = ${s}(greaterThanEqual(source, end));
${s} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${m}) +
gte * ((end - 1) * 2 - source + ${m});
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${u.join()}), ${p});
${l[o-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${u.join()}), ${p});
}
rc = outputLoc;
${l[o-2]} += 1;
if(${l[o-2]} < ${this.outputShape[o-2]}) {
${d}
result[2] = getChannel(getX(${u.join()}), ${p});
${l[o-1]} += 1;
if(${c}) {
${d}
result[3] = getChannel(getX(${u.join()}), ${p});
}
}
`}this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${i});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${f}
setOutput(result);
}
`}};var ote=({inputs:r,backend:e,attrs:t})=>{let{x:n}=r,{paddings:o,mode:s}=t,a=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ZI(n.shape,o,s):new YI(n.shape,o,s);return e.runWebGLProgram(a,[n],n.dtype)},cz={kernelName:os,backendName:"webgl",kernelFunc:ote};var ste=`if (b == 0.0) return NAN;
return mod(a, b);`,ite=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Kl+`
return result;
`,ate=at({opSnippet:ste,packedOpSnippet:ite}),pz={kernelName:aa,backendName:"webgl",kernelFunc:ate};var JI=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}};var lte=`
if (a == b) {
return 1.0;
};
return a / b;`,ute=`
// 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;
`,QI=at({opSnippet:lte,packedOpSnippet:ute,checkOutOfBounds:!0}),mz={kernelName:Wo,backendName:"webgl",kernelFunc:QI};var fz="return a - b;",eS=at({opSnippet:fz,packedOpSnippet:fz,supportsComplex:!0,cpuKernelImpl:AP}),dz={kernelName:ks,backendName:"webgl",kernelFunc:eS};function tS(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{dim:s}=n,a=b.parseAxisParam([s],o.shape),i=qI({inputs:{x:o},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=I.expandShapeToKeepDim(i.shape,a),u=ue({inputs:{x:i},backend:t,attrs:{shape:l}}),c=eS({inputs:{a:o,b:u},backend:t}),p=PI({inputs:{x:c},backend:t}),m=kc({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=ue({inputs:{x:m},backend:t,attrs:{shape:l}}),d=QI({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var hz={kernelName:ws,backendName:"webgl",kernelFunc:tS};function cte(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{numSamples:s,seed:a,normalized:i}=n,l=i?o:tS({inputs:{logits:o},backend:t,attrs:{dim:o.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new JI(u,c,s),m=[[a]],f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var gz={kernelName:pp,backendName:"webgl",kernelFunc:cte};var xz="return -x;";function pte(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])){let s=t.texData.get(n.dataId),[a,i]=gP(s.values,n.shape,n.dtype);return t.makeTensorInfo(i,n.dtype,a)}let o;return U().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Xs(n.shape,xz):o=new En(n.shape,xz),t.runWebGLProgram(o,[n],n.dtype)}var yz={kernelName:ii,backendName:"webgl",kernelFunc:pte};var mte=Gr.nonMaxSuppressionV3Impl;function fte(r){I.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=n,u=t.readSync(o.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=mte(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var bz={kernelName:ua,backendName:"webgl",kernelFunc:fte};var dte=Gr.nonMaxSuppressionV4Impl;function hte(r){I.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=dte(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var wz={kernelName:ca,backendName:"webgl",kernelFunc:hte};var gte=Gr.nonMaxSuppressionV5Impl;function xte(r){I.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=gte(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var _z={kernelName:pa,backendName:"webgl",kernelFunc:xte};var rS=class{constructor(e,t,n,o){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${o}), float(${n}),
float(index == coords.y)));
}
`}};var yte=r=>{let{inputs:e,backend:t,attrs:n}=r,{indices:o}=e,{depth:s,onValue:a,offValue:i}=n,l=b.sizeFromShape(o.shape),u=new rS(l,s,a,i),c=ue({inputs:{x:o},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],o.dtype);t.disposeIntermediateTensorInfo(c);let m=[...o.shape,s],f=ue({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},kz={kernelName:is,backendName:"webgl",kernelFunc:yte};function Zh(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="complex64"){let o=tl({inputs:{input:n},backend:t}),s=Zh({inputs:{x:o},backend:t}),a=Ic({inputs:{input:n},backend:t}),i=Zh({inputs:{x:a},backend:t}),l=An({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return rl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:t})}var vz={kernelName:hi,backendName:"webgl",kernelFunc:Zh};function Cz(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=tl({inputs:{input:n},backend:t}),s=Cz({inputs:{x:o},backend:t}),a=Ic({inputs:{input:n},backend:t}),i=Zh({inputs:{x:a},backend:t}),l=An({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return rl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:t})}var Iz={kernelName:ai,backendName:"webgl",kernelFunc:Cz};function bte(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n;if(e.length===1)return cb({inputs:{input:e[0]},backend:t,attrs:{dim:o}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{b.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),b.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=cb({inputs:{input:c},backend:t,attrs:{dim:o}});return i.push(p),p}),u=vI({inputs:l,backend:t,attrs:{axis:o}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var Sz={kernelName:li,backendName:"webgl",kernelFunc:bte};var nS=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let o=e.length,s=Ue(o),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${i});
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${s} coords = outC - start;
setOutput(getX(${l}));
}
}
`}};var oS=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let o=e.length,s=Ue(o),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Jt("rc",o),u=Jt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[o-1]} += 1;
if(${c}) {
`,o===1?"":`}
rc = outputLoc;
${l[o-2]} += 1;
if(${l[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${l[o-1]} += 1;
if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===1?2:4;h<g;h++)d+=`
${m[h]}
if (${f}) {
result[${h}] = float(value);
} else {
${s} source = rc - start;
result[${h}] = getChannel(getX(${u.join()}), ${p});
}
`;d+=o===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${i});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}};var sS=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{paddings:s,constantValue:a}=n;if(b.sizeFromShape(o.shape)===0){let u=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return rl({backend:t,attrs:{shape:u,value:a,dtype:o.dtype}})}let i=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oS(o.shape,s,a):new nS(o.shape,s,a),l=[[a]];return t.runWebGLProgram(i,[o],o.dtype,l)},Nz={kernelName:as,backendName:"webgl",kernelFunc:sS};var wte=`
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);
`,_te=`
// 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));
`+Kl+`
return result;
`,kte=at({opSnippet:wte,packedOpSnippet:_te}),Tz={kernelName:ls,backendName:"webgl",kernelFunc:kte};function vte(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=[],u=b.parseAxisParam(s,o.shape),c=u,p=I.getAxesPermutation(c,i),m=o;p!=null&&(m=Pt({inputs:{x:o},backend:t,attrs:{perm:p}}),c=I.getInnerMostAxes(c.length,i),l.push(m)),I.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=yP(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=I.computeOutAndReduceShapes(m.shape,c),g=b.sizeFromShape(h),x=ue({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),y=hu(o.dtype),w=Vn(x,y,"prod",t);f=ue({inputs:{x:w},backend:t,attrs:{shape:d}}),l.push(x),l.push(w)}if(a){l.push(f);let d=I.expandShapeToKeepDim(f.shape,u);f=ue({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var Ez={kernelName:ma,backendName:"webgl",kernelFunc:vte};var iS=r=>{let{backend:e,attrs:t}=r,{start:n,stop:o,step:s,dtype:a}=t,i=bP(n,o,s,a);return e.makeTensorInfo([i.length],a,i)},Az={kernelName:wl,backendName:"webgl",kernelFunc:iS};var Cte="return 1.0 / x;",Ite=ve({opSnippet:Cte}),Dz={kernelName:fa,backendName:"webgl",kernelFunc:Ite};var Ste=Nr+`
