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human/dist/human.esm.js

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/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
var __defProp = Object.defineProperty;
var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value;
var __export = (target, all2) => {
for (var name in all2)
__defProp(target, name, { get: all2[name], enumerable: true });
};
var __publicField = (obj, key, value) => {
__defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value);
return value;
};
var __accessCheck = (obj, member, msg) => {
if (!member.has(obj))
throw TypeError("Cannot " + msg);
};
var __privateGet = (obj, member, getter) => {
__accessCheck(obj, member, "read from private field");
return getter ? getter.call(obj) : member.get(obj);
};
var __privateAdd = (obj, member, value) => {
if (member.has(obj))
throw TypeError("Cannot add the same private member more than once");
member instanceof WeakSet ? member.add(obj) : member.set(obj, value);
};
var __privateSet = (obj, member, value, setter) => {
__accessCheck(obj, member, "write to private field");
setter ? setter.call(obj, value) : member.set(obj, value);
return value;
};
// dist/tfjs.esm.js
var tfjs_esm_exports = {};
__export(tfjs_esm_exports, {
Abs: () => Gs,
Acos: () => zo,
Acosh: () => Vo,
AdadeltaOptimizer: () => Yu,
AdagradOptimizer: () => Qu,
AdamOptimizer: () => Zu,
AdamaxOptimizer: () => Ju,
Add: () => no,
AddN: () => Wo,
All: () => Uo,
Any: () => Go,
ArgMax: () => Hs,
ArgMin: () => Ks,
Asin: () => Ho,
Asinh: () => Ko,
Atan: () => qo,
Atan2: () => Xo,
Atanh: () => jo,
AvgPool: () => Yo,
AvgPool3D: () => qs,
AvgPool3DGrad: () => Ni,
AvgPoolGrad: () => Gp,
BackendWasm: () => am,
BatchMatMul: () => Qo,
BatchToSpaceND: () => js,
Bincount: () => Zo,
BitwiseAnd: () => ml,
BroadcastArgs: () => Xs,
BroadcastTo: () => Kpe,
Cast: () => ho,
Ceil: () => Jo,
ClipByValue: () => go,
Complex: () => Ti,
ComplexAbs: () => _i,
Concat: () => Ys,
Conv2D: () => en,
Conv2DBackpropFilter: () => $i,
Conv2DBackpropInput: () => tn,
Conv3D: () => rn,
Conv3DBackpropFilterV2: () => za,
Conv3DBackpropInputV2: () => on,
Cos: () => nn,
Cosh: () => sn,
CropAndResize: () => pn,
Cumprod: () => an,
Cumsum: () => un,
DataStorage: () => Lo,
DenseBincount: () => Qs,
DepthToSpace: () => cn,
DepthwiseConv2dNative: () => ln,
DepthwiseConv2dNativeBackpropFilter: () => Ei,
DepthwiseConv2dNativeBackpropInput: () => Ri,
Diag: () => Zs,
Dilation2D: () => mn,
Dilation2DBackpropFilter: () => Ai,
Dilation2DBackpropInput: () => Di,
ENV: () => WC,
Einsum: () => Fi,
Elu: () => fn,
EluGrad: () => Va,
Environment: () => cl,
Equal: () => hn,
Erf: () => Wa,
Exp: () => gn,
ExpandDims: () => Js,
Expm1: () => xn,
FFT: () => Pi,
Fill: () => ea,
FlipLeftRight: () => yn,
Floor: () => bn,
FloorDiv: () => Cn,
FromPixels: () => $u,
FusedBatchNorm: () => wn,
FusedConv2D: () => Co,
FusedDepthwiseConv2D: () => wo,
GPGPUContext: () => xp,
GatherNd: () => Sn,
GatherV2: () => ta,
GraphModel: () => Ol,
Greater: () => In,
GreaterEqual: () => vn,
IFFT: () => Oi,
Identity: () => xo,
Imag: () => Mi,
IsFinite: () => kn,
IsInf: () => Nn,
IsNan: () => Tn,
KernelBackend: () => ro,
LRN: () => Mn,
LRNGrad: () => Ua,
LeakyRelu: () => _n,
Less: () => $n,
LessEqual: () => En,
LinSpace: () => Rn,
Log: () => Dn,
Log1p: () => An,
LogSoftmax: () => qpe,
LogicalAnd: () => Fn,
LogicalNot: () => Pn,
LogicalOr: () => On,
LogicalXor: () => m0,
LowerBound: () => jpe,
MathBackendCPU: () => lu,
MathBackendWebGL: () => hu,
MatrixBandPart: () => Xpe,
Max: () => Ln,
MaxPool: () => zn,
MaxPool3D: () => ra,
MaxPool3DGrad: () => Li,
MaxPoolGrad: () => Hp,
MaxPoolWithArgmax: () => Bi,
Maximum: () => Bn,
Mean: () => Vn,
Min: () => Wn,
Minimum: () => Un,
MirrorPad: () => Gn,
Mod: () => Ga,
MomentumOptimizer: () => ep,
Multinomial: () => Hn,
Multiply: () => Kn,
Neg: () => oa,
NonMaxSuppressionV3: () => jn,
NonMaxSuppressionV4: () => Ha,
NonMaxSuppressionV5: () => Xn,
NotEqual: () => qn,
OP_SCOPE_SUFFIX: () => pw,
OneHot: () => Yn,
OnesLike: () => na,
Optimizer: () => kr,
OptimizerConstructors: () => Dl,
Pack: () => sa,
PadV2: () => Qn,
Pool: () => Ype,
Pow: () => Zn,
Prelu: () => Jn,
Prod: () => es,
RMSPropOptimizer: () => tp,
RaggedGather: () => Kp,
RaggedRange: () => qp,
RaggedTensorToTensor: () => jp,
Range: () => aa,
Rank: () => JC,
Real: () => zi,
RealDiv: () => dn,
Reciprocal: () => ts,
Reduction: () => Et,
Relu: () => rs,
Relu6: () => ss,
Reshape: () => ia,
ResizeBilinear: () => ns,
ResizeBilinearGrad: () => qa,
ResizeNearestNeighbor: () => os,
ResizeNearestNeighborGrad: () => Ka,
Reverse: () => as,
RotateWithOffset: () => _s,
Round: () => is,
Rsqrt: () => us,
SGDOptimizer: () => ii,
ScatterNd: () => ps,
SearchSorted: () => ls,
Select: () => ua,
Selu: () => ms,
Sigmoid: () => hs,
Sign: () => fs,
Sin: () => ds,
Sinh: () => ja,
Slice: () => pa,
Softmax: () => bs,
Softplus: () => gs,
SpaceToBatchND: () => ca,
SparseFillEmptyRows: () => Vi,
SparseReshape: () => Xa,
SparseSegmentMean: () => Wi,
SparseSegmentSum: () => Ui,
SparseToDense: () => Cs,
SplitV: () => la,
Sqrt: () => xs,
Square: () => Gi,
SquaredDifference: () => ws,
StaticRegexReplace: () => _u,
Step: () => yo,
StridedSlice: () => Ss,
StringNGrams: () => ma,
StringSplit: () => Hi,
StringToHashBucketFast: () => Ki,
Sub: () => Is,
Sum: () => ys,
Tan: () => vs,
Tanh: () => ks,
Tensor: () => pt,
TensorBuffer: () => tt,
TensorScatterUpdate: () => cs,
Tile: () => so,
TopK: () => Ns,
Transform: () => Ts,
Transpose: () => ao,
Unique: () => qi,
Unpack: () => da,
UnsortedSegmentSum: () => ji,
UpperBound: () => Qpe,
Variable: () => Qa,
WebGPUBackend: () => Cu,
ZerosLike: () => fa,
_FusedMatMul: () => bo,
abs: () => Zt,
acos: () => ik,
acosh: () => uk,
add: () => be,
addN: () => pk,
all: () => ck,
any: () => lk,
argMax: () => mk,
argMin: () => dk,
asin: () => fk,
asinh: () => hk,
atan: () => gk,
atan2: () => xk,
atanh: () => yk,
avgPool: () => cd,
avgPool3d: () => wk,
backend: () => Tme,
backend_util: () => C,
basicLSTMCell: () => Sk,
batchNorm: () => tu,
batchNorm2d: () => vk,
batchNorm3d: () => kk,
batchNorm4d: () => Nk,
batchToSpaceND: () => ld,
bincount: () => md,
bitwiseAnd: () => Tk,
booleanMaskAsync: () => qq,
broadcastArgs: () => _k,
broadcastTo: () => ru,
broadcast_util: () => Sr,
browser: () => MN,
buffer: () => me,
cast: () => Ye,
ceil: () => $k,
clipByValue: () => Ek,
clone: () => Vr,
complex: () => $r,
concat: () => yt,
concat1d: () => Rk,
concat2d: () => Dk,
concat3d: () => Ak,
concat4d: () => Fk,
conv1d: () => Pk,
conv2d: () => ou,
conv2dTranspose: () => Ok,
conv3d: () => Mk,
conv3dTranspose: () => Bk,
copyRegisteredKernels: () => sce,
cos: () => zk,
cosh: () => Vk,
cosineWindow: () => _l,
cumprod: () => Wk,
cumsum: () => Uk,
customGrad: () => Ir,
denseBincount: () => Gk,
deprecationWarn: () => bw,
depthToSpace: () => Hk,
depthwiseConv2d: () => ac,
deregisterOp: () => KX,
device_util: () => Zi,
diag: () => Kk,
dilation2d: () => qk,
disableDeprecationWarnings: () => gme,
dispose: () => Ot,
disposeVariables: () => xme,
div: () => Ke,
divNoNan: () => Xk,
dot: () => Yk,
dropout: () => s6,
einsum: () => Qk,
elu: () => gd,
enableDebugMode: () => hme,
enableProdMode: () => fme,
enclosingPowerOfTwo: () => Pw,
engine: () => ur,
ensureShape: () => Zk,
env: () => P,
equal: () => hd,
erf: () => Jk,
euclideanNorm: () => r2,
exp: () => ko,
expandDims: () => oi,
expm1: () => o2,
eye: () => xd,
fft: () => pc,
fill: () => Sa,
findBackend: () => kme,
findBackendFactory: () => Nme,
floor: () => yd,
floorDiv: () => pd,
forceHalfFloat: () => hD,
fused: () => Ow,
gather: () => bd,
gatherND: () => o6,
gather_util: () => of,
getBackend: () => Ime,
getGradient: () => HC,
getKernel: () => fl,
getKernelsForBackend: () => Km,
getThreadsCount: () => ese,
gpgpu_util: () => qI,
grad: () => YH,
grads: () => QH,
greater: () => Bu,
greaterEqual: () => Cd,
ifft: () => Hu,
imag: () => su,
image: () => uj,
inTopKAsync: () => i6,
io: () => pi,
irfft: () => Wd,
isFinite: () => n2,
isInf: () => s2,
isNaN: () => a2,
keep: () => Er,
kernel_impls: () => Wt,
leakyRelu: () => wd,
less: () => kl,
lessEqual: () => ic,
linalg: () => pj,
linspace: () => i2,
loadGraphModel: () => W5,
loadGraphModelSync: () => U5,
localResponseNormalization: () => u2,
log: () => ni,
log1p: () => Sd,
logSigmoid: () => p2,
logSoftmax: () => c2,
logSumExp: () => kd,
logicalAnd: () => zu,
logicalNot: () => Nd,
logicalOr: () => Td,
logicalXor: () => l2,
losses: () => cj,
lowerBound: () => m2,
matMul: () => Qe,
math: () => PN,
max: () => Ia,
maxPool: () => $d,
maxPool3d: () => d2,
maxPoolWithArgmax: () => f2,
maximum: () => Ed,
mean: () => Vu,
memory: () => yme,
meshgrid: () => h2,
min: () => vl,
minimum: () => Wu,
mirrorPad: () => g2,
mod: () => x2,
moments: () => y2,
movingAverage: () => Yq,
mul: () => se,
multiRNNCell: () => b2,
multinomial: () => C2,
neg: () => pr,
nextFrame: () => Kw,
norm: () => Lu,
notEqual: () => Rd,
oneHot: () => Tl,
ones: () => va,
onesLike: () => w2,
op: () => N,
outerProduct: () => S2,
pad: () => ka,
pad1d: () => I2,
pad2d: () => v2,
pad3d: () => k2,
pad4d: () => N2,
pool: () => T2,
pow: () => ri,
prelu: () => Ad,
print: () => ud,
prod: () => _2,
profile: () => bme,