return (x < 0.0) ? 0.0 : x;
`,Nte=`
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;
`,Tte=ve({opSnippet:Ste,packedOpSnippet:Nte}),$z={kernelName:cs,backendName:"webgl",kernelFunc:Tte};var Ete=Nr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Ate=`
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;
`,Dte=ve({opSnippet:Ete,packedOpSnippet:Ate}),Rz={kernelName:ms,backendName:"webgl",kernelFunc:Dte};var aS=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}};var lS=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/p[0]},
${c[1]/p[1]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${l}.0,
${l}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-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 $te(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=U().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new lS(o.shape,l,u,s,a):new aS(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],"float32")}var Fz={kernelName:ps,backendName:"webgl",kernelFunc:$te};var uS=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
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), ${o-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Rte(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new uS(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var Oz={kernelName:dp,backendName:"webgl",kernelFunc:Rte};var cS=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${f};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};var pS=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/p[0]},
${c[1]/p[1]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${l}.0,
${l}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${f};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function Fte(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=U().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new pS(o.shape,l,u,s,a):new cS(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],o.dtype)}var Pz={kernelName:_l,backendName:"webgl",kernelFunc:Fte};var mS=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
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 >= ${i}) {
continue;
}
float sourceFracRow =
float(${l[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${l[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${o}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Ote(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new mS(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var Mz={kernelName:fp,backendName:"webgl",kernelFunc:Ote};var fS=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let o=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>o(l)).join(","),a=Ue(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}};var dS=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-${n} tensor is not yet supported`);this.outputShape=e;let o=Jt("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,i=Ue(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${s}){
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 = ${l(o.slice())};
if(${s}){
result.g = ${u(o.slice())};
}
if(${a}) {
result.b = ${c(o.slice())};
if(${s}) {
result.a = ${p(o.slice())};
}
}
setOutput(result);
}
`;function l(d){return m(d)}function u(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=e.map((y,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function Pte(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dims:s}=n,a=o.shape.length,i=b.parseAxisParam(s,o.shape);if(a===0)return Qt({inputs:{x:o},backend:t});let l=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dS(o.shape,i):new fS(o.shape,i);return t.runWebGLProgram(l,[o],o.dtype)}var Lz={kernelName:fs,backendName:"webgl",kernelFunc:Pte};var hS=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],o=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${s}
if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}};var zz={kernelName:ka,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:n}=r,{radians:o,fillValue:s,center:a}=e,i=t,l=new hS(n.shape,s),[u,c]=I.getImageCenter(a,n.shape[1],n.shape[2]),p=[[u,c,Math.sin(o),Math.cos(o)]];return i.runWebGLProgram(l,[n],n.dtype,p)}};var Mte=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,Lte=ve({opSnippet:Mte}),Bz={kernelName:ds,backendName:"webgl",kernelFunc:Lte};var zte="return inversesqrt(x);",Bte=ve({opSnippet:zte,cpuKernelImpl:wP}),Vz={kernelName:hs,backendName:"webgl",kernelFunc:Bte};var Jh=class{constructor(e,t,n,o,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Ue(s.length),u=Ue(a.length),c="";n===1?c="i":n===2&&(c="i, j");let p=`getIndices(${c})`,m="";o===1?m="i":o===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
${l} strides = ${l}(${s});
void main() {
${u} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${p});
flattenedIndex += index * ${d};
}
if (flattenedIndex == coords[0]) {
sum += ${f};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function Vte(r){let{inputs:e,backend:t,attrs:n}=r,{indices:o,updates:s}=e,{shape:a}=n,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=I.calculateShapes(s,o,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,o.dtype);let f=ue({inputs:{x:o},backend:t,attrs:{shape:[l,i]}}),d=ue({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new Jh(l,i,f.shape.length,d.shape.length,c,m),x=t.runWebGLProgram(g,[d,f,h],d.dtype),y=ue({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),y}var Gz={kernelName:da,backendName:"webgl",kernelFunc:Vte};var gS=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);o=l.join(),s=u.join()}let a=Ue(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${o});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}};function Gte(r){let{inputs:e,backend:t}=r,{condition:n,t:o,e:s}=e,a=new gS(n.shape.length,o.shape,o.shape.length);return t.runWebGLProgram(a,[n,o,s],hr(o.dtype,s.dtype))}var Wz={kernelName:ci,backendName:"webgl",kernelFunc:Gte};var Wte=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${I.SELU_SCALEALPHA};
float scale = ${I.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Ute=ve({opSnippet:Wte}),Uz={kernelName:ha,backendName:"webgl",kernelFunc:Ute};var jz="return 1.0 / (1.0 + exp(-1.0 * x));",jte=ve({opSnippet:jz,packedOpSnippet:jz,cpuKernelImpl:_P}),Hz={kernelName:xs,backendName:"webgl",kernelFunc:jte};var Hte=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,qte=ve({opSnippet:Hte}),qz={kernelName:xa,backendName:"webgl",kernelFunc:qte};var Kte=tb+`
return sin(x);
`,Xte=ve({opSnippet:Kte}),Kz={kernelName:gs,backendName:"webgl",kernelFunc:Xte};var Yte=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Zte=ve({opSnippet:Yte}),Xz={kernelName:ga,backendName:"webgl",kernelFunc:Zte};var Jte=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,Qte=ve({opSnippet:Jte}),Yz={kernelName:ya,backendName:"webgl",kernelFunc:Qte};var ere=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,paddings:a}=n;b.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,y)=>x*y),l=[[0,0]];l.push(...a);for(let x=1+s.length;x<o.shape.length;++x)l.push([0,0]);let u=[],c=sS({inputs:{x:o},backend:t,attrs:{paddings:l,constantValue:0}}),p=I.getReshaped(c.shape,s,i,!1),m=I.getPermuted(p.length,s.length,!1),f=I.getReshapedPermuted(c.shape,s,i,!1),d=ue({inputs:{x:c},backend:t,attrs:{shape:p}}),h=Pt({inputs:{x:d},backend:t,attrs:{perm:m}}),g=ue({inputs:{x:h},backend:t,attrs:{shape:f}});return u.push(c),u.push(d),u.push(h),u.forEach(x=>t.disposeIntermediateTensorInfo(x)),g},Zz={kernelName:mi,backendName:"webgl",kernelFunc:ere};function tre(r){let{inputs:e,backend:t}=r,{indices:n,values:o,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:
${o.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${a.shape}`);let i=t.readSync(n.dataId),l=t.readSync(o.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[p,m,f,d,h]=vP(i,n.shape,n.dtype,l,o.dtype,u,c);return[t.makeTensorInfo(m,n.dtype,p),t.makeTensorInfo([m[0]],o.dtype,f),t.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),t.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var Jz={kernelName:hp,backendName:"webgl",kernelFunc:tre};function rre(r){let{inputs:e,backend:t}=r,{inputIndices:n,inputShape:o,newShape:s}=e;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(o.dataId)),i=t.readSync(n.dataId),l=Array.from(t.readSync(s.dataId)),[u,c,p]=CP(i,n.shape,n.dtype,a,l);return[t.makeTensorInfo(c,n.dtype,u),t.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var Qz={kernelName:gp,backendName:"webgl",kernelFunc:rre};function nre(r){let{inputs:e,backend:t}=r,{data:n,indices:o,segmentIds:s}=e;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let a=t.readSync(n.dataId),i=t.readSync(o.dataId),l=t.readSync(s.dataId),[u,c]=Jy(a,n.shape,n.dtype,i,l,!0);return t.makeTensorInfo(c,n.dtype,u)}var e3={kernelName:xp,backendName:"webgl",kernelFunc:nre};function ore(r){let{inputs:e,backend:t}=r,{data:n,indices:o,segmentIds:s}=e;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let a=t.readSync(n.dataId),i=t.readSync(o.dataId),l=t.readSync(s.dataId),[u,c]=Jy(a,n.shape,n.dtype,i,l);return t.makeTensorInfo(c,n.dtype,u)}var t3={kernelName:yp,backendName:"webgl",kernelFunc:ore};function sre(r){let{inputs:e,backend:t,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:a}=e,{outputShape:i}=n,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=I.calculateShapes(s,o,i),m=!1,f=new Jh(u,l,o.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,o,a],s.dtype),h=ue({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var r3={kernelName:bp,backendName:"webgl",kernelFunc:sre};function ire(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{numOrSizeSplits:s,axis:a}=n,i=b.parseAxisParam(a,o.shape)[0],l=I.prepareSplitSize(o,s,i),u=o.shape.length,c=new Array(u).fill(0),p=o.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=Zs({inputs:{x:o},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var n3={kernelName:fi,backendName:"webgl",kernelFunc:ire};var o3="return sqrt(x);",are=ve({opSnippet:o3,packedOpSnippet:o3,cpuKernelImpl:IP}),s3={kernelName:ys,backendName:"webgl",kernelFunc:are};var lre="return x * x;",ure=ve({opSnippet:lre}),i3={kernelName:kl,backendName:"webgl",kernelFunc:ure};var a3="return (a - b) * (a - b);",cre=at({opSnippet:a3,packedOpSnippet:a3}),l3={kernelName:_s,backendName:"webgl",kernelFunc:cre};function pre({inputs:r,attrs:e,backend:t}){let{x:n}=r,o=Nr+`
return x > 0.0 ? 1.0 : float(${e.alpha});
`,s=new En(n.shape,o);return t.runWebGLProgram(s,[n],n.dtype)}var u3={kernelName:no,backendName:"webgl",kernelFunc:pre};var xS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=Ue(n.length),a=Ue(n.length),i="";if(o===1)i="coords * strides + begin";else{let l=0;i=n.map((u,c)=>(l++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${s} begin = ${s}(${e});
${s} strides = ${s}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function mre(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:x,outShape:y}=pr.sliceInfo(o.shape,s,a,i,l,u,c,p,m),w=ue({inputs:{x:o},backend:t,attrs:{shape:x}}),_;if(f){let A=Zs({inputs:{x:w},backend:t,attrs:{begin:d,size:g}});_=ue({inputs:{x:A},backend:t,attrs:{shape:y}}),t.disposeIntermediateTensorInfo(A)}else if(y.some(A=>A===0))_=t.makeTensorInfo(y,o.dtype,[]);else if(t.shouldExecuteOnCPU([w])){let R=t.texData.get(w.dataId).values,P=Ie(w.shape,w.dtype,R),L=SP(y,P,h,d);_=t.makeTensorInfo(y,w.dtype,L.values)}else{let D=new xS(d,h,y);_=t.runWebGLProgram(D,[w],w.dtype)}let C=ue({inputs:{x:_},backend:t,attrs:{shape:y}});return t.disposeIntermediateTensorInfo(w),t.disposeIntermediateTensorInfo(_),C}var c3={kernelName:ba,backendName:"webgl",kernelFunc:mre};function fre(r){let{inputs:e,backend:t,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:a,rightPad:i,padWidth:l,preserveShortSequences:u}=n,{data:c,dataSplits:p}=e,m=t.readSync(c.dataId),f=t.readSync(p.dataId),[d,h]=NP(m,f,o,s,a,i,l,u);return[t.makeTensorInfo([d.length],"string",d),t.makeTensorInfo(p.shape,"int32",h)]}var p3={kernelName:wp,backendName:"webgl",kernelFunc:fre};function dre(r){let{inputs:e,backend:t,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:a}=e;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),l=t.readSync(a.dataId)[0],[u,c,p]=TP(i,l,o),m=c.length;return[t.makeTensorInfo([m,2],"int32",u),t.makeTensorInfo([m],"string",c),t.makeTensorInfo([2],"int32",new Int32Array(p))]}var m3={kernelName:_p,backendName:"webgl",kernelFunc:dre};function hre(r){let{inputs:e,backend:t,attrs:n}=r,{numBuckets:o}=n,{input:s}=e;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let a=t.readSync(s.dataId),i=EP(a,o);return t.makeTensorInfo(s.shape,"int32",i)}var f3={kernelName:kp,backendName:"webgl",kernelFunc:hre};var gre="return tan(x);",xre=ve({opSnippet:gre}),d3={kernelName:vs,backendName:"webgl",kernelFunc:xre};var yre=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,bre=ve({opSnippet:yre}),h3={kernelName:Cs,backendName:"webgl",kernelFunc:bre};var yS=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let o=Ue(this.rank),s=wre(e);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function wre(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${t[o]}, ${r[o]})`);return n.join()}function bS(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reps:s}=n;if(o.dtype==="string"||o.shape.length>5){let l=t.readSync(o.dataId),u=o.dtype==="string"?l.map(m=>b.decodeString(m)):l,c=Ie(o.shape,o.dtype,u),p=DP(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new yS(o.shape,s);return t.runWebGLProgram(a,[o],o.dtype)}var g3={kernelName:Hn,backendName:"webgl",kernelFunc:bS};var wS=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},_S=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}};function Nc(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function x3(r){let e=1;for(;e<r;)e*=2;return e}function _re(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{k:s,sorted:a}=n,i=U().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=U().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=o.shape,c=u[u.length-1];if(t.shouldExecuteOnCPU([o])||c<i||s>l){let L=t.readSync(o.dataId),[G,W]=$P(L,u,o.dtype,s,a);return[t.makeTensorInfo(G.shape,G.dtype,G.values),t.makeTensorInfo(W.shape,W.dtype,W.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,o.dtype,[]),t.makeTensorInfo(u,"int32",[])];if(c===1)return[o,rl({attrs:{shape:u,dtype:"int32",value:0},backend:t})];let p=t.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?t.unpackTensor(o):o,h=b.sizeFromShape(u)/c,g=ue({inputs:{x:f},attrs:{shape:[h,c]},backend:t});m&&Nc(t,f);let x=x3(s),y=x3(c),w=null,_=()=>w===null?[g,g]:[g,w],C=(L,G,W)=>{let j=_(),H=new wS(W),X=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[L],[G]],re=w;w=t.runWebGLProgram(H,j,"int32",X),Nc(t,re)};for(let L=1;L<x;L*=2){let G=L*2;for(let W=L;W>=1;W/=2)C(G,W,[h,y])}for(let L=y;L>x;L/=2){let G=_(),W=new _S([h,L/2]),H=[[c],[w===null?1:0],[x]],q=w;w=t.runWebGLProgram(W,G,"int32",H),Nc(t,q);let X=x/2,re=X*2;for(let J=X;J>=1;J/=2)C(re,J,w.shape)}let A=w;w=Zs({inputs:{x:w},backend:t,attrs:{begin:0,size:[h,s]}}),Nc(t,A);let D=WI({inputs:{x:g,indices:w},backend:t,attrs:{axis:1,batchDims:1}});Nc(t,g);let R=u.slice(0,-1);R.push(s),A=w,w=ue({inputs:{x:w},attrs:{shape:R},backend:t}),Nc(t,A);let P=D;return D=ue({inputs:{x:D},attrs:{shape:R},backend:t}),Nc(t,P),[D,w]}var y3={kernelName:wa,backendName:"webgl",kernelFunc:_re};var kS=class{constructor(e,t,n,o,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,l;switch(o){case"constant":l=1;break;case"reflect":l=2;break;case"wrap":l=3;break;case"nearest":l=4;break;default:l=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${l} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${l} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${l} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${s});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${s});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${i} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function kre(r){let{inputs:e,backend:t,attrs:n}=r,{image:o,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:l,outputShape:u}=n,[c,p,m,f]=o.shape,[d,h]=u!