raggedGather: () => $2,
raggedRange: () => E2,
raggedTensorToTensor: () => R2,
rand: () => D2,
randomGamma: () => J2,
randomNormal: () => Bd,
randomStandardNormal: () => e1,
randomUniform: () => uc,
randomUniformInt: () => t1,
range: () => au,
ready: () => Sme,
real: () => si,
reciprocal: () => r1,
registerBackend: () => eu,
registerGradient: () => rce,
registerKernel: () => Ya,
registerOp: () => HX,
relu: () => iu,
relu6: () => zd,
removeBackend: () => vme,
reshape: () => W,
reverse: () => uo,
reverse1d: () => o1,
reverse2d: () => n1,
reverse3d: () => s1,
reverse4d: () => a1,
rfft: () => cc,
round: () => Vd,
rsqrt: () => i1,
scalar: () => ke,
scatterND: () => Zq,
scatter_util: () => pu,
searchSorted: () => Nl,
selu: () => u1,
separableConv2d: () => p1,
serialization: () => vN,
setBackend: () => wme,
setPlatform: () => _me,
setThreadsCount: () => Jne,
setWasmPath: () => Qne,
setWasmPaths: () => Zne,
setWebGLContext: () => iI,
setdiff1dAsync: () => c1,
shared: () => Sc,
sigmoid: () => wa,
sign: () => l1,
signal: () => ij,
sin: () => m1,
sinh: () => d1,
slice: () => qe,
slice1d: () => f1,
slice2d: () => h1,
slice3d: () => g1,
slice4d: () => x1,
slice_util: () => ct,
softmax: () => y1,
softplus: () => vd,
spaceToBatchND: () => Dd,
sparse: () => lj,
sparseToDense: () => t6,
spectral: () => aj,
split: () => ai,
sqrt: () => Rr,
square: () => Jt,
squaredDifference: () => Ud,
squeeze: () => lc,
stack: () => vr,
step: () => Gd,
stridedSlice: () => b1,
string: () => mj,
sub: () => Te,
sum: () => ot,
sumOutType: () => Za,
tan: () => C1,
tanh: () => Il,
tensor: () => ir,
tensor1d: () => xr,
tensor2d: () => uu,
tensor3d: () => Hd,
tensor4d: () => w1,
tensor5d: () => S1,
tensor6d: () => I1,
tensorScatterUpdate: () => k1,
tensor_util: () => M0,
test_util: () => Z2,
tidy: () => De,
tile: () => nu,
time: () => Cme,
topk: () => N1,
train: () => CUe,
transpose: () => dc,
truncatedNormal: () => T1,
unique: () => _1,
unregisterGradient: () => nce,
unregisterKernel: () => oce,
unsortedSegmentSum: () => $1,
unstack: () => po,
upcastType: () => dt,
upperBound: () => E1,
util: () => y,
valueAndGrad: () => ZH,
valueAndGrads: () => JH,
variable: () => R1,
variableGrads: () => vw,
version: () => zpe,
version_converter: () => H5,
version_core: () => Vj,
version_cpu: () => I8,
version_wasm: () => tse,
version_webgl: () => xZ,
webgl: () => cst,
webgl_util: () => _c,
webgpu_util: () => Fv,
where: () => io,
whereAsync: () => qd,
zeros: () => Wr,
zerosLike: () => Ht
});
var BU = Object.create;
var PC = Object.defineProperty;
var zU = Object.getOwnPropertyDescriptor;
var VU = Object.getOwnPropertyNames;
var WU = Object.getPrototypeOf;
var UU = Object.prototype.hasOwnProperty;
var qt = (r, e) => () => (e || r((e = { exports: {} }).exports, e), e.exports);
var He = (r, e) => {
for (var t10 in e)
PC(r, t10, { get: e[t10], enumerable: true });
};
var GU = (r, e, t10, o) => {
if (e && typeof e == "object" || typeof e == "function")
for (let n of VU(e))
!UU.call(r, n) && n !== t10 && PC(r, n, { get: () => e[n], enumerable: !(o = zU(e, n)) || o.enumerable });
return r;
};
var Bp = (r, e, t10) => (t10 = r != null ? BU(WU(r)) : {}, GU(e || !r || !r.__esModule ? PC(t10, "default", { value: r, enumerable: true }) : t10, r));
var v0 = qt((uce, I0) => {
I0.exports = vt;
var So = null;
try {
So = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11])), {}).exports;
} catch (r) {
}
function vt(r, e, t10) {
this.low = r | 0, this.high = e | 0, this.unsigned = !!t10;
}
vt.prototype.__isLong__;
Object.defineProperty(vt.prototype, "__isLong__", { value: true });
function zr(r) {
return (r && r.__isLong__) === true;
}
vt.isLong = zr;
var f0 = {}, h0 = {};
function Ru(r, e) {
var t10, o, n;
return e ? (r >>>= 0, (n = 0 <= r && r < 256) && (o = h0[r], o) ? o : (t10 = kt(r, (r | 0) < 0 ? -1 : 0, true), n && (h0[r] = t10), t10)) : (r |= 0, (n = -128 <= r && r < 128) && (o = f0[r], o) ? o : (t10 = kt(r, r < 0 ? -1 : 0, false), n && (f0[r] = t10), t10));
}
vt.fromInt = Ru;
function Io(r, e) {
if (isNaN(r))
return e ? Eu : vo;
if (e) {
if (r < 0)
return Eu;
if (r >= b0)
return S0;
} else {
if (r <= -x0)
return Br;
if (r + 1 >= x0)
return w0;
}
return r < 0 ? Io(-r, e).neg() : kt(r % Qp | 0, r / Qp | 0, e);
}
vt.fromNumber = Io;
function kt(r, e, t10) {
return new vt(r, e, t10);
}
vt.fromBits = kt;
var jm = Math.pow;
function jC(r, e, t10) {
if (r.length === 0)
throw Error("empty string");
if (r === "NaN" || r === "Infinity" || r === "+Infinity" || r === "-Infinity")
return vo;
if (typeof e == "number" ? (t10 = e, e = false) : e = !!e, t10 = t10 || 10, t10 < 2 || 36 < t10)
throw RangeError("radix");
var o;
if ((o = r.indexOf("-")) > 0)
throw Error("interior hyphen");
if (o === 0)
return jC(r.substring(1), e, t10).neg();
for (var n = Io(jm(t10, 8)), s = vo, a = 0; a < r.length; a += 8) {
var i = Math.min(8, r.length - a), p = parseInt(r.substring(a, a + i), t10);
if (i < 8) {
var u = Io(jm(t10, i));
s = s.mul(u).add(Io(p));
} else
s = s.mul(n), s = s.add(Io(p));
}
return s.unsigned = e, s;
}
vt.fromString = jC;
function $s(r, e) {
return typeof r == "number" ? Io(r, e) : typeof r == "string" ? jC(r, e) : kt(r.low, r.high, typeof e == "boolean" ? e : r.unsigned);
}
vt.fromValue = $s;
var g0 = 1 << 16, dG = 1 << 24, Qp = g0 * g0, b0 = Qp * Qp, x0 = b0 / 2, y0 = Ru(dG), vo = Ru(0);
vt.ZERO = vo;
var Eu = Ru(0, true);
vt.UZERO = Eu;
var Yp = Ru(1);
vt.ONE = Yp;
var C0 = Ru(1, true);
vt.UONE = C0;
var qC = Ru(-1);
vt.NEG_ONE = qC;
var w0 = kt(-1, 2147483647, false);
vt.MAX_VALUE = w0;
var S0 = kt(-1, -1, true);
vt.MAX_UNSIGNED_VALUE = S0;
var Br = kt(0, -2147483648, false);
vt.MIN_VALUE = Br;
var de = vt.prototype;
de.toInt = function() {
return this.unsigned ? this.low >>> 0 : this.low;
};
de.toNumber = function() {
return this.unsigned ? (this.high >>> 0) * Qp + (this.low >>> 0) : this.high * Qp + (this.low >>> 0);
};
de.toString = function(e) {
if (e = e || 10, e < 2 || 36 < e)
throw RangeError("radix");
if (this.isZero())
return "0";
if (this.isNegative())
if (this.eq(Br)) {
var t10 = Io(e), o = this.div(t10), n = o.mul(t10).sub(this);
return o.toString(e) + n.toInt().toString(e);
} else
return "-" + this.neg().toString(e);
for (var s = Io(jm(e, 6), this.unsigned), a = this, i = ""; ; ) {
var p = a.div(s), u = a.sub(p.mul(s)).toInt() >>> 0, c = u.toString(e);
if (a = p, a.isZero())
return c + i;
for (; c.length < 6; )
c = "0" + c;
i = "" + c + i;
}
};
de.getHighBits = function() {
return this.high;
};
de.getHighBitsUnsigned = function() {
return this.high >>> 0;
};
de.getLowBits = function() {
return this.low;
};
de.getLowBitsUnsigned = function() {
return this.low >>> 0;
};
de.getNumBitsAbs = function() {
if (this.isNegative())
return this.eq(Br) ? 64 : this.neg().getNumBitsAbs();
for (var e = this.high != 0 ? this.high : this.low, t10 = 31; t10 > 0 && !(e & 1 << t10); t10--)
;
return this.high != 0 ? t10 + 33 : t10 + 1;
};
de.isZero = function() {
return this.high === 0 && this.low === 0;
};
de.eqz = de.isZero;
de.isNegative = function() {
return !this.unsigned && this.high < 0;
};
de.isPositive = function() {
return this.unsigned || this.high >= 0;
};
de.isOdd = function() {
return (this.low & 1) === 1;
};
de.isEven = function() {
return (this.low & 1) === 0;
};
de.equals = function(e) {
return zr(e) || (e = $s(e)), this.unsigned !== e.unsigned && this.high >>> 31 === 1 && e.high >>> 31 === 1 ? false : this.high === e.high && this.low === e.low;
};
de.eq = de.equals;
de.notEquals = function(e) {
return !this.eq(e);
};
de.neq = de.notEquals;
de.ne = de.notEquals;
de.lessThan = function(e) {
return this.comp(e) < 0;
};
de.lt = de.lessThan;
de.lessThanOrEqual = function(e) {
return this.comp(e) <= 0;
};
de.lte = de.lessThanOrEqual;
de.le = de.lessThanOrEqual;
de.greaterThan = function(e) {
return this.comp(e) > 0;
};
de.gt = de.greaterThan;
de.greaterThanOrEqual = function(e) {
return this.comp(e) >= 0;
};
de.gte = de.greaterThanOrEqual;
de.ge = de.greaterThanOrEqual;
de.compare = function(e) {
if (zr(e) || (e = $s(e)), this.eq(e))
return 0;
var t10 = this.isNegative(), o = e.isNegative();
return t10 && !o ? -1 : !t10 && o ? 1 : this.unsigned ? e.high >>> 0 > this.high >>> 0 || e.high === this.high && e.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(e).isNegative() ? -1 : 1;
};
de.comp = de.compare;
de.negate = function() {
return !this.unsigned && this.eq(Br) ? Br : this.not().add(Yp);
};
de.neg = de.negate;
de.add = function(e) {
zr(e) || (e = $s(e));
var t10 = this.high >>> 16, o = this.high & 65535, n = this.low >>> 16, s = this.low & 65535, a = e.high >>> 16, i = e.high & 65535, p = e.low >>> 16, u = e.low & 65535, c = 0, l = 0, m = 0, d = 0;
return d += s + u, m += d >>> 16, d &= 65535, m += n + p, l += m >>> 16, m &= 65535, l += o + i, c += l >>> 16, l &= 65535, c += t10 + a, c &= 65535, kt(m << 16 | d, c << 16 | l, this.unsigned);
};
de.subtract = function(e) {
return zr(e) || (e = $s(e)), this.add(e.neg());
};
de.sub = de.subtract;
de.multiply = function(e) {
if (this.isZero())
return vo;
if (zr(e) || (e = $s(e)), So) {
var t10 = So.mul(this.low, this.high, e.low, e.high);
return kt(t10, So.get_high(), this.unsigned);
}