=null?u:[p,m],g=[c,d,h,f],x=new kS(p,m,a,i,l,g);return t.runWebGLProgram(x,[o,s],"float32")}var b3={kernelName:_a,backendName:"webgl",kernelFunc:kre};function vre(r){let{inputs:e,attrs:t,backend:n}=r,{axis:o}=t,{x:s}=e;qs(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=n.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=RP(a,o,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,i),n.makeTensorInfo([u.length],"int32",u)]}var w3={kernelName:vp,backendName:"webgl",kernelFunc:vre};function Cre(r){let{inputs:e,backend:t,attrs:n}=r,{value:o}=e,{axis:s}=n;s<0&&(s+=o.shape.length);let a=o,i=a.shape.length,l=o.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let p=[],m=new Array(i).fill(0),f=a.shape.slice();f[s]=1;let d=new Array(l);for(let h=0;h<d.length;h++){m[s]=h;let g=Zs({inputs:{x:a},backend:t,attrs:{begin:m,size:f}}),x=ue({inputs:{x:g},backend:t,attrs:{shape:u}});d[h]=x,p.push(g)}return p.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var _3={kernelName:di,backendName:"webgl",kernelFunc:Cre};var vS=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,o=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/n);this.outputShape=[o,i];let l="0.0",u="sumValue",c=Math.floor(n/4)*4,p=n%4,m=`
sumValue += dot(values, segFilter);
`,f="";s%n>0&&(f=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`);let d="";s%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${l};
float getValue(int batch, int inIdx) {
${f}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${d}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${m}
}
int inIdx = inOffset + ${c};
if (${p===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${m}
} else if (${p===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${m}
} else if (${p===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${m}
}
setOutput(${u});
}
`}};function Ire(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,segmentIds:s}=e,{numSegments:a}=n,i=o.shape.length,l=[],u=0,c=I.getAxesPermutation([u],i),p=o;c!=null&&(p=Pt({inputs:{x:o},backend:t,attrs:{perm:c}}),l.push(p),u=I.getInnerMostAxes(1,i)[0]);let m=I.segment_util.computeOutShape(p.shape,u,a),f=b.sizeFromShape([p.shape[u]]),d=ue({inputs:{x:p},backend:t,attrs:{shape:[-1,f]}});l.push(d);let h=hu(o.dtype),g=(_,C,A,D,R)=>{let P=_.shape[0],L=_.shape[1],G=I.segment_util.segOpComputeOptimalWindowSize(L,R),W={windowSize:G,inSize:L,batchSize:P,numSegments:R},j=new vS(W,C),H=t.compileAndRun(j,[_,A],D);if(l.push(H),H.shape[1]===R)return H;let q=iS({backend:t,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),X=bS({inputs:{x:q},backend:t,attrs:{reps:[L/G]}});return l.push(q),l.push(X),g(H,C,X,D,R)},x=g(d,"unsortedSegmentSum",s,h,a),y=ue({inputs:{x},backend:t,attrs:{shape:m}}),w=y;if(c!=null){l.push(y);let _=I.getUndoAxesPermutation(c);w=Pt({inputs:{x:w},backend:t,attrs:{perm:_}})}return l.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var k3={kernelName:vl,backendName:"webgl",kernelFunc:Ire};var Sre=[XL,YL,uM,pM,mM,fM,hM,gM,xM,yM,_M,kM,vM,CM,SM,IM,NM,EM,TM,AM,DM,$M,RM,OM,PM,MM,VM,WM,UM,HM,ZP,KM,YM,ZM,XM,QM,eL,JM,tL,rL,nL,iL,aL,lL,cL,pL,uL,mL,fL,dL,hL,gL,xL,yL,wL,_L,vL,CL,IL,SL,TL,EL,AL,DL,$L,RL,FL,OL,PL,YP,ML,qM,LL,zL,BL,JP,VL,GL,WL,jL,UL,HL,qL,KL,JL,tz,ez,rz,nz,sz,QL,az,lz,uz,cz,pz,gz,nM,yz,bz,wz,_z,LM,kz,Iz,Sz,Nz,Tz,QP,Ez,Az,zM,mz,Dz,Rz,$z,sM,Fz,Oz,Pz,Mz,Lz,zz,Bz,Vz,Gz,Wz,Uz,Hz,qz,Kz,Xz,FM,hz,Yz,Zz,Jz,Qz,e3,t3,r3,n3,s3,i3,l3,u3,c3,p3,m3,f3,dz,aM,d3,h3,g3,y3,b3,lM,w3,_3,k3,vz];for(let r of Sre)uu(r);var et;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(et||(et={}));var Zl;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu",r[r.sigmoid=5]="sigmoid",r[r.elu=6]="elu"})(Zl||(Zl={}));var v3;function Nre(r){v3=r.wasm.cwrap(gi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Tre(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=n,m=t.dataIdMap.get(o.dataId).id,f=t.dataIdMap.get(s.dataId).id,d=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);d=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=Zl[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=l?o.shape[2]:o.shape[1],y=u?s.shape[1]:s.shape[2],w=o.shape[0],_=t.makeOutput([w,x,y],o.dtype),C=t.dataIdMap.get(_.dataId).id,A=new Uint8Array(new Int32Array(o.shape).buffer),D=new 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Gre(r){let{inputs:e,attrs:t,backend:n}=r,o=e.x,s=n.dataIdMap.get(o.dataId).id,{filterSize:a,strides:i,pad:l,dimRoundingMode:u}=t,c=I.computePool2DInfo(o.shape,a,i,1,l,u),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.strideHeight,y=c.strideWidth,w=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let _=n.makeOutput(c.outShape,"float32"),C=n.dataIdMap.get(_.dataId).id;return L3(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,y,w,C),_}var z3={kernelName:Fo,backendName:"wasm",setupFunc:Vre,kernelFunc:Gre};function ar(r){let{inputs:e,attrs:t}=r,{x:n}=e,{shape:o}=t,s=b.sizeFromShape(n.shape),a=b.inferFromImplicitShape(o,s);return b.assert(s===b.sizeFromShape(a),()=>`new shape: ${a}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(n.dataId),{dataId:n.dataId,shape:a,dtype:n.dtype}}var B3={kernelName:ui,backendName:"wasm",kernelFunc:ar};var V3;function Wre(r){V3=r.wasm.cwrap(Oo,null,["number","array","number","number","array","number","number","number","number"])}function Ure(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s}=e,{transposeA:a,transposeB:i}=n;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=o.shape.length,u=s.shape.length,c=a?o.shape[l-2]:o.shape[l-1],p=i?s.shape[u-1]:s.shape[u-2],m=a?o.shape[l-1]:o.shape[l-2],f=i?s.shape[u-2]:s.shape[u-1],d=o.shape.slice(0,-2),h=s.shape.slice(0,-2),g=b.sizeFromShape(d),x=b.sizeFromShape(h),y=g===x||g===1||x===1;b.assert(l>=2&&u>=2&&y,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${d}) and (${h}).`);let _=(g>x?o.shape.slice(0,-2):s.shape.slice(0,-2)).concat([m,f]);b.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${o.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`);let C=a?[g,c,m]:[g,m,c],A=i?[x,f,p]:[x,p,f],D=ar({inputs:{x:o},backend:t,attrs:{shape:C}}),R=ar({inputs:{x:s},backend:t,attrs:{shape:A}}),P=t.dataIdMap.get(D.dataId).id,L=t.dataIdMap.get(R.dataId).id,G=a?D.shape[2]:D.shape[1],W=i?R.shape[1]:R.shape[2],j=Math.max(g,x),H=t.makeOutput([j,G,W],D.dtype),q=t.dataIdMap.get(H.dataId).id,X=new Uint8Array(new Int32Array(D.shape).buffer),re=new Uint8Array(new Int32Array(R.shape).buffer);return V3(P,X,D.shape.length,L,re,R.shape.length,a,i,q),t.disposeData(D.dataId),t.disposeData(R.dataId),H.shape=_,H}var G3={kernelName:Oo,backendName:"wasm",setupFunc:Wre,kernelFunc:Ure};function nl(r){let{inputs:{x:e},attrs:{begin:t,size:n},backend:o}=r,[s,a]=pr.parseSliceParams(e,t,n),i=pr.isSliceContinous(e.shape,s,a),l=o.readSync(e.dataId),u=o.makeOutput(a,e.dtype),c=b.computeStrides(e.shape),p=o.dataIdMap.get(u.dataId);if(i){let d=pr.computeFlatOffset(s,c);return e.dtype==="string"?p.stringBytes=l.slice(d,d+b.sizeFromShape(a)):o.typedArrayFromHeap(u).set(l.subarray(d,d+b.sizeFromShape(a))),u}if(e.dtype==="string"){let d=hc(l,s,a,e.shape,e.dtype);return p.stringBytes=d,u}let m=o.typedArrayFromHeap(u),f=e.shape.length;if(f===2)jre(l,c[0],m,s,a);else if(f===3)Hre(l,c[0],c[1],m,s,a);else if(f===4)qre(l,c[0],c[1],c[2],m,s,a);else{let d=hc(l,s,a,e.shape,e.dtype);m.set(d)}return