if (e.isZero())
return vo;
if (this.eq(Br))
return e.isOdd() ? Br : vo;
if (e.eq(Br))
return this.isOdd() ? Br : vo;
if (this.isNegative())
return e.isNegative() ? this.neg().mul(e.neg()) : this.neg().mul(e).neg();
if (e.isNegative())
return this.mul(e.neg()).neg();
if (this.lt(y0) && e.lt(y0))
return Io(this.toNumber() * e.toNumber(), this.unsigned);
var o = this.high >>> 16, n = this.high & 65535, s = this.low >>> 16, a = this.low & 65535, i = e.high >>> 16, p = e.high & 65535, u = e.low >>> 16, c = e.low & 65535, l = 0, m = 0, d = 0, f = 0;
return f += a * c, d += f >>> 16, f &= 65535, d += s * c, m += d >>> 16, d &= 65535, d += a * u, m += d >>> 16, d &= 65535, m += n * c, l += m >>> 16, m &= 65535, m += s * u, l += m >>> 16, m &= 65535, m += a * p, l += m >>> 16, m &= 65535, l += o * c + n * u + s * p + a * i, l &= 65535, kt(d << 16 | f, l << 16 | m, this.unsigned);
};
de.mul = de.multiply;
de.divide = function(e) {
if (zr(e) || (e = $s(e)), e.isZero())
throw Error("division by zero");
if (So) {
if (!this.unsigned && this.high === -2147483648 && e.low === -1 && e.high === -1)
return this;
var t10 = (this.unsigned ? So.div_u : So.div_s)(this.low, this.high, e.low, e.high);
return kt(t10, So.get_high(), this.unsigned);
}
if (this.isZero())
return this.unsigned ? Eu : vo;
var o, n, s;
if (this.unsigned) {
if (e.unsigned || (e = e.toUnsigned()), e.gt(this))
return Eu;
if (e.gt(this.shru(1)))
return C0;
s = Eu;
} else {
if (this.eq(Br)) {
if (e.eq(Yp) || e.eq(qC))
return Br;
if (e.eq(Br))
return Yp;
var a = this.shr(1);
return o = a.div(e).shl(1), o.eq(vo) ? e.isNegative() ? Yp : qC : (n = this.sub(e.mul(o)), s = o.add(n.div(e)), s);
} else if (e.eq(Br))
return this.unsigned ? Eu : vo;
if (this.isNegative())
return e.isNegative() ? this.neg().div(e.neg()) : this.neg().div(e).neg();
if (e.isNegative())
return this.div(e.neg()).neg();
s = vo;
}
for (n = this; n.gte(e); ) {
o = Math.max(1, Math.floor(n.toNumber() / e.toNumber()));
for (var i = Math.ceil(Math.log(o) / Math.LN2), p = i <= 48 ? 1 : jm(2, i - 48), u = Io(o), c = u.mul(e); c.isNegative() || c.gt(n); )
o -= p, u = Io(o, this.unsigned), c = u.mul(e);
u.isZero() && (u = Yp), s = s.add(u), n = n.sub(c);
}
return s;
};
de.div = de.divide;
de.modulo = function(e) {
if (zr(e) || (e = $s(e)), So) {
var t10 = (this.unsigned ? So.rem_u : So.rem_s)(this.low, this.high, e.low, e.high);
return kt(t10, So.get_high(), this.unsigned);
}
return this.sub(this.div(e).mul(e));
};
de.mod = de.modulo;
de.rem = de.modulo;
de.not = function() {
return kt(~this.low, ~this.high, this.unsigned);
};
de.and = function(e) {
return zr(e) || (e = $s(e)), kt(this.low & e.low, this.high & e.high, this.unsigned);
};
de.or = function(e) {
return zr(e) || (e = $s(e)), kt(this.low | e.low, this.high | e.high, this.unsigned);
};
de.xor = function(e) {
return zr(e) || (e = $s(e)), kt(this.low ^ e.low, this.high ^ e.high, this.unsigned);
};
de.shiftLeft = function(e) {
return zr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? kt(this.low << e, this.high << e | this.low >>> 32 - e, this.unsigned) : kt(0, this.low << e - 32, this.unsigned);
};
de.shl = de.shiftLeft;
de.shiftRight = function(e) {
return zr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? kt(this.low >>> e | this.high << 32 - e, this.high >> e, this.unsigned) : kt(this.high >> e - 32, this.high >= 0 ? 0 : -1, this.unsigned);
};
de.shr = de.shiftRight;
de.shiftRightUnsigned = function(e) {
if (zr(e) && (e = e.toInt()), e &= 63, e === 0)
return this;
var t10 = this.high;
if (e < 32) {
var o = this.low;
return kt(o >>> e | t10 << 32 - e, t10 >>> e, this.unsigned);
} else
return e === 32 ? kt(t10, 0, this.unsigned) : kt(t10 >>> e - 32, 0, this.unsigned);
};
de.shru = de.shiftRightUnsigned;
de.shr_u = de.shiftRightUnsigned;
de.toSigned = function() {
return this.unsigned ? kt(this.low, this.high, false) : this;
};
de.toUnsigned = function() {
return this.unsigned ? this : kt(this.low, this.high, true);
};
de.toBytes = function(e) {
return e ? this.toBytesLE() : this.toBytesBE();
};
de.toBytesLE = function() {
var e = this.high, t10 = this.low;
return [t10 & 255, t10 >>> 8 & 255, t10 >>> 16 & 255, t10 >>> 24, e & 255, e >>> 8 & 255, e >>> 16 & 255, e >>> 24];
};
de.toBytesBE = function() {
var e = this.high, t10 = this.low;
return [e >>> 24, e >>> 16 & 255, e >>> 8 & 255, e & 255, t10 >>> 24, t10 >>> 16 & 255, t10 >>> 8 & 255, t10 & 255];
};
vt.fromBytes = function(e, t10, o) {
return o ? vt.fromBytesLE(e, t10) : vt.fromBytesBE(e, t10);
};
vt.fromBytesLE = function(e, t10) {
return new vt(e[0] | e[1] << 8 | e[2] << 16 | e[3] << 24, e[4] | e[5] << 8 | e[6] << 16 | e[7] << 24, t10);
};
vt.fromBytesBE = function(e, t10) {
return new vt(e[4] << 24 | e[5] << 16 | e[6] << 8 | e[7], e[0] << 24 | e[1] << 16 | e[2] << 8 | e[3], t10);
};
});
var sk = qt(() => {
});
var ak = qt(() => {
});
var F2 = qt((A2, kw) => {
(function(r, e, t10) {
function o(i) {
var p = this, u = a();
p.next = function() {
var c = 2091639 * p.s0 + p.c * 23283064365386963e-26;
return p.s0 = p.s1, p.s1 = p.s2, p.s2 = c - (p.c = c | 0);
}, p.c = 1, p.s0 = u(" "), p.s1 = u(" "), p.s2 = u(" "), p.s0 -= u(i), p.s0 < 0 && (p.s0 += 1), p.s1 -= u(i), p.s1 < 0 && (p.s1 += 1), p.s2 -= u(i), p.s2 < 0 && (p.s2 += 1), u = null;
}
function n(i, p) {
return p.c = i.c, p.s0 = i.s0, p.s1 = i.s1, p.s2 = i.s2, p;
}
function s(i, p) {
var u = new o(i), c = p && p.state, l = u.next;
return l.int32 = function() {
return u.next() * 4294967296 | 0;
}, l.double = function() {
return l() + (l() * 2097152 | 0) * 11102230246251565e-32;
}, l.quick = l, c && (typeof c == "object" && n(c, u), l.state = function() {
return n(u, {});
}), l;
}
function a() {
var i = 4022871197, p = function(u) {
u = String(u);
for (var c = 0; c < u.length; c++) {
i += u.charCodeAt(c);
var l = 0.02519603282416938 * i;
i = l >>> 0, l -= i, l *= i, i = l >>> 0, l -= i, i += l * 4294967296;
}
return (i >>> 0) * 23283064365386963e-26;
};
return p;
}
e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
return s;
}) : this.alea = s;
})(A2, typeof kw == "object" && kw, typeof define == "function" && define);
});
var O2 = qt((P2, Nw) => {
(function(r, e, t10) {
function o(a) {
var i = this, p = "";
i.x = 0, i.y = 0, i.z = 0, i.w = 0, i.next = function() {
var c = i.x ^ i.x << 11;
return i.x = i.y, i.y = i.z, i.z = i.w, i.w ^= i.w >>> 19 ^ c ^ c >>> 8;
}, a === (a | 0) ? i.x = a : p += a;
for (var u = 0; u < p.length + 64; u++)
i.x ^= p.charCodeAt(u) | 0, i.next();
}
function n(a, i) {
return i.x = a.x, i.y = a.y, i.z = a.z, i.w = a.w, i;
}
function s(a, i) {
var p = new o(a), u = i && i.state, c = function() {
return (p.next() >>> 0) / 4294967296;
};
return c.double = function() {
do
var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, d = (l + m) / (1 << 21);
while (d === 0);
return d;
}, c.int32 = p.next, c.quick = c, u && (typeof u == "object" && n(u, p), c.state = function() {
return n(p, {});
}), c;
}
e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
return s;
}) : this.xor128 = s;
})(P2, typeof Nw == "object" && Nw, typeof define == "function" && define);
});
var L2 = qt((M2, Tw) => {
(function(r, e, t10) {
function o(a) {
var i = this, p = "";
i.next = function() {
var c = i.x ^ i.x >>> 2;
return i.x = i.y, i.y = i.z, i.z = i.w, i.w = i.v, (i.d = i.d + 362437 | 0) + (i.v = i.v ^ i.v << 4 ^ (c ^ c << 1)) | 0;
}, i.x = 0, i.y = 0, i.z = 0, i.w = 0, i.v = 0, a === (a | 0) ? i.x = a : p += a;
for (var u = 0; u < p.length + 64; u++)
i.x ^= p.charCodeAt(u) | 0, u == p.length && (i.d = i.x << 10 ^ i.x >>> 4), i.next();
}
function n(a, i) {
return i.x = a.x, i.y = a.y, i.z = a.z, i.w = a.w, i.v = a.v, i.d = a.d, i;
}
function s(a, i) {
var p = new o(a), u = i && i.state, c = function() {
return (p.next() >>> 0) / 4294967296;
};
return c.double = function() {
do
var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, d = (l + m) / (1 << 21);
while (d === 0);
return d;
}, c.int32 = p.next, c.quick = c, u && (typeof u == "object" && n(u, p), c.state = function() {
return n(p, {});
}), c;
}
e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
return s;
}) : this.xorwow = s;
})(M2, typeof Tw == "object" && Tw, typeof define == "function" && define);
});
var z2 = qt((B2, _w) => {
(function(r, e, t10) {
function o(a) {
var i = this;
i.next = function() {
var u = i.x, c = i.i, l, m, d;
return l = u[c], l ^= l >>> 7, m = l ^ l << 24, l = u[c + 1 & 7], m ^= l ^ l >>> 10, l = u[c + 3 & 7], m ^= l ^ l >>> 3, l = u[c + 4 & 7], m ^= l ^ l << 7, l = u[c + 7 & 7], l = l ^ l << 13, m ^= l ^ l << 9, u[c] = m, i.i = c + 1 & 7, m;
};
function p(u, c) {
var l, m, d = [];
if (c === (c | 0))
m = d[0] = c;
else
for (c = "" + c, l = 0; l < c.length; ++l)
d[l & 7] = d[l & 7] << 15 ^ c.charCodeAt(l) + d[l + 1 & 7] << 13;
for (; d.length < 8; )
d.push(0);
for (l = 0; l < 8 && d[l] === 0; ++l)
;
for (l == 8 ? m = d[7] = -1 : m = d[l], u.x = d, u.i = 0, l = 256; l > 0; --l)
u.next();
}
p(i, a);
}
function n(a, i) {
return i.x = a.x.slice(), i.i = a.i, i;
}
function s(a, i) {
a == null && (a = +/* @__PURE__ */ new Date());
var p = new o(a), u = i && i.state, c = function() {
return (p.next() >>> 0) / 4294967296;