u}function jre(r,e,t,n,o){let s=0,a=n[0],i=n[1],l=a+o[0];for(let u=a;u<l;u++){let c=u*e+i;t.set(r.subarray(c,c+o[1]),s),s+=o[1]}}function Hre(r,e,t,n,o,s){let a=0,i=o[0],l=o[1],u=o[2],c=i+s[0],p=l+s[1];for(let m=i;m<c;m++)for(let f=l;f<p;f++){let d=m*e+f*t+u;n.set(r.subarray(d,d+s[2]),a),a+=s[2]}}function qre(r,e,t,n,o,s,a){let i=0,l=s[0],u=s[1],c=s[2],p=l+a[0],m=u+a[1],f=c+a[2],d=s[3];for(let h=l;h<p;h++)for(let g=u;g<m;g++)for(let x=c;x<f;x++){let y=h*e+g*t+x*n+d;o.set(r.subarray(y,y+a[3]),i),i+=a[3]}}var W3={kernelName:pi,backendName:"wasm",kernelFunc:nl};function Kre(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n,i=s.reduce((x,y)=>x*y),l=I.getReshaped(o.shape,s,i),u=I.getPermuted(l.length,s.length),c=I.getReshapedPermuted(o.shape,s,i),p=I.getSliceBeginCoords(a,s.length),m=I.getSliceSize(c,a,s.length),f=ar({inputs:{x:o},backend:t,attrs:{shape:l}}),d=$i({inputs:{x:f},backend:t,attrs:{perm:u}}),h=ar({inputs:{x:d},backend:t,attrs:{shape:c}}),g=nl({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeData(f.dataId),t.disposeData(d.dataId),t.disposeData(f.dataId),g}var U3={kernelName:ri,backendName:"wasm",kernelFunc:Kre};function ol(r){let{inputs:{x:e},attrs:{dtype:t},backend:n}=r,o=n.makeOutput(e.shape,t),s=n.typedArrayFromHeap(e);return n.typedArrayFromHeap(o).set(s),o}var j3={kernelName:eo,backendName:"wasm",kernelFunc:ol};var H3=lt(Po);var q3;function Xre(r){q3=r.wasm.cwrap(to,null,["number","number","number","number"])}function Yre(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{clipValueMin:s,clipValueMax:a}=n,i=t.dataIdMap.get(o.dataId).id,l=t.makeOutput(o.shape,o.dtype),u=t.dataIdMap.get(l.dataId).id;return q3(i,s,a,u),l}var K3={kernelName:to,backendName:"wasm",setupFunc:Xre,kernelFunc:Yre};function CS(r){let{inputs:e,backend:t}=r,n=b.parseAxisParam(r.attrs.axis,e[0].shape)[0],o=I.computeOutShape(e.map(f=>f.shape),n),s=e.filter(f=>b.sizeFromShape(f.shape)>0);if(s.length===1)return Tc({inputs:{x:s[0]},backend:t});let a=t.makeOutput(o,e[0].dtype);if(b.sizeFromShape(o)===0)return a;let i=s.map(f=>f.shape);if(I.assertParamsConsistent(i,n),s[0].dtype==="string"){let f=s.map(w=>{let _=b.sizeFromShape(w.shape.slice(n));return ar({inputs:{x:w},backend:t,attrs:{shape:[-1,_]}})}),d=f.map(w=>({vals:t.readSync(w.dataId),shape:w.shape}));o=I.computeOutShape(f.map(w=>w.shape),1);let h=f[0].shape[0]===1,g=mc(d,o,e[0].dtype,h),x=I.computeOutShape(s.map(w=>w.shape),n);a.shape=x;let y=t.dataIdMap.get(a.dataId);return y.stringBytes=I.fromStringArrayToUint8(g),f.forEach(w=>t.disposeData(w.dataId)),a}let l=b.sizeFromShape(s[0].shape.slice(0,n)),u=0,c=s.map(f=>{let d=b.sizeFromShape(f.shape.slice(n));return u+=d,d}),p=s.map(f=>t.typedArrayFromHeap(f)),m=t.typedArrayFromHeap(a);for(let f=0;f<l;f++){let d=f*u;for(let h=0;h<p.length;h++){let g=c[h],x=f*g,y=p[h].subarray(x,x+g);m.set(y,d),d+=g}}return a}var X3={kernelName:ni,backendName:"wasm",kernelFunc:CS};var Y3;function Zre(r){Y3=r.wasm.cwrap(Mo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jre(r){let{inputs:e,attrs:t,backend:n}=r,{x:o,filter:s}=e,a=n.dataIdMap.get(o.dataId).id,i=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p,dataFormat:m}=t,f=I.convertConv2DDataFormat(m),d=I.computeConv2DInfo(o.shape,s.shape,l,u,c,p,!1,f),h=d.filterHeight,g=d.filterWidth,x=d.padInfo.top,y=d.padInfo.right,w=d.padInfo.bottom,_=d.padInfo.left,C=d.dilationHeight,A=d.dilationWidth,D=d.strideHeight,R=d.strideWidth,P=d.inChannels,L=d.outChannels,G=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let W=n.makeOutput(d.outShape,"float32"),j=n.dataIdMap.get(W.dataId).id;return Y3(a,o.shape[0],o.shape[1],o.shape[2],i,h,g,x,y,w,_,G,C,A,D,R,P,L,j),W}var Z3={kernelName:Mo,backendName:"wasm",setupFunc:Zre,kernelFunc:Jre};var J3;function Qre(r){J3=r.wasm.cwrap(Lo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ene(r){let{backend:e,inputs:t,attrs:n}=r,{dy:o,filter:s}=t,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,inputShape:c}=n,p=1,m=I.convertConv2DDataFormat(l),f=I.computeConv2DInfo(c,s.shape,a,p,i,u,!1,m),{batchSize:d,filterHeight:h,filterWidth:g,inChannels:x,inHeight:y,inWidth:w,outChannels:_,outHeight:C,outWidth:A,strideHeight:D,strideWidth:R}=f,P=h-1-f.padInfo.top,L=g-1-f.padInfo.left,G=f.dataFormat==="channelsLast",W=b.computeStrides(f.inShape),j=b.computeStrides(o.shape),[H,q,X]=b.computeStrides(s.shape),re=W[0],J=G?W[1]:W[2],oe=G?W[2]:1,se=G?1:W[1],ne=j[0],fe=G?j[1]:j[2],ae=G?j[2]:1,ge=G?1:j[1],de=e.makeOutput(f.inShape,"float32"),ye=e.dataIdMap.get(de.dataId).id,_e=e.dataIdMap.get(o.dataId).id,Re=e.dataIdMap.get(s.dataId).id;return J3(_e,Re,d,h,g,y,w,x,C,A,_,D,R,P,L,H,q,X,re,J,oe,se,ne,fe,ae,ge,ye),de}var Q3={kernelName:Lo,backendName:"wasm",setupFunc:Qre,kernelFunc:ene};var eB=lt(zo);var tB=lt(Bo);var IS;(function(r){r[r.bilinear=0]="bilinear",r[r.nearest=1]="nearest"})(IS||(IS={}));var rB;function tne(r){rB=r.wasm.cwrap(Hi,null,["number","number","number","number","array","number","number","number","number","number"])}function rne(r){let{backend:e,inputs:t,attrs:n}=r,{method:o,extrapolationValue:s,cropSize:a}=n,{image:i,boxes:l,boxInd:u}=t,c=l.shape[0],[p,m]=a,f=[c,p,m,i.shape[3]],d=e.dataIdMap.get(i.dataId),h;i.dtype!=="float32"&&(h=ol({backend:e,inputs:{x:i},attrs:{dtype:"float32"}}),d=e.dataIdMap.get(h.dataId));let g=d.id,x=e.dataIdMap.get(l.dataId).id,y=e.dataIdMap.get(u.dataId).id,w=e.makeOutput(f,"float32"),_=e.dataIdMap.get(w.dataId).id,C=new Uint8Array(new Int32Array(i.shape).buffer);return rB(g,x,y,c,C,p,m,IS[o],s,_),h!=null&&e.disposeData(h.dataId),w}var nB={kernelName:Hi,backendName:"wasm",setupFunc:tne,kernelFunc:rne};var oB;function nne(r){oB=r.wasm.cwrap(Vo,null,["number","number","number","number","number","number"])}function one(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n,l=o.shape.length;b.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let u=I.getAxesPermutation([s],l),c=o;u!==null&&(c=$i({inputs:{x:o},attrs:{perm:u},backend:t}));let p=I.getInnerMostAxes(1,l)[0];I.assertAxesAreInnerMostDims("cumsum",[p],l);let m=t.makeOutput(c.shape,c.dtype),f=c.shape[p],d=t.dataIdMap.get(c.dataId).id,h=t.dataIdMap.get(m.dataId).id;oB(d,a?1:0,i?1:0,f,h,et[o.dtype]);let g=m;if(u!==null){let x=I.getUndoAxesPermutation(u);g=$i({inputs:{x:m},attrs:{perm:x},backend:t}),t.disposeData(c.dataId),t.disposeData(m.dataId)}return g}var sB={kernelName:Vo,backendName:"wasm",setupFunc:nne,kernelFunc:one};var iB;function sne(r){iB=r.wasm.cwrap(qi,null,["number","number","number","array","number","array","array","number","number"])}function ine(r){let{backend:e,inputs:t,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:a}=n,i=o.shape[0],l=a==="NHWC"?o.shape[1]:o.shape[2],u=a==="NHWC"?o.shape[2]:o.shape[3],c=a==="NHWC"?o.shape[3]:o.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=e.makeOutput(d,"float32"),x=e.dataIdMap.get(o.dataId).id,y=new Uint8Array(new Int32Array(b.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),_=new Uint8Array(new Int32Array(b.computeStrides(d)).buffer),C=e.dataIdMap.get(h.dataId).id;return iB(x,s,a==="NHWC"?1:0,y,o.shape.length-1,w,_,d.length,C),h}var aB={kernelName:qi,backendName:"wasm",setupFunc:sne,kernelFunc:ine};var lB;function ane(r){lB=r.wasm.cwrap(Go,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lne(r){let{inputs:e,attrs:t,backend:n}=r,{x:o,filter:s}=e,a=n.dataIdMap.get(o.dataId).id,i=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=t,m=u==null?[1,1]:u,f=I.computeConv2DInfo(o.shape,s.shape,l,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,y=f.padInfo.bottom,w=f.padInfo.left,_=f.dilationHeight,C=f.dilationWidth,A=f.strideHeight,D=f.strideWidth,R=f.inChannels,P=f.outChannels,L=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let G=n.makeOutput(f.outShape,"float32"),W=n.dataIdMap.get(G.dataId).id;return lB(a,o.shape[0],o.shape[1],o.shape[2],i,d,h,g,x,y,w,L,_,C,A,D,R,P,W),G}var uB={kernelName:Go,backendName:"wasm",setupFunc:ane,kernelFunc:lne};var cB=lt(Uo);var une=!1,pB=kt(Xi,une,"bool");var mB=lt(jo,"float32");function fb(r){let{inputs:e,attrs:t,backend:n}=r,{input:o}=e,{dim:s}=t,a=o.shape.length,i=o.shape.slice(),l=s;return s<0&&(b.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+s+1),i.splice(l,0,1),ar({inputs:{x:o},backend:n,attrs:{shape:i}})}var fB={kernelName:oi,backendName:"wasm",kernelFunc:fb};function SS(r){let{attrs:{shape:e,value:t,dtype:n},backend:o}=r,s=o.makeOutput(e,n);return o.typedArrayFromHeap(s).fill(t),s}var dB={kernelName:xl,backendName:"wasm",kernelFunc:SS};var hB;function cne(r){hB=r.wasm.cwrap(Zi,null,["number","number","number","number","number","number"])}function pne(r){let{inputs:e,backend:t}=r,{image:n}=e,o=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(n.dataId).id,a=t.dataIdMap.get(o.dataId).id,[i,l,u,c]=n.shape;return hB(s,i,l,u,c,a),o}var gB={kernelName:Zi,backendName:"wasm",kernelFunc:pne,setupFunc:cne};var xB=lt(Ho);var mne=!1,yB=kt(qo,mne);var bB;function fne(r){bB=r.wasm.cwrap(Ko,null,["number","number","number","number","number","number","number"])}function dne(r){let{backend:e,inputs:t,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:a,variance:i,offset:l,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,p=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,f=l!=null?e.dataIdMap.get(l.dataId).id:0,d=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(b.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return bB(c,p,m,f,d,o,g),h}var wB={kernelName:Ko,backendName:"wasm",setupFunc:fne,kernelFunc:dne};var _B;function hne(r){_B=r.wasm.cwrap(xi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gne(r){let{inputs:e,attrs:t,backend:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=t,h=I.computeConv2DInfo(o.shape,s.shape,l,c,u,m),g=Zl[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,y=n.dataIdMap.get(s.dataId).id,w=h.outChannels,_=0;if(a!=null){let ae=n.dataIdMap.get(a.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${w})`);_=ae.id}let C=h.filterHeight,A=h.filterWidth,D=h.padInfo.top,R=h.padInfo.right,P=h.padInfo.bottom,L=h.padInfo.left,G=h.dilationHeight,W=h.dilationWidth,j=h.strideHeight,H=h.strideWidth,q=h.inChannels,X=h.padInfo.type==="SAME"?1:0,re=h.batchSize,J=h.inHeight,oe=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=n.makeOutput(h.outShape,"float32"),ne=n.dataIdMap.get(se.dataId).id,fe=i==null?0:n.dataIdMap.get(i.dataId).id;return _B(x,re,J,oe,y,C,A,_,D,R,P,L,X,G,W,j,H,q,w,g,fe,d||0,ne),se}var kB={kernelName:xi,backendName:"wasm",setupFunc:hne,kernelFunc:gne};var vB;function xne(r){vB=r.wasm.cwrap(yi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yne(r){let{inputs:e,attrs:t,backend:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=t,h=I.computeConv2DInfo(o.shape,s.shape,l,c,u,m,!0),g=Zl[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,y=n.dataIdMap.get(s.dataId).id,w=h.outChannels,_=0;if(a!=null){let ae=n.dataIdMap.get(a.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${w})`);_=ae.id}let C=h.filterHeight,A=h.filterWidth,D=h.padInfo.top,R=h.padInfo.right,P=h.padInfo.bottom,L=h.padInfo.left,G=h.dilationHeight,W=h.dilationWidth,j=h.strideHeight,H=h.strideWidth,q=h.inChannels,X=h.padInfo.type==="SAME"?1:0,re=h.batchSize,J=h.inHeight,oe=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=n.makeOutput(h.outShape,"float32"),ne=n.dataIdMap.get(se.dataId).id,fe=i==null?0:n.dataIdMap.get(i.dataId).id;return vB(x,re,J,oe,y,C,A,_,D,R,P,L,X,G,W,j,H,q,w,g,fe,d||0,ne),se}var CB={kernelName:yi,backendName:"wasm",setupFunc:xne,kernelFunc:yne};var IB;function bne(r){IB=r.wasm.cwrap(Ji,null,["number","number","number","number","number","number","array","number"])}function wne(r){let{backend:e,inputs:t}=r,{params:n,indices:o}=t,[s,a,i,l]=Wg.prepareAndValidate(n,o),u=e.makeOutput(s,n.dtype);if(a===0)return u;let c=o.shape,p=c[c.length-1],f=e.dataIdMap.get(n.dataId).id,h=e.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),x=e.dataIdMap.get(u.dataId).id;return IB(f,et[n.dtype],h,a,p,i,g,x),u}var SB={kernelName:Ji,backendName:"wasm",setupFunc:bne,kernelFunc:wne};var NB;function _ne(r){NB=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function kne(r){let{backend:e,inputs:t,attrs:n}=r,{x:o,indices:s}=t,{axis:a,batchDims:i}=n,l=b.parseAxisParam(a,o.shape)[0],u=e.readSync(s.dataId),c=o.shape[l];for(let P=0;P<u.length;++P){let L=u[P];b.assert(L<=c-1&&L>=0,()=>`GatherV2: the index value ${L} is not in [0, ${c-1}]`)}let p=I.segment_util.collectGatherOpShapeInfo(o,s,l,i),m=ar({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:e}),f=b.sizeFromShape(s.shape),d=ar({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:e}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=e.makeOutput(h,o.dtype);if(b.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=e.dataIdMap.get(m.dataId).id,C=e.dataIdMap.get(d.dataId).id,A=e.dataIdMap.get(g.dataId).id,D=new Uint8Array(new Int32Array(b.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(b.computeStrides(h)).buffer);return NB(w,et[o.dtype],D,x,C,p.batchSize,R,A),e.disposeData(m.dataId),e.disposeData(d.dataId),g.shape=p.outputShape,g}var TB={kernelName:si,backendName:"wasm",setupFunc:_ne,kernelFunc:kne};var vne=!1,EB=kt(Qi,vne,"bool");var Cne=!1,AB=kt(Xo,Cne,"bool");var DB;function Ine(r){DB=r.wasm.cwrap(Yo,null,["number","number","number","number"])}function Sne(r){let{inputs:{x:e},attrs:{alpha:t},backend:n}=r,o=n.dataIdMap.get(e.dataId).id,s=n.makeOutput(e.shape,"float32");if(b.sizeFromShape(e.shape)!==0){let a=n.dataIdMap.get(s.dataId).id;DB(o,et[e.dtype],t,a)}return s}var $B={kernelName:Yo,backendName:"wasm",setupFunc:Ine,kernelFunc:Sne};var Nne=!1,RB=kt(na,Nne,"bool");var Tne=!1,FB=kt(oa,Tne,"bool");var OB=lt(Zo);var Ene=!1,PB=kt(ia,Ene,"bool");var MB;function Ane(r){MB=r.wasm.cwrap(Jo,null,["number","number","number","number"])}function Dne(r){let{backend:e,inputs:t,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:a}=t,l=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=hn(a,o,e);if(f){let w=e.dataIdMap.get(c.dataId).id;u=c,l=w}let d=u.shape.length;I.assertAxesAreInnerMostDims("max",p,d);let[h,g]=I.computeOutAndReduceShapes(u.shape,p),x=b.sizeFromShape(g),y=e.makeOutput(h,a.dtype);if(b.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(y.dataId).id;MB(l,et[a.dtype],x,w)}if(f&&e.disposeData(c.dataId),s){let w=I.expandShapeToKeepDim(y.shape,m);y.shape=w}return y}var LB={kernelName:Jo,backendName:"wasm",setupFunc:Ane,kernelFunc:Dne};var $ne=!1,zB=kt(Qo,$ne);var BB;function Rne(r){BB=r.wasm.cwrap(es,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Fne(r){let{inputs:e,attrs:t,backend:n}=r,o=e.x,s=n.dataIdMap.get(o.dataId).id;b.assert(o.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:a,strides:i,pad:l,dimRoundingMode:u}=t,c=I.computePool2DInfo(o.shape,a,i,1,l,u),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,y=c.dilationWidth,w=c.strideHeight,_=c.strideWidth,C=c.inChannels,A=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let D=n.makeOutput(c.outShape,"float32"),R=n.dataIdMap.get(D.dataId).id;return BB(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,y,w,_,C,A,R),D}var VB={kernelName:es,backendName:"wasm",setupFunc:Rne,kernelFunc:Fne};var GB;function One(r){GB=r.wasm.cwrap(ts,null,["number, number, number"])}function Pne(r){let{backend:e,inputs:t,attrs:n}=r,{axis:o,keepDims:s}=n,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,l=i,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=hn(a,o,e),d=p;if(f){let _=e.dataIdMap.get(c.dataId).id;_!==i&&(u=c,l=_,d=I.getInnerMostAxes(d.length,u.shape.length))}I.assertAxesAreInnerMostDims("mean",d,u.shape.length);let[h,g]=I.computeOutAndReduceShapes(u.shape,d),x=b.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=ol({backend:e,inputs:{x:u},attrs:{dtype:"float32"}}),l=e.dataIdMap.get(y.dataId).id);let w=e.makeOutput(h,"float32");if(b.sizeFromShape(u.shape)!