};
return c.double = function() {
do
var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, d = (l + m) / (1 << 21);
while (d === 0);
return d;
}, c.int32 = p.next, c.quick = c, u && (u.x && n(u, p), c.state = function() {
return n(p, {});
}), c;
}
e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
return s;
}) : this.xorshift7 = s;
})(B2, typeof _w == "object" && _w, typeof define == "function" && define);
});
var W2 = qt((V2, $w) => {
(function(r, e, t10) {
function o(a) {
var i = this;
i.next = function() {
var u = i.w, c = i.X, l = i.i, m, d;
return i.w = u = u + 1640531527 | 0, d = c[l + 34 & 127], m = c[l = l + 1 & 127], d ^= d << 13, m ^= m << 17, d ^= d >>> 15, m ^= m >>> 12, d = c[l] = d ^ m, i.i = l, d + (u ^ u >>> 16) | 0;
};
function p(u, c) {
var l, m, d, f, h, g = [], x = 128;
for (c === (c | 0) ? (m = c, c = null) : (c = c + "\0", m = 0, x = Math.max(x, c.length)), d = 0, f = -32; f < x; ++f)
c && (m ^= c.charCodeAt((f + 32) % c.length)), f === 0 && (h = m), m ^= m << 10, m ^= m >>> 15, m ^= m << 4, m ^= m >>> 13, f >= 0 && (h = h + 1640531527 | 0, l = g[f & 127] ^= m + h, d = l == 0 ? d + 1 : 0);
for (d >= 128 && (g[(c && c.length || 0) & 127] = -1), d = 127, f = 4 * 128; f > 0; --f)
m = g[d + 34 & 127], l = g[d = d + 1 & 127], m ^= m << 13, l ^= l << 17, m ^= m >>> 15, l ^= l >>> 12, g[d] = m ^ l;
u.w = h, u.X = g, u.i = d;
}
p(i, a);
}
function n(a, i) {
return i.i = a.i, i.w = a.w, i.X = a.X.slice(), i;
}
function s(a, i) {
a == null && (a = +/* @__PURE__ */ new Date());
var p = new o(a), u = i && i.state, c = function() {
return (p.next() >>> 0) / 4294967296;
};
return c.double = function() {
do
var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, d = (l + m) / (1 << 21);
while (d === 0);
return d;
}, c.int32 = p.next, c.quick = c, u && (u.X && n(u, p), c.state = function() {
return n(p, {});
}), c;
}
e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
return s;
}) : this.xor4096 = s;
})(V2, typeof $w == "object" && $w, typeof define == "function" && define);
});
var G2 = qt((U2, Ew) => {
(function(r, e, t10) {
function o(a) {
var i = this, p = "";
i.next = function() {
var c = i.b, l = i.c, m = i.d, d = i.a;
return c = c << 25 ^ c >>> 7 ^ l, l = l - m | 0, m = m << 24 ^ m >>> 8 ^ d, d = d - c | 0, i.b = c = c << 20 ^ c >>> 12 ^ l, i.c = l = l - m | 0, i.d = m << 16 ^ l >>> 16 ^ d, i.a = d - c | 0;
}, i.a = 0, i.b = 0, i.c = -1640531527, i.d = 1367130551, a === Math.floor(a) ? (i.a = a / 4294967296 | 0, i.b = a | 0) : p += a;
for (var u = 0; u < p.length + 20; u++)
i.b ^= p.charCodeAt(u) | 0, i.next();
}
function n(a, i) {
return i.a = a.a, i.b = a.b, i.c = a.c, i.d = a.d, i;
}
function s(a, i) {
var p = new o(a), u = i && i.state, c = function() {
return (p.next() >>> 0) / 4294967296;
};
return c.double = function() {
do
var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, d = (l + m) / (1 << 21);
while (d === 0);
return d;
}, c.int32 = p.next, c.quick = c, u && (typeof u == "object" && n(u, p), c.state = function() {
return n(p, {});
}), c;
}
e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
return s;
}) : this.tychei = s;
})(U2, typeof Ew == "object" && Ew, typeof define == "function" && define);
});
var H2 = qt(() => {
});
var q2 = qt((K2, Fd) => {
(function(r, e, t10) {
var o = 256, n = 6, s = 52, a = "random", i = t10.pow(o, n), p = t10.pow(2, s), u = p * 2, c = o - 1, l;
function m(w, S, k) {
var _ = [];
S = S == true ? { entropy: true } : S || {};
var E = g(h(S.entropy ? [w, b(e)] : w == null ? x() : w, 3), _), R = new d(_), D = function() {
for (var F = R.g(n), O = i, M = 0; F < p; )
F = (F + M) * o, O *= o, M = R.g(1);
for (; F >= u; )
F /= 2, O /= 2, M >>>= 1;
return (F + M) / O;
};
return D.int32 = function() {
return R.g(4) | 0;
}, D.quick = function() {
return R.g(4) / 4294967296;
}, D.double = D, g(b(R.S), e), (S.pass || k || function(F, O, M, L) {
return L && (L.S && f(L, R), F.state = function() {
return f(R, {});
}), M ? (t10[a] = F, O) : F;
})(D, E, "global" in S ? S.global : this == t10, S.state);
}
function d(w) {
var S, k = w.length, _ = this, E = 0, R = _.i = _.j = 0, D = _.S = [];
for (k || (w = [k++]); E < o; )
D[E] = E++;
for (E = 0; E < o; E++)
D[E] = D[R = c & R + w[E % k] + (S = D[E])], D[R] = S;
(_.g = function(F) {
for (var O, M = 0, L = _.i, B = _.j, z = _.S; F--; )
O = z[L = c & L + 1], M = M * o + z[c & (z[L] = z[B = c & B + O]) + (z[B] = O)];
return _.i = L, _.j = B, M;
})(o);
}
function f(w, S) {
return S.i = w.i, S.j = w.j, S.S = w.S.slice(), S;
}
function h(w, S) {
var k = [], _ = typeof w, E;
if (S && _ == "object")
for (E in w)
try {
k.push(h(w[E], S - 1));
} catch (R) {
}
return k.length ? k : _ == "string" ? w : w + "\0";
}
function g(w, S) {
for (var k = w + "", _, E = 0; E < k.length; )
S[c & E] = c & (_ ^= S[c & E] * 19) + k.charCodeAt(E++);
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throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), V;
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if (ue + 3 >= Le)
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return ee;
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function Vs() {
if (u.preRun)
for (typeof u.preRun == "function" && (u.preRun = [u.preRun]); u.preRun.length; )
el(u.preRun.shift());
ol(Zr);
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function Xt() {
Fa = true, !S && ol(Jr);
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function Pa() {
if (!S) {
if (u.postRun)
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jv(u.postRun.shift());
ol(fr);
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function el(A) {
Zr.unshift(A);
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function tl(A) {
Jr.unshift(A);
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function jv(A) {
fr.unshift(A);
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wi++, u.monitorRunDependencies && u.monitorRunDependencies(wi);
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var V = Oa;
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function Su(A) {
u.onAbort && u.onAbort(A), A = "Aborted(" + A + ")", j(A), le = true, ye = 1, A += ". Build with -sASSERTIONS for more info.";
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function ym(A) {
return A.startsWith(my);
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function Ap(A) {
return A.startsWith("file://");
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var hr;
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function bm(A) {
try {
if (A == hr && ne)
return new Uint8Array(ne);
if (D)
return D(A);
throw "both async and sync fetching of the wasm failed";
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Su(V);
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}
function dy() {
if (!ne && (x || b)) {
if (typeof fetch == "function" && !Ap(hr))
return fetch(hr, { credentials: "same-origin" }).then(function(A) {
if (!A.ok)
throw "failed to load wasm binary file at '" + hr + "'";
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return bm(hr);
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if (R)
return new Promise(function(A, V) {
R(hr, function(ue) {
A(new Uint8Array(ue));
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function fy() {
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Me.loadWasmModuleToWorker(La, function() {
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V(he.instance, he.module);
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j("failed to asynchronously prepare wasm: " + Ne), Su(Ne);
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function Be() {
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var Ne = WebAssembly.instantiateStreaming(he, A);
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return j("wasm streaming compile failed: " + Ft), j("falling back to ArrayBuffer instantiation"), Ee(ue);
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}) : Ee(ue);
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try {
var Le = u.instantiateWasm(A, V);
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j("Module.instantiateWasm callback failed with error: " + he), l(he);
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function gy(A) {
var V = Me.pthreads[A];
V.postMessage({ cmd: "cancel" });
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function rl(A) {
var V = Me.pthreads[A];
_e(V), Me.returnWorkerToPool(V);
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function xy(A) {
var V = Me.getNewWorker();
if (!V)
return 6;
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return V.runPthread = () => {
w && V.ref(), V.postMessage(ue, A.transferList), delete V.runPthread;
}, V.loaded && V.runPthread(), 0;
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var wm = { varargs: void 0, get: function() {
wm.varargs += 4;
var A = s()[wm.varargs - 4 >>> 2];
return A;
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var V = Pe(A);
return V;
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if (S)
return Si(1, 1, A);
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function yy(A, V) {
if (ye = A, !V && S)
throw vm(A), "unwind";