==0){let _=e.dataIdMap.get(w.dataId).id;GB(l,x,_)}if(f&&e.disposeData(c.dataId),s){let _=I.expandShapeToKeepDim(w.shape,m);w.shape=_}return u.dtype!=="float32"&&e.disposeData(y.dataId),w}var WB={kernelName:ts,backendName:"wasm",setupFunc:One,kernelFunc:Pne};var UB;function Mne(r){UB=r.wasm.cwrap(rs,null,["number","number","number","number"])}function Lne(r){let{backend:e,inputs:t,attrs:n}=r,{axis:o,keepDims:s}=n,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,l=i,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=hn(a,o,e);if(f){let w=e.dataIdMap.get(c.dataId).id;w!==i&&(u=c,l=w)}let d=u.shape.length;I.assertAxesAreInnerMostDims("min",p,d);let[h,g]=I.computeOutAndReduceShapes(u.shape,p),x=b.sizeFromShape(g),y=e.makeOutput(h,u.dtype);if(b.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(y.dataId).id;UB(l,et[a.dtype],x,w)}if(f&&e.disposeData(c.dataId),s){let w=I.expandShapeToKeepDim(y.shape,m);y.shape=w}return y}var jB={kernelName:rs,backendName:"wasm",setupFunc:Mne,kernelFunc:Lne};var zne=!1,HB=kt(ns,zne);var NS;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(NS||(NS={}));var qB;function Bne(r){qB=r.wasm.cwrap(os,null,["number","array","number","number","array","array","number","number"])}function Vne(r){let{inputs:{x:e},backend:t,attrs:{paddings:n,mode:o}}=r,s=n.map((d,h)=>d[0]+e.shape[h]+d[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),l=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),c=n.map(d=>d[0]),p=n.map(d=>d[1]),m=new Uint8Array(new Int32Array(c).buffer),f=new Uint8Array(new Int32Array(p).buffer);return qB(a,u,e.shape.length,et[e.dtype],m,f,NS[o],l),i}var KB={kernelName:os,backendName:"wasm",kernelFunc:Vne,setupFunc:Bne};var Gne=!0,XB=kt(ss,Gne);var YB=lt(ii);function Mm(r,e){let t=new Int32Array(r.wasm.HEAPU8.buffer,e,4),n=t[0],o=t[1],s=t[2],a=t[3];return r.wasm._free(e),{pSelectedIndices:n,selectedSize:o,pSelectedScores:s,pValidOutputs:a}}var ZB;function 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x=e.makeOutput([d],"int32",f),y=e.makeOutput([],"int32",g);return[x,y]}var eV={kernelName:ca,backendName:"wasm",setupFunc:jne,kernelFunc:Hne};var tV;function qne(r){tV=r.wasm.cwrap(pa,"number",["number","number","number","number","number","number"])}function Kne(r){let{backend:e,inputs:t,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:a,softNmsSigma:i}=n,{boxes:l,scores:u}=t,c=e.dataIdMap.get(l.dataId).id,p=e.dataIdMap.get(u.dataId).id,m=tV(c,p,s,o,a,i),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Mm(e,m);e.wasm._free(g);let x=e.makeOutput([d],"int32",f),y=e.makeOutput([d],"float32",h);return[x,y]}var rV={kernelName:pa,backendName:"wasm",setupFunc:qne,kernelFunc:Kne};var Xne=!1,nV=kt(la,Xne,"bool");var oV;function Yne(r){oV=r.wasm.cwrap(is,null,["number","number","number","number","number"])}function 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aV={kernelName:li,backendName:"wasm",kernelFunc:Qne};var lV;function eoe(r){lV=r.wasm.cwrap(as,null,["number","array","number","number","array","array","number","number"])}function toe(r){let{inputs:{x:e},backend:t,attrs:{paddings:n,constantValue:o}}=r,s=n.map((h,g)=>h[0]+e.shape[g]+h[1]);if(b.sizeFromShape(e.shape)===0)return SS({backend:t,attrs:{shape:s,value:o,dtype:e.dtype}});let a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(e.shape).buffer),p=n.map(h=>h[0]),m=n.map(h=>h[1]),f=new Uint8Array(new Int32Array(p).buffer),d=new Uint8Array(new Int32Array(m).buffer);return lV(a,c,e.shape.length,et[e.dtype],f,d,o,u),i}var db={kernelName:as,backendName:"wasm",kernelFunc:toe,setupFunc:eoe};var roe=!1,uV=kt(ls,roe);var cV;function noe(r){cV=r.wasm.cwrap(us,null,["number","number","number"])}function 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coe(r){let{backend:e,inputs:t,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,[c,p,m,f]=o.shape,d=[c,l,u,f],h=e.dataIdMap.get(o.dataId),g;h.dtype!=="float32"&&(g=ol({backend:e,inputs:{x:o},attrs:{dtype:"float32"}}),h=e.dataIdMap.get(g.dataId));let x=h.id,y=e.makeOutput(d,"float32");if(b.sizeFromShape(o.shape)===0)return y;let w=e.dataIdMap.get(y.dataId).id;return yV(x,c,p,m,f,l,u,s?1:0,a?1:0,w),g!=null&&e.disposeData(g.dataId),y}var bV={kernelName:ps,backendName:"wasm",setupFunc:uoe,kernelFunc:coe};var wV;function poe(r){wV=r.wasm.cwrap(fs,null,["number","array","number","array","number","number"])}function moe(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dims:s}=n,a=b.parseAxisParam(s,o.shape);if(o.shape.length===0)return Tc({inputs:{x:o},backend:t});let i=t.makeOutput(o.shape,o.dtype),l=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),p=new Uint8Array(new 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AV;function boe(r){AV=r.wasm.cwrap(xs,null,["number","number"])}function woe(r){let{backend:e,inputs:{x:t}}=r,n=e.dataIdMap.get(t.dataId).id,o=e.makeOutput(t.shape,t.dtype),s=e.dataIdMap.get(o.dataId).id;return b.sizeFromShape(o.shape)===0||AV(n,s),o}var DV={kernelName:"Sigmoid",backendName:"wasm",setupFunc:boe,kernelFunc:woe};var $V=lt(gs);var RV;function _oe(r){RV=r.wasm.cwrap(ws,null,["number","number","number","number"])}function koe(r){let{backend:e,inputs:{logits:t},attrs:{dim:n}}=r,o=e.dataIdMap.get(t.dataId).id,s=e.makeOutput(t.shape,t.dtype),a=e.dataIdMap.get(s.dataId).id,i=t.shape[n],l=b.sizeFromShape(t.shape)/i;return b.sizeFromShape(s.shape)===0||RV(o,a,i,l),s}var FV={kernelName:ws,backendName:"wasm",setupFunc:_oe,kernelFunc:koe};function voe(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,paddings:a}=n,i=b.sizeFromShape(s),l=[[0,0]];l.push(...a);for(let A=1+s.length;A<o.shape.length;++A)l.push([0,0]);let 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Noe(r){let{backend:e,inputs:t,attrs:n}=r,{alpha:o}=n,{x:s}=t,a=e.dataIdMap.get(s.dataId).id,i=e.makeOutput(s.shape,s.dtype),l=e.dataIdMap.get(i.dataId).id;return BV(a,o,et[s.dtype],l),i}var VV={kernelName:no,backendName:"wasm",setupFunc:Soe,kernelFunc:Noe};var GV;function Toe(r){GV=r.wasm.cwrap(ba,null,["number","array","number","array","array","array","array","array","number","number"])}function Eoe(r){let{backend:e,inputs:t,attrs:n}=r,{x:o}=t,{begin:s,end:a,strides:i}=n;i==null&&(i=new Array(s.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,f=I.slice_util.maskToAxes(c);if(f.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&m!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let d=o.shape.length-s.length,h=I.slice_util.maskToAxes(p),g=o.shape.slice();h.forEach(G=>{s[G]=0,a[G]=1,g.splice(G,0,1)});let x=ar({inputs:{x:o},attrs:{shape:g},backend:e}),{begin:y,end:w,strides:_}=I.slice_util.getNormalizedAxes(x.shape,f,d,s,a,i,l,u,c);s=y,a=w,i=_;let C=I.slice_util.maskToAxes(m);C.forEach(G=>{a[G]=s[G]+1,i[G]=1});let A=I.slice_util.computeOutShape(s,a,i),D=A.filter((G,W)=>C.indexOf(W)===-1);if(i.every(G=>G===1)){let G=nl({inputs:{x},attrs:{begin:s,size:A},backend:e});e.disposeData(x.dataId);let W=ar({inputs:{x:G},attrs:{shape:D},backend:e});return e.disposeData(G.dataId),W}let P=e.makeOutput(D,"float32");if(!D.some(G=>G===0)){let G=e.dataIdMap.get(x.dataId).id,W=new Uint8Array(new Int32Array(b.computeStrides(x.shape)).buffer),j=new Uint8Array(new Int32Array(s).buffer),H=new Uint8Array(new Int32Array(a).buffer),q=new Uint8Array(new Int32Array(i).buffer),X=new Uint8Array(new Int32Array(D).buffer),re=new Uint8Array(new 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r&&e?n="tfjs-backend-wasm-threaded-simd.wasm":r&&(n="tfjs-backend-wasm-simd.wasm"),eg!=null&&eg[n]!=null?eg[n]:t+n}async function cG(){let[r,e]=await Promise.all([U().getAsync("WASM_HAS_SIMD_SUPPORT"),U().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((t,n)=>{let o={};o.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let u=iG,c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return i.endsWith(".wasm")?uG(r,e,Qh!