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function by(A) {
if (A instanceof Iu || A == "unwind")
return ye;
g(1, A);
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var Me = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], pthreads: {}, init: function() {
S ? Me.initWorker() : Me.initMainThread();
}, initMainThread: function() {
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Me.allocateUnusedWorker();
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ee = false;
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ye = A;
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for (var A of Object.values(Me.pthreads))
Me.returnWorkerToPool(A);
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A.terminate();
Me.unusedWorkers = [];
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var V = A.pthread_ptr;
delete Me.pthreads[V], Me.unusedWorkers.push(A), Me.runningWorkers.splice(Me.runningWorkers.indexOf(A), 1), A.pthread_ptr = 0, w && A.unref(), DC(V);
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Me.tlsInitFunctions.forEach((A) => A());
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var he = Le.data, Ne = he.cmd;
if (A.pthread_ptr && (Me.currentProxiedOperationCallerThread = A.pthread_ptr), he.targetThread && he.targetThread != Lm()) {
var Ft = Me.pthreads[he.targetThread];
Ft ? Ft.postMessage(he, he.transferList) : j('Internal error! Worker sent a message "' + Ne + '" to target pthread ' + he.targetThread + ", but that thread no longer exists!"), Me.currentProxiedOperationCallerThread = void 0;
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var he = "worker sent an error!";
throw j(he + " " + Le.filename + ":" + Le.lineno + ": " + Le.message), Le;
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A.onmessage({ data: Le });
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A.onerror(Le);
}), A.on("detachedExit", function() {
}));
var ue = [], Ee = ["onExit", "onAbort", "print", "printErr"];
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u.hasOwnProperty(Be) && ue.push(Be);
A.postMessage({ cmd: "load", handlers: ue, urlOrBlob: u.mainScriptUrlOrBlob || r, wasmMemory: oe, wasmModule: ie });
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var A, V = _("tfjs-backend-wasm-threaded-simd.worker.js");
A = new Worker(V), Me.unusedWorkers.push(A);
}, getNewWorker: function() {
return Me.unusedWorkers.length == 0 && (Me.allocateUnusedWorker(), Me.loadWasmModuleToWorker(Me.unusedWorkers[0])), Me.unusedWorkers.pop();
} };
u.PThread = Me;
function ol(A) {
for (; A.length > 0; )
A.shift()(u);
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function Cy() {
var A = Lm(), V = s()[A + 52 >>> 2], ue = s()[A + 56 >>> 2], Ee = V - ue;
r0(V, Ee), Bm(V);
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u.establishStackSpace = Cy;
function vm(A) {
if (S)
return Si(2, 0, A);
try {
Im(A);
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by(V);
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var Fp = [];
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var V = Fp[A];
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function Sy(A, V) {
var ue = wy(A)(V);
Mo() ? Me.setExitStatus(ue) : t0(ue);
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u.invokeEntryPoint = Sy;
function Iy(A) {
Me.tlsInitFunctions.push(A);
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function vy(A) {
Zv(A, !b, 1, !x), Me.threadInitTLS();
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function ky(A) {
S ? postMessage({ cmd: "cleanupThread", thread: A }) : rl(A);
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function km(A, V, ue, Ee) {
return S ? Si(3, 1, A, V, ue, Ee) : Nm(A, V, ue, Ee);
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function Nm(A, V, ue, Ee) {
if (typeof SharedArrayBuffer == "undefined")
return j("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6;
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if (S && (Be.length === 0 || Le))
return km(A, V, ue, Ee);
if (Le)
return Le;
var he = { startRoutine: ue, pthread_ptr: A, arg: Ee, transferList: Be };
return S ? (he.cmd = "spawnThread", postMessage(he, Be), 0) : xy(he);
}
function Ny() {
return 65536;
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var Ty = true;
function _y() {
return Ty;
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function nl(A) {
Atomics.store(s(), A >> 2, 1), Lm() && e0(A), Atomics.compareExchange(s(), A >> 2, 1, 0);
}
u.executeNotifiedProxyingQueue = nl;
function $y(A, V, ue, Ee) {
if (A == V)
setTimeout(() => nl(Ee));
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postMessage({ targetThread: A, cmd: "processProxyingQueue", queue: Ee });
else {
var Be = Me.pthreads[A];
if (!Be)
return;
Be.postMessage({ cmd: "processProxyingQueue", queue: Ee });
}
return 1;
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function Ey(A, V, ue) {
return -1;
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function Ry() {
Su("");
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function vu(A) {
vu.shown || (vu.shown = {}), vu.shown[A] || (vu.shown[A] = 1, w && (A = "warning: " + A), j(A));
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function Dy() {
w || b || vu("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
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function Ay() {
return Date.now();
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function Tm() {
return 4294901760;
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function Fy() {
return Tm();
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var sl;
w ? sl = () => {
var A = process.hrtime();
return A[0] * 1e3 + A[1] / 1e6;
} : sl = () => performance.timeOrigin + performance.now();
function Py(A, V, ue) {
o().copyWithin(A >>> 0, V >>> 0, V + ue >>> 0);
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function Oy() {
return w ? eB().cpus().length : navigator.hardwareConcurrency;
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function My(A) {
var V = AC(), ue = A();
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function Si(A, V) {
var ue = arguments.length - 2, Ee = arguments;
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for (var Be = ue, Le = zm(Be * 8), he = Le >> 3, Ne = 0; Ne < ue; Ne++) {
var Ft = Ee[2 + Ne];
p()[he + Ne >>> 0] = Ft;
}
return Jv(A, Be, Le, V);
});
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var al = [];
function Ly(A, V, ue) {
al.length = V;
for (var Ee = ue >> 3, Be = 0; Be < V; Be++)
al[Be] = p()[Ee + Be >>> 0];
var Le = A < 0, he = Le ? Cm[-A - 1] : qy[A];
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function By(A) {
try {
return oe.grow(A - We.byteLength + 65535 >>> 16), Tt(oe.buffer), 1;
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function zy(A) {
var V = o().length;
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return false;
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return false;
let Ee = (Ft, to) => Ft + (to - Ft % to) % to;
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var Le = V * (1 + 0.2 / Be);
Le = Math.min(Le, A + 100663296);
var he = Math.min(ue, Ee(Math.max(A, Le), 65536)), Ne = By(he);
if (Ne)
return true;
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return false;
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function Vy() {
throw "unwind";
}
function _m(A) {
return S ? Si(4, 1, A) : 52;
}
function $m(A, V, ue, Ee, Be) {
return S ? Si(5, 1, A, V, ue, Ee, Be) : 70;
}
var Wy = [null, [], []];
function Uy(A, V) {
var ue = Wy[A];
V === 0 || V === 10 ? ((A === 1 ? U : j)(Fe(ue, 0)), ue.length = 0) : ue.push(V);
}
function Em(A, V, ue, Ee) {
if (S)
return Si(6, 1, A, V, ue, Ee);
for (var Be = 0, Le = 0; Le < ue; Le++) {
var he = a()[V >>> 2], Ne = a()[V + 4 >>> 2];
V += 8;
for (var Ft = 0; Ft < Ne; Ft++)
Uy(A, o()[he + Ft >>> 0]);
Be += Ne;
}
return a()[Ee >>> 2] = Be, 0;
}
function Rm(A) {
var V = u["_" + A];
return V;
}
function Gy(A, V) {
t10().set(A, V >>> 0);
}
function Hy(A, V, ue, Ee, Be) {
var Le = { string: (Mr) => {
var Lp = 0;
if (Mr != null && Mr !== 0) {
var s0 = (Mr.length << 2) + 1;
Lp = zm(s0), lt(Mr, Lp, s0);
}
return Lp;
}, array: (Mr) => {
var Lp = zm(Mr.length);
return Gy(Mr, Lp), Lp;
} };
function he(Mr) {
return V === "string" ? Pe(Mr) : V === "boolean" ? !!Mr : Mr;
}
var Ne = Rm(A), Ft = [], to = 0;
if (Ee)
for (var La = 0; La < Ee.length; La++) {
var n0 = Le[ue[La]];
n0 ? (to === 0 && (to = AC()), Ft[La] = n0(Ee[La])) : Ft[La] = Ee[La];
}
var FC = Ne.apply(null, Ft);
function LU(Mr) {
return to !== 0 && Bm(to), he(Mr);
}
return FC = LU(FC), FC;
}
function Ky(A, V, ue, Ee) {
ue = ue || [];
var Be = ue.every((he) => he === "number" || he === "boolean"), Le = V !== "string";
return Le && Be && !Ee ? Rm(A) : function() {
return Hy(A, V, ue, arguments, Ee);
};
}
Me.init();
var qy = [null, Sm, vm, km, _m, $m, Em], Dm = { __emscripten_init_main_thread_js: vy, __emscripten_thread_cleanup: ky, __pthread_create_js: Nm, _emscripten_default_pthread_stack_size: Ny, _emscripten_get_now_is_monotonic: _y, _emscripten_notify_task_queue: $y, _emscripten_set_offscreencanvas_size: Ey, abort: Ry, emscripten_check_blocking_allowed: Dy, emscripten_date_now: Ay, emscripten_get_heap_max: Fy, emscripten_get_now: sl, emscripten_memcpy_big: Py, emscripten_num_logical_cores: Oy, emscripten_receive_on_main_thread_js: Ly, emscripten_resize_heap: zy, emscripten_unwind_to_js_event_loop: Vy, exit: Im, fd_close: _m, fd_seek: $m, fd_write: Em, memory: oe || u.wasmMemory }, Qv = fy(), jy = u.___wasm_call_ctors = function() {
return (jy = u.___wasm_call_ctors = u.asm.__wasm_call_ctors).apply(null, arguments);
}, Xy = u._init = function() {
return (Xy = u._init = u.asm.init).apply(null, arguments);
}, Yy = u._init_with_threads_count = function() {
return (Yy = u._init_with_threads_count = u.asm.init_with_threads_count).apply(null, arguments);
}, Qy = u._get_threads_count = function() {
return (Qy = u._get_threads_count = u.asm.get_threads_count).apply(null, arguments);
}, Zy = u._register_tensor = function() {
return (Zy = u._register_tensor = u.asm.register_tensor).apply(null, arguments);
}, Jy = u._dispose_data = function() {
return (Jy = u._dispose_data = u.asm.dispose_data).apply(null, arguments);
}, eb = u._dispose = function() {
return (eb = u._dispose = u.asm.dispose).apply(null, arguments);
}, tb = u._Abs = function() {
return (tb = u._Abs = u.asm.Abs).apply(null, arguments);
}, rb = u._Acos = function() {
return (rb = u._Acos = u.asm.Acos).apply(null, arguments);
}, ob = u._Acosh = function() {
return (ob = u._Acosh = u.asm.Acosh).apply(null, arguments);
}, nb = u._Add = function() {
return (nb = u._Add = u.asm.Add).apply(null, arguments);
}, sb = u._AddN = function() {
return (sb = u._AddN = u.asm.AddN).apply(null, arguments);
}, ab = u._All = function() {
return (ab = u._All = u.asm.All).apply(null, arguments);
}, ib = u._Any = function() {
return (ib = u._Any = u.asm.Any).apply(null, arguments);
}, ub = u._ArgMax = function() {
return (ub = u._ArgMax = u.asm.ArgMax).apply(null, arguments);
}, pb = u._ArgMin = function() {
return (pb = u._ArgMin = u.asm.ArgMin).apply(null, arguments);
}, cb = u._Asin = function() {
return (cb = u._Asin = u.asm.Asin).apply(null, arguments);
}, lb = u._Asinh = function() {
return (lb = u._Asinh = u.asm.Asinh).apply(null, arguments);
}, mb = u._Atan = function() {
return (mb = u._Atan = u.asm.Atan).apply(null, arguments);
}, db = u._Atan2 = function() {
return (db = u._Atan2 = u.asm.Atan2).apply(null, arguments);
}, fb = u._Atanh = function() {
return (fb = u._Atanh = u.asm.Atanh).apply(null, arguments);
}, hb = u._AvgPool = function() {
return (hb = u._AvgPool = u.asm.AvgPool).apply(null, arguments);
}, gb = u._AvgPool3D = function() {
return (gb = u._AvgPool3D = u.asm.AvgPool3D).apply(null, arguments);
}, xb = u._AvgPool3DGrad = function() {
return (xb = u._AvgPool3DGrad = u.asm.AvgPool3DGrad).apply(null, arguments);
}, yb = u._BatchMatMul = function() {
return (yb = u._BatchMatMul = u.asm.BatchMatMul).apply(null, arguments);
}, bb = u._Bincount = function() {
return (bb = u._Bincount = u.asm.Bincount).apply(null, arguments);
}, Cb = u._Ceil = function() {
return (Cb = u._Ceil = u.asm.Ceil).apply(null, arguments);
}, wb = u._ClipByValue = function() {
return (wb = u._ClipByValue = u.asm.ClipByValue).apply(null, arguments);
}, Sb = u._Conv2D = function() {
return (Sb = u._Conv2D = u.asm.Conv2D).apply(null, arguments);
}, Ib = u._Conv2DBackpropInput = function() {
return (Ib = u._Conv2DBackpropInput = u.asm.Conv2DBackpropInput).apply(null, arguments);
}, vb = u._Conv3D = function() {
return (vb = u._Conv3D = u.asm.Conv3D).apply(null, arguments);
}, kb = u._Conv3DBackpropFilterV2 = function() {
return (kb = u._Conv3DBackpropFilterV2 = u.asm.Conv3DBackpropFilterV2).apply(null, arguments);
}, Nb = u._Conv3DBackpropInputV2 = function() {
return (Nb = u._Conv3DBackpropInputV2 = u.asm.Conv3DBackpropInputV2).apply(null, arguments);
}, Tb = u._Cos = function() {
return (Tb = u._Cos = u.asm.Cos).apply(null, arguments);
}, _b = u._Cosh = function() {
return (_b = u._Cosh = u.asm.Cosh).apply(null, arguments);
}, $b = u._CropAndResize = function() {
return ($b = u._CropAndResize = u.asm.CropAndResize).apply(null, arguments);
}, Eb = u._Cumprod = function() {
return (Eb = u._Cumprod = u.asm.Cumprod).apply(null, arguments);
}, Rb = u._Cumsum = function() {
return (Rb = u._Cumsum = u.asm.Cumsum).apply(null, arguments);
}, Db = u._DenseBincount = function() {
return (Db = u._DenseBincount = u.asm.DenseBincount).apply(null, arguments);
}, Ab = u._DepthToSpace = function() {
return (Ab = u._DepthToSpace = u.asm.DepthToSpace).apply(null, arguments);
}, Fb = u._DepthwiseConv2dNative = function() {
return (Fb = u._DepthwiseConv2dNative = u.asm.DepthwiseConv2dNative).apply(null, arguments);
}, Pb = u._Diag = function() {
return (Pb = u._Diag = u.asm.Diag).apply(null, arguments);
}, Ob = u._Dilation2D = function() {
return (Ob = u._Dilation2D = u.asm.Dilation2D).apply(null, arguments);
}, Mb = u._Dilation2DBackpropFilter = function() {
return (Mb = u._Dilation2DBackpropFilter = u.asm.Dilation2DBackpropFilter).apply(null, arguments);
}, Lb = u._Dilation2DBackpropInput = function() {
return (Lb = u._Dilation2DBackpropInput = u.asm.Dilation2DBackpropInput).apply(null, arguments);
}, Bb = u._Elu = function() {
return (Bb = u._Elu = u.asm.Elu).apply(null, arguments);
}, zb = u._EluGrad = function() {
return (zb = u._EluGrad = u.asm.EluGrad).apply(null, arguments);
}, Vb = u._Equal = function() {
return (Vb = u._Equal = u.asm.Equal).apply(null, arguments);
}, Wb = u._Exp = function() {
return (Wb = u._Exp = u.asm.Exp).apply(null, arguments);
}, Ub = u._Expm1 = function() {
return (Ub = u._Expm1 = u.asm.Expm1).apply(null, arguments);
}, Gb = u._FlipLeftRight = function() {
return (Gb = u._FlipLeftRight = u.asm.FlipLeftRight).apply(null, arguments);
}, Hb = u._Floor = function() {
return (Hb = u._Floor = u.asm.Floor).apply(null, arguments);
}, Kb = u._FloorDiv = function() {
return (Kb = u._FloorDiv = u.asm.FloorDiv).apply(null, arguments);
}, qb = u._FusedBatchNorm = function() {
return (qb = u._FusedBatchNorm = u.asm.FusedBatchNorm).apply(null, arguments);
}, jb = u._FusedConv2D = function() {
return (jb = u._FusedConv2D = u.asm.FusedConv2D).apply(null, arguments);
}, Xb = u._FusedDepthwiseConv2D = function() {
return (Xb = u._FusedDepthwiseConv2D = u.asm.FusedDepthwiseConv2D).apply(null, arguments);
}, Yb = u._Gather = function() {
return (Yb = u._Gather = u.asm.Gather).apply(null, arguments);
}, Qb = u._GatherNd = function() {
return (Qb = u._GatherNd = u.asm.GatherNd).apply(null, arguments);
}, Zb = u._Greater = function() {
return (Zb = u._Greater = u.asm.Greater).apply(null, arguments);
}, Jb = u._GreaterEqual = function() {
return (Jb = u._GreaterEqual = u.asm.GreaterEqual).apply(null, arguments);
}, eC = u._IsFinite = function() {
return (eC = u._IsFinite = u.asm.IsFinite).apply(null, arguments);
}, tC = u._IsInf = function() {
return (tC = u._IsInf = u.asm.IsInf).apply(null, arguments);
}, rC = u._IsNan = function() {
return (rC = u._IsNan = u.asm.IsNan).apply(null, arguments);
}, oC = u._LRN = function() {
return (oC = u._LRN = u.asm.LRN).apply(null, arguments);
}, nC = u._LRNGrad = function() {
return (nC = u._LRNGrad = u.asm.LRNGrad).apply(null, arguments);
}, sC = u._LeakyRelu = function() {
return (sC = u._LeakyRelu = u.asm.LeakyRelu).apply(null, arguments);
}, aC = u._Less = function() {
return (aC = u._Less = u.asm.Less).apply(null, arguments);
}, iC = u._LessEqual = function() {
return (iC = u._LessEqual = u.asm.LessEqual).apply(null, arguments);
}, uC = u._LinSpace = function() {
return (uC = u._LinSpace = u.asm.LinSpace).apply(null, arguments);
}, pC = u._Log = function() {
return (pC = u._Log = u.asm.Log).apply(null, arguments);
}, cC = u._Log1p = function() {
return (cC = u._Log1p = u.asm.Log1p).apply(null, arguments);
}, lC = u._LogicalAnd = function() {
return (lC = u._LogicalAnd = u.asm.LogicalAnd).apply(null, arguments);
}, mC = u._LogicalNot = function() {
return (mC = u._LogicalNot = u.asm.LogicalNot).apply(null, arguments);
}, dC = u._LogicalOr = function() {
return (dC = u._LogicalOr = u.asm.LogicalOr).apply(null, arguments);
}, fC = u._LogicalXor = function() {
return (fC = u._LogicalXor = u.asm.LogicalXor).apply(null, arguments);
}, hC = u._Max = function() {
return (hC = u._Max = u.asm.Max).apply(null, arguments);
}, gC = u._MaxPool = function() {
return (gC = u._MaxPool = u.asm.MaxPool).apply(null, arguments);
}, xC = u._MaxPool3D = function() {
return (xC = u._MaxPool3D = u.asm.MaxPool3D).apply(null, arguments);
}, yC = u._MaxPool3DGrad = function() {
return (yC = u._MaxPool3DGrad = u.asm.MaxPool3DGrad).apply(null, arguments);
}, bC = u._Maximum = function() {
return (bC = u._Maximum = u.asm.Maximum).apply(null, arguments);
}, CC = u._Mean = function() {
return (CC = u._Mean = u.asm.Mean).apply(null, arguments);
}, wC = u._Min = function() {
return (wC = u._Min = u.asm.Min).apply(null, arguments);
}, SC = u._Minimum = function() {
return (SC = u._Minimum = u.asm.Minimum).apply(null, arguments);
}, IC = u._MirrorPad = function() {
return (IC = u._MirrorPad = u.asm.MirrorPad).apply(null, arguments);
}, vC = u._Multinomial = function() {
return (vC = u._Multinomial = u.asm.Multinomial).apply(null, arguments);
}, kC = u._Multiply = function() {
return (kC = u._Multiply = u.asm.Multiply).apply(null, arguments);
}, NC = u._Neg = function() {
return (NC = u._Neg = u.asm.Neg).apply(null, arguments);
}, TC = u._NonMaxSuppressionV3 = function() {
return (TC = u._NonMaxSuppressionV3 = u.asm.NonMaxSuppressionV3).apply(null, arguments);
}, Am = u._NonMaxSuppressionV4 = function() {
return (Am = u._NonMaxSuppressionV4 = u.asm.NonMaxSuppressionV4).apply(null, arguments);
}, Fm = u._NonMaxSuppressionV5 = function() {
return (Fm = u._NonMaxSuppressionV5 = u.asm.NonMaxSuppressionV5).apply(null, arguments);
}, il = u._NotEqual = function() {
return (il = u._NotEqual = u.asm.NotEqual).apply(null, arguments);
}, _C = u._OneHot = function() {
return (_C = u._OneHot = u.asm.OneHot).apply(null, arguments);
}, $C = u._PadV2 = function() {
return ($C = u._PadV2 = u.asm.PadV2).apply(null, arguments);
}, Pp = u._Pow = function() {
return (Pp = u._Pow = u.asm.Pow).apply(null, arguments);
}, Pm = u._Prelu = function() {
return (Pm = u._Prelu = u.asm.Prelu).apply(null, arguments);
}, Op = u._Prod = function() {
return (Op = u._Prod = u.asm.Prod).apply(null, arguments);
}, Mp = u._RealDiv = function() {
return (Mp = u._RealDiv = u.asm.RealDiv).apply(null, arguments);
}, EC = u._Reciprocal = function() {
return (EC = u._Reciprocal = u.asm.Reciprocal).apply(null, arguments);
}, G = u._Relu = function() {
return (G = u._Relu = u.asm.Relu).apply(null, arguments);
}, ae = u._Relu6 = function() {
return (ae = u._Relu6 = u.asm.Relu6).apply(null, arguments);
}, $e = u._ResizeBilinear = function() {
return ($e = u._ResizeBilinear = u.asm.ResizeBilinear).apply(null, arguments);
}, at = u._ResizeBilinearGrad = function() {
return (at = u._ResizeBilinearGrad = u.asm.ResizeBilinearGrad).apply(null, arguments);
}, _t = u._ResizeNearestNeighbor = function() {
return (_t = u._ResizeNearestNeighbor = u.asm.ResizeNearestNeighbor).apply(null, arguments);
}, $t = u._ResizeNearestNeighborGrad = function() {
return ($t = u._ResizeNearestNeighborGrad = u.asm.ResizeNearestNeighborGrad).apply(null, arguments);
}, Xe = u._Reverse = function() {
return (Xe = u._Reverse = u.asm.Reverse).apply(null, arguments);
}, Ge = u._RotateWithOffset = function() {
return (Ge = u._RotateWithOffset = u.asm.RotateWithOffset).apply(null, arguments);
}, Gt = u._Round = function() {
return (Gt = u._Round = u.asm.Round).apply(null, arguments);
}, eo = u._Rsqrt = function() {
return (eo = u._Rsqrt = u.asm.Rsqrt).apply(null, arguments);
}, Ma = u._ScatterNd = function() {
return (Ma = u._ScatterNd = u.asm.ScatterNd).apply(null, arguments);
}, Om = u._SearchSorted = function() {
return (Om = u._SearchSorted = u.asm.SearchSorted).apply(null, arguments);
}, ul = u._SelectV2 = function() {
return (ul = u._SelectV2 = u.asm.SelectV2).apply(null, arguments);
}, RC = u._Selu = function() {
return (RC = u._Selu = u.asm.Selu).apply(null, arguments);
}, yr = u._Sigmoid = function() {
return (yr = u._Sigmoid = u.asm.Sigmoid).apply(null, arguments);
}, Ii = u._Sign = function() {
return (Ii = u._Sign = u.asm.Sign).apply(null, arguments);
}, Mm = u._Sin = function() {
return (Mm = u._Sin = u.asm.Sin).apply(null, arguments);
}, aU = u._Softmax = function() {
return (aU = u._Softmax = u.asm.Softmax).apply(null, arguments);
}, iU = u._Softplus = function() {
return (iU = u._Softplus = u.asm.Softplus).apply(null, arguments);
}, uU = u._SparseFillEmptyRows = function() {
return (uU = u._SparseFillEmptyRows = u.asm.SparseFillEmptyRows).apply(null, arguments);
}, pU = u._SparseReshape = function() {
return (pU = u._SparseReshape = u.asm.SparseReshape).apply(null, arguments);
}, cU = u._SparseSegmentReduction = function() {
return (cU = u._SparseSegmentReduction = u.asm.SparseSegmentReduction).apply(null, arguments);
}, lU = u._SparseToDense = function() {
return (lU = u._SparseToDense = u.asm.SparseToDense).apply(null, arguments);
}, mU = u._Sqrt = function() {
return (mU = u._Sqrt = u.asm.Sqrt).apply(null, arguments);
}, dU = u._Square = function() {
return (dU = u._Square = u.asm.Square).apply(null, arguments);
}, fU = u._SquaredDifference = function() {
return (fU = u._SquaredDifference = u.asm.SquaredDifference).apply(null, arguments);
}, hU = u._Step = function() {
return (hU = u._Step = u.asm.Step).apply(null, arguments);
}, gU = u._StridedSlice = function() {
return (gU = u._StridedSlice = u.asm.StridedSlice).apply(null, arguments);
}, xU = u._Sub = function() {
return (xU = u._Sub = u.asm.Sub).apply(null, arguments);
}, yU = u._Sum = function() {
return (yU = u._Sum = u.asm.Sum).apply(null, arguments);
}, bU = u._Tan = function() {
return (bU = u._Tan = u.asm.Tan).apply(null, arguments);
}, CU = u._Tanh = function() {
return (CU = u._Tanh = u.asm.Tanh).apply(null, arguments);
}, wU = u._TensorScatterUpdate = function() {
return (wU = u._TensorScatterUpdate = u.asm.TensorScatterUpdate).apply(null, arguments);
}, SU = u._Tile = function() {
return (SU = u._Tile = u.asm.Tile).apply(null, arguments);
}, IU = u._TopK = function() {
return (IU = u._TopK = u.asm.TopK).apply(null, arguments);
}, vU = u._Transform = function() {
return (vU = u._Transform = u.asm.Transform).apply(null, arguments);
}, kU = u._Transpose = function() {
return (kU = u._Transpose = u.asm.Transpose).apply(null, arguments);
}, NU = u.__FusedMatMul = function() {
return (NU = u.__FusedMatMul = u.asm._FusedMatMul).apply(null, arguments);
}, TU = u._malloc = function() {
return (TU = u._malloc = u.asm.malloc).apply(null, arguments);
}, _U = u._free = function() {
return (_U = u._free = u.asm.free).apply(null, arguments);
}, $U = u.__emscripten_tls_init = function() {
return ($U = u.__emscripten_tls_init = u.asm._emscripten_tls_init).apply(null, arguments);
}, Lm = u._pthread_self = function() {
return (Lm = u._pthread_self = u.asm.pthread_self).apply(null, arguments);
}, EU = u.___errno_location = function() {
return (EU = u.___errno_location = u.asm.__errno_location).apply(null, arguments);
}, Zv = u.__emscripten_thread_init = function() {
return (Zv = u.__emscripten_thread_init = u.asm._emscripten_thread_init).apply(null, arguments);
}, RU = u.__emscripten_thread_crashed = function() {
return (RU = u.__emscripten_thread_crashed = u.asm._emscripten_thread_crashed).apply(null, arguments);
}, DU = u._emscripten_main_thread_process_queued_calls = function() {
return (DU = u._emscripten_main_thread_process_queued_calls = u.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
}, AU = u._emscripten_main_browser_thread_id = function() {
return (AU = u._emscripten_main_browser_thread_id = u.asm.emscripten_main_browser_thread_id).apply(null, arguments);
}, Jv = u._emscripten_run_in_main_runtime_thread_js = function() {
return (Jv = u._emscripten_run_in_main_runtime_thread_js = u.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
}, FU = u._emscripten_dispatch_to_thread_ = function() {
return (FU = u._emscripten_dispatch_to_thread_ = u.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
}, e0 = u.__emscripten_proxy_execute_task_queue = function() {
return (e0 = u.__emscripten_proxy_execute_task_queue = u.asm._emscripten_proxy_execute_task_queue).apply(null, arguments);
}, DC = u.__emscripten_thread_free_data = function() {
return (DC = u.__emscripten_thread_free_data = u.asm._emscripten_thread_free_data).apply(null, arguments);
}, t0 = u.__emscripten_thread_exit = function() {
return (t0 = u.__emscripten_thread_exit = u.asm._emscripten_thread_exit).apply(null, arguments);
}, r0 = u._emscripten_stack_set_limits = function() {
return (r0 = u._emscripten_stack_set_limits = u.asm.emscripten_stack_set_limits).apply(null, arguments);
}, AC = u.stackSave = function() {
return (AC = u.stackSave = u.asm.stackSave).apply(null, arguments);
}, Bm = u.stackRestore = function() {
return (Bm = u.stackRestore = u.asm.stackRestore).apply(null, arguments);
}, zm = u.stackAlloc = function() {
return (zm = u.stackAlloc = u.asm.stackAlloc).apply(null, arguments);
}, PU = u.dynCall_iijjiiii = function() {
return (PU = u.dynCall_iijjiiii = u.asm.dynCall_iijjiiii).apply(null, arguments);
}, OU = u.dynCall_jiji = function() {
return (OU = u.dynCall_jiji = u.asm.dynCall_jiji).apply(null, arguments);
};
u.keepRuntimeAlive = Mo, u.wasmMemory = oe, u.cwrap = Ky, u.ExitStatus = Iu, u.PThread = Me;
var Vm;
Oa = function A() {
Vm || o0(), Vm || (Oa = A);
};
function o0(A) {
if (A = A || f, wi > 0)
return;
if (S) {
c(u), Xt(), startWorker(u);
return;
}
if (Vs(), wi > 0)
return;
function V() {
Vm || (Vm = true, u.calledRun = true, !le && (Xt(), c(u), u.onRuntimeInitialized && u.onRuntimeInitialized(), Pa()));
}
u.setStatus ? (u.setStatus("Running..."), setTimeout(function() {
setTimeout(function() {
u.setStatus("");
}, 1), V();
}, 1)) : V();
}
if (u.preInit)
for (typeof u.preInit == "function" && (u.preInit = [u.preInit]); u.preInit.length > 0; )
u.preInit.pop()();
o0();
var Wm;
m && (Wm = { uncaughtException: process.listeners("uncaughtException").filter(function(A) {
return !m.uncaughtException.indexOf(A) > -1;
}), unhandledRejection: process.listeners("unhandledRejection").filter(function(A) {
return !m.unhandledRejection.indexOf(A) > -1;