=null?Qh:l):l+i},FS&&(o.instantiateWasm=Goe(uG(r,e,Qh!=null?Qh:"")));let s=!1;o.onAbort=()=>{if(s||tg)return;tg=!0,n({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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BatchMatMul,ri as BatchToSpaceND,jc as Bincount,Hc as BroadcastArgs,rN as BroadcastTo,Sv as Callback,Lk as CallbackList,eo as Cast,Po as Ceil,to as ClipByValue,qc as Complex,dl as ComplexAbs,ni as Concat,Mo as Conv2D,Kc as Conv2DBackpropFilter,Lo as Conv2DBackpropInput,hl as Conv3D,Xc as Conv3DBackpropFilterV2,Yc as Conv3DBackpropInputV2,zo as Cos,Bo as Cosh,Hi as CropAndResize,Vo as Cumsum,Bk as CustomCallback,pl as DataStorage,Zc as DenseBincount,qi as DepthToSpace,Go as DepthwiseConv2dNative,Jc as DepthwiseConv2dNativeBackpropFilter,Qc as DepthwiseConv2dNativeBackpropInput,ep as Diag,gl as Dilation2D,rf as Dilation2DBackpropFilter,tf as Dilation2DBackpropInput,Xw as ENV,Nv as EarlyStopping,tp as Einsum,Uo as Elu,rp as EluGrad,Eg as Environment,Xi as Equal,Ki as Erf,jo as Exp,oi as ExpandDims,Yi as Expm1,np as FFT,xl as Fill,Zi as FlipLeftRight,Ho as Floor,qo as FloorDiv,nf as FromPixels,Ko as FusedBatchNorm,xi as FusedConv2D,yi as FusedDepthwiseConv2D,Xy as GPGPUContext,Ji as GatherNd,si as GatherV2,ly as GraphModel,Qi as Greater,Xo as GreaterEqual,zk as History,op as IFFT,ro as Identity,sp as Imag,Tt as InputSpec,ea as IsFinite,ta as IsInf,ra as IsNan,Js as KernelBackend,yl as LRN,ap as LRNGrad,Ex as LayerVariable,Yn as LayersModel,Yo as LeakyRelu,na as Less,oa as LessEqual,ip as LinSpace,Zo as Log,sa as Log1p,nN as LogSoftmax,ia as LogicalAnd,au as LogicalNot,lu as LogicalOr,_c as MathBackendWebGL,Jo as Max,es as MaxPool,bl as MaxPool3D,up as MaxPool3DGrad,lp as MaxPoolGrad,cp as MaxPoolWithArgmax,Qo as Maximum,ts as Mean,rs as Min,ns as Minimum,os as MirrorPad,aa as Mod,Yu as MomentumOptimizer,pp as Multinomial,ss as Multiply,ii as Neg,ua as NonMaxSuppressionV3,ca as NonMaxSuppressionV4,pa as NonMaxSuppressionV5,la as NotEqual,$N as OP_SCOPE_SUFFIX,is as OneHot,ai as OnesLike,qr as Optimizer,li as Pack,as as PadV2,hse as Pool,ls as Pow,us as Prelu,ma as Prod,Zu as RMSPropOptimizer,Ln as RNN,wl as Range,s_ as Rank,mp as Real,Wo as RealDiv,fa as Reciprocal,Yt as Reduction,cs as Relu,ms as Relu6,ui as Reshape,ps as ResizeBilinear,dp as ResizeBilinearGrad,_l as ResizeNearestNeighbor,fp as ResizeNearestNeighborGrad,fs as Reverse,ka as RotateWithOffset,ds as Round,hs as Rsqrt,Ga as SGDOptimizer,da as ScatterNd,ci as Select,ha as Selu,qa as Sequential,xs as Sigmoid,xa as Sign,gs as Sin,ga as Sinh,pi as Slice,ws as Softmax,ya as Softplus,mi as SpaceToBatchND,hp as SparseFillEmptyRows,gp as SparseReshape,xp as SparseSegmentMean,yp as SparseSegmentSum,bp as SparseToDense,fi as SplitV,ys as Sqrt,kl as Square,_s as SquaredDifference,no as Step,ba as StridedSlice,wp as StringNGrams,_p as StringSplit,kp as StringToHashBucketFast,ks as Sub,bs as Sum,cn as SymbolicTensor,vs as Tan,Cs as Tanh,Le as Tensor,mt as TensorBuffer,Hn as Tile,wa as TopK,_a as Transform,Is as Transpose,vp as Unique,di as Unpack,vl as UnsortedSegmentSum,Sl as Variable,hi as ZerosLike,gi as _FusedMatMul,Ct as abs,df as acos,hf as acosh,Z as add,F_ as addN,wu as all,El as any,As as argMax,gf as argMin,xf as asin,yf as asinh,bf as atan,wf as atan2,_f as atanh,Ta as avgPool,kf as avgPool3d,A1 as backend,I as backend_util,IU as basicLSTMCell,lo as batchNorm,L_ as batchNorm2d,z_ as batchNorm3d,B_ as batchNorm4d,Ea as batchToSpaceND,vf as bincount,JSe as booleanMaskAsync,V_ as broadcastArgs,Aa as broadcastTo,Gg as browser,Ie as buffer,PX as callbacks,Y as cast,Cf as ceil,gr as clipByValue,wn as clone,Pn as complex,tt as concat,G_ as concat1d,W_ as concat2d,U_ as concat3d,j_ as concat4d,W2 as constraints,vu as conv1d,nn as conv2d,Cu as conv2dTranspose,If as conv3d,H_ as conv3dTranspose,kse as copyRegisteredKernels,Da as cos,Iu as cosh,sx as cosineWindow,Su as cumsum,on as customGrad,v$ as data,q_ as denseBincount,R_ as deprecationWarn,Sf as depthToSpace,$s as depthwiseConv2d,LX as deregisterOp,xu as device_util,ej as diag,Nf as dilation2d,sue as disableDeprecationWarnings,De as dispose,iue as disposeVariables,ce as div,Tf as divNoNan,K_ as dot,eT as dropout,X_ as einsum,Rs as elu,oue as enableDebugMode,nue as enableProdMode,tT as enclosingPowerOfTwo,Es as engine,U as env,kr as equal,Ef as erf,Kt as exp,mr as expandDims,Af as expm1,Lp as eye,Ba as fft,Fs as fill,mue as findBackend,fue as findBackendFactory,Os as floor,bu as floorDiv,XP as forceHalfFloat,fo as fused,uo as gather,J1 as gatherND,Wg as gather_util,cue as getBackend,Jw as getGradient,sf as getKernel,Ag as getKernelsForBackend,Koe as getThreadsCount,XO as gpgpu_util,Aj as grad,Dj as grads,zt as greater,kn as greaterEqual,_i as ifft,Nu as imag,Cn as image,l1e as inTopKAsync,uA as initializers,Xk as input,Lr as io,zu as irfft,Y_ as isFinite,Z_ as isInf,Df as isNaN,Ft as keep,Gr as kernel_impls,UA as layers,$a as leakyRelu,Tu as less,vn as lessEqual,BT as linalg,J_ as linspace,m7 as loadGraphModel,K5 as loadLayersModel,$f as localResponseNormalization,xr as log,Ra as log1p,Q_ as logSigmoid,Eu as logSoftmax,Ff as logSumExp,Cr as logicalAnd,Fa as logicalNot,Au as logicalOr,nk as logicalXor,oFe as losses,ze as matMul,p1 as math,Rr as max,Oa as maxPool,Of as maxPool3d,ok as maxPoolWithArgmax,sn as maximum,xt as mean,ff as memory,Qj as meshgrid,jA as metrics,Al as min,Ps as minimum,Pf as mirrorPad,Mf as mod,H5 as model,HA as models,zp as moments,CNe as movingAverage,O as mul,aH as multiRNNCell,sk as multinomial,He as neg,wk as nextFrame,Wp as norm,mo as notEqual,Ts as oneHot,or as ones,fr as onesLike,N as op,mH as outerProduct,jr as pad,hH as pad1d,xH as pad2d,bH as pad3d,_H as pad4d,ik as pool,Hr as pow,Ma as prelu,C_ as print,Du as prod,aue as profile,AH as rand,LH as randomGamma,tx as randomNormal,Ms as randomUniform,La as range,uue as ready,Dl as real,Lf as reciprocal,Op as registerBackend,X5 as registerCallbackConstructor,oN as registerGradient,uu as registerKernel,MX as registerOp,qA as regularizers,Ir as relu,Ru as relu6,pue as removeBackend,F as reshape,er as reverse,qH as reverse1d,XH as reverse2d,ZH as reverse3d,QH as reverse4d,Va as rfft,Fu as round,Ou as rsqrt,pe as scalar,Y1 as scatterND,jg as scatter_util,Pu as selu,zf as separableConv2d,q5 as sequential,ee as serialization,q4 as setBackend,due as setPlatform,qoe as setThreadsCount,joe as setWasmPath,Hoe as setWasmPaths,tC as setWebGLContext,xk as setdiff1dAsync,zr as sigmoid,Bf as sign,DRe as signal,Mu as sin,Lu as sinh,Oe as slice,Vf as slice1d,rx as slice2d,Gf as slice3d,Vp as slice4d,pr as slice_util,za as softmax,co as softplus,Pa as spaceToBatchND,Kf as sparse,ox as sparseToDense,SRe as spectral,sr as split,bt as sqrt,Ve as square,Bu as squaredDifference,Br as squeeze,Xt as stack,Ls as step,Wf as stridedSlice,hx as string,le as sub,me as sum,hu as sumOutType,Uf as tan,Ds as tanh,Dr as tensor,$t as tensor1d,ki as tensor2d,T_ as tensor3d,Iq as tensor4d,Sq as tensor5d,Nq as tensor6d,ao as tensor_util,T1 as test_util,V as tidy,vr as tile,lue as time,jf as topk,Ju as train,Be as transpose,Vu as truncatedNormal,Gp as unique,_se as unregisterGradient,wse as unregisterKernel,Hf as unsortedSegmentSum,yr as unstack,hr as upcastType,b as util,$j as valueAndGrad,Rj as valueAndGrads,yk as variable,Zg as variableGrads,sse as version,SD as version_converter,E1 as version_core,lm as version_layers,Xoe as version_wasm,KP as version_webgl,LTt as webgl,zO as webgl_util,St as where,qf as whereAsync,yt as zeros,Se as zerosLike};
/**
* @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
* 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
*
* https://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 See the LICENSE file. */
//# sourceMappingURL=tfjs.esm.js.map
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