}) });
var Um;
if (typeof WasmBackendModule != "undefined")
Um = WasmBackendModule;
else if (typeof e != "undefined")
Um = e;
else
throw new Error("Could not find wasm module in post.js");
if (Wm) {
var MU = Um._dispose;
Um._dispose = function() {
MU(), Wm.uncaughtException.forEach(function(A) {
process.removeListener("uncaughtException", A);
}), Wm.unhandledRejection.forEach(function(A) {
process.removeListener("unhandledRejection", A);
});
};
}
return e.ready;
};
})();
typeof Bg == "object" && typeof kv == "object" ? kv.exports = vv : typeof define == "function" && define.amd ? define([], function() {
return vv;
}) : typeof Bg == "object" && (Bg.WasmBackendModuleThreadedSimd = vv);
});
var oB = qt((tAt, rB) => {
rB.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"
");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.startWorker=instance=>{Module=instance;postMessage({"cmd":"loaded"})};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:"callHandler",handler:handler,args:[...arguments]})}}Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module)}else if(e.data.cmd==="run"){Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};`;
});
var nB = qt((zg, Tv) => {
var Nv = (() => {
var r = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0;
return typeof __filename != "undefined" && (r = r || __filename), function(e) {
e = e || {};
var t10 = typeof e != "undefined" ? e : {}, o, n;
t10.ready = new Promise(function(G, ae) {
o = G, n = ae;
});
var s;
typeof process != "undefined" && process.listeners && (s = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") });
var a = Object.assign({}, t10), i = [], p = "./this.program", u = (G, ae) => {
throw ae;
}, c = typeof window == "object", l = typeof importScripts == "function", m = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", d = "";
function f(G) {
return t10.locateFile ? t10.locateFile(G, d) : d + G;
}
var h, g, x, b;
function w(G) {
if (G instanceof Dp)
return;
E("exiting due to exception: " + G);
}
if (m) {
var S = Sv(), k = Iv();
l ? d = k.dirname(d) + "/" : d = __dirname + "/", h = (G, ae) => (G = Vs(G) ? new URL(G) : k.normalize(G), S.readFileSync(G, ae ? void 0 : "utf8")), x = (G) => {
var ae = h(G, true);
return ae.buffer || (ae = new Uint8Array(ae)), ae;
}, g = (G, ae, $e) => {
G = Vs(G) ? new URL(G) : k.normalize(G), S.readFile(G, function(at, _t) {
at ? $e(at) : ae(_t.buffer);
});
}, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), i = process.argv.slice(2), process.on("uncaughtException", function(G) {
if (!(G instanceof Dp))
throw G;
}), process.on("unhandledRejection", function(G) {
throw G;
}), u = (G, ae) => {
if (it())
throw process.exitCode = G, ae;
w(ae), process.exit(G);
}, t10.inspect = function() {
return "[Emscripten Module object]";
};
} else
(c || l) && (l ? d = self.location.href : typeof document != "undefined" && document.currentScript && (d = document.currentScript.src), r && (d = r), d.indexOf("blob:") !== 0 ? d = d.substr(0, d.replace(/[?#].*/, "").lastIndexOf("/") + 1) : d = "", h = (G) => {
var ae = new XMLHttpRequest();
return ae.open("GET", G, false), ae.send(null), ae.responseText;
}, l && (x = (G) => {
var ae = new XMLHttpRequest();
return ae.open("GET", G, false), ae.responseType = "arraybuffer", ae.send(null), new Uint8Array(ae.response);
}), g = (G, ae, $e) => {
var at = new XMLHttpRequest();
at.open("GET", G, true), at.responseType = "arraybuffer", at.onload = () => {
if (at.status == 200 || at.status == 0 && at.response) {
ae(at.response);
return;
}
$e();
}, at.onerror = $e, at.send(null);
}, b = (G) => document.title = G);
var _ = t10.print || console.log.bind(console), E = t10.printErr || console.warn.bind(console);
Object.assign(t10, a), a = null, t10.arguments && (i = t10.arguments), t10.thisProgram && (p = t10.thisProgram), t10.quit && (u = t10.quit);
var R = 4, D;
t10.wasmBinary && (D = t10.wasmBinary);
var F = t10.noExitRuntime || true;
typeof WebAssembly != "object" && fr("no native wasm support detected");
var O, M = false, L;
function B(G, ae) {
G || fr(ae);
}
var z = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0;
function U(G, ae, $e) {
ae >>>= 0;
for (var at = ae + $e, _t = ae; G[_t] && !(_t >= at); )
++_t;
if (_t - ae > 16 && G.buffer && z)
return z.decode(G.subarray(ae, _t));
for (var $t = ""; ae < _t; ) {
var Xe = G[ae++];
if (!(Xe & 128)) {
$t += String.fromCharCode(Xe);
continue;
}
var Ge = G[ae++] & 63;
if ((Xe & 224) == 192) {
$t += String.fromCharCode((Xe & 31) << 6 | Ge);
continue;
}
var Gt = G[ae++] & 63;
if ((Xe & 240) == 224 ? Xe = (Xe & 15) << 12 | Ge << 6 | Gt : Xe = (Xe & 7) << 18 | Ge << 12 | Gt << 6 | G[ae++] & 63, Xe < 65536)
$t += String.fromCharCode(Xe);
else {
var eo = Xe - 65536;
$t += String.fromCharCode(55296 | eo >> 10, 56320 | eo & 1023);
}
}
return $t;
}
function j(G, ae) {
return G >>>= 0, G ? U(ne, G, ae) : "";
}
function H(G, ae, $e, at) {
if ($e >>>= 0, !(at > 0))
return 0;
for (var _t = $e, $t = $e + at - 1, Xe = 0; Xe < G.length; ++Xe) {
var Ge = G.charCodeAt(Xe);
if (Ge >= 55296 && Ge <= 57343) {
var Gt = G.charCodeAt(++Xe);
Ge = 65536 + ((Ge & 1023) << 10) | Gt & 1023;
}
if (Ge <= 127) {
if ($e >= $t)
break;
ae[$e++ >>> 0] = Ge;
} else if (Ge <= 2047) {
if ($e + 1 >= $t)
break;
ae[$e++ >>> 0] = 192 | Ge >> 6, ae[$e++ >>> 0] = 128 | Ge & 63;
} else if (Ge <= 65535) {
if ($e + 2 >= $t)
break;
ae[$e++ >>> 0] = 224 | Ge >> 12, ae[$e++ >>> 0] = 128 | Ge >> 6 & 63, ae[$e++ >>> 0] = 128 | Ge & 63;
} else {
if ($e + 3 >= $t)
break;
ae[$e++ >>> 0] = 240 | Ge >> 18, ae[$e++ >>> 0] = 128 | Ge >> 12 & 63, ae[$e++ >>> 0] = 128 | Ge >> 6 & 63, ae[$e++ >>> 0] = 128 | Ge & 63;
}
}
return ae[$e >>> 0] = 0, $e - _t;
}
function X(G, ae, $e) {
return H(G, ne, ae, $e);
}
var J, re, ne, ee, oe, ie, le, ye, _e;
function ve(G) {
J = G, t10.HEAP8 = re = new Int8Array(G), t10.HEAP16 = ee = new Int16Array(G), t10.HEAP32 = ie = new Int32Array(G), t10.HEAPU8 = ne = new Uint8Array(G), t10.HEAPU16 = oe = new Uint16Array(G), t10.HEAPU32 = le = new Uint32Array(G), t10.HEAPF32 = ye = new Float32Array(G), t10.HEAPF64 = _e = new Float64Array(G);
}
var Fe = t10.INITIAL_MEMORY || 16777216, Pe, st = [], lt = [], We = [], mt = false;
function it() {
return F;
}
function ht() {
if (t10.preRun)
for (typeof t10.preRun == "function" && (t10.preRun = [t10.preRun]); t10.preRun.length; )
Mt(t10.preRun.shift());
Oa(st);
}
function gt() {
mt = true, Oa(lt);
}
function Or() {
if (t10.postRun)
for (typeof t10.postRun == "function" && (t10.postRun = [t10.postRun]); t10.postRun.length; )
or(t10.postRun.shift());
Oa(We);
}
function Mt(G) {
st.unshift(G);
}
function Qr(G) {
lt.unshift(G);
}
function or(G) {
We.unshift(G);
}
var Tt = 0, nr = null, sr = null;
function Zr(G) {
Tt++, t10.monitorRunDependencies && t10.monitorRunDependencies(Tt);
}
function Jr(G) {
if (Tt--, t10.monitorRunDependencies && t10.monitorRunDependencies(Tt), Tt == 0 && (nr !== null && (clearInterval(nr), nr = null), sr)) {
var ae = sr;
sr = null, ae();
}
}
function fr(G) {
t10.onAbort && t10.onAbort(G), G = "Aborted(" + G + ")", E(G), M = true, L = 1, G += ". Build with -sASSERTIONS for more info.";
var ae = new WebAssembly.RuntimeError(G);
throw n(ae), ae;
}
var Fa = "data:application/octet-stream;base64,";
function Mo(G) {
return G.startsWith(Fa);
}
function Vs(G) {
return G.startsWith("file://");
}
var Xt;
Xt = "tfjs-backend-wasm.wasm", Mo(Xt) || (Xt = f(Xt));
function Pa(G) {
try {
if (G == Xt && D)
return new Uint8Array(D);
if (x)
return x(G);
throw "both async and sync fetching of the wasm failed";
} catch (ae) {
fr(ae);
}
}
function el() {
if (!D && (c || l)) {
if (typeof fetch == "function" && !Vs(Xt))
return fetch(Xt, { credentials: "same-origin" }).then(function(G) {
if (!G.ok)
throw "failed to load wasm binary file at '" + Xt + "'";
return G.arrayBuffer();
}).catch(function() {
return Pa(Xt);
});
if (g)
return new Promise(function(G, ae) {
g(Xt, function($e) {
G(new Uint8Array($e));
}, ae);
});
}
return Promise.resolve().then(function() {
return Pa(Xt);
});
}
function tl() {
var G = { env: rl, wasi_snapshot_preview1: rl };
function ae(Xe, Ge) {
var Gt = Xe.exports;
t10.asm = Gt, O = t10.asm.memory, ve(O.buffer), Pe = t10.asm.__indirect_function_table, Qr(t10.asm.__wasm_call_ctors), Jr("wasm-instantiate");
}
Zr("wasm-instantiate");
function $e(Xe) {
ae(Xe.instance);
}
function at(Xe) {
return el().then(function(Ge) {
return WebAssembly.instantiate(Ge, G);
}).then(function(Ge) {
return Ge;
}).then(Xe, function(Ge) {
E("failed to asynchronously prepare wasm: " + Ge), fr(Ge);
});
}
function _t() {
return !D && typeof WebAssembly.instantiateStreaming == "function" && !Mo(Xt) && !Vs(Xt) && !m && typeof fetch == "function" ? fetch(Xt, { credentials: "same-origin" }).then(function(Xe) {
var Ge = WebAssembly.instantiateStreaming(Xe, G);
return Ge.then($e, function(Gt) {
return E("wasm streaming compile failed: " + Gt), E("falling back to ArrayBuffer instantiation"), at($e);
});
}) : at($e);
}
if (t10.instantiateWasm)
try {
var $t = t10.instantiateWasm(G, ae);
return $t;
} catch (Xe) {
E("Module.instantiateWasm callback failed with error: " + Xe), n(Xe);
}
return _t().catch(n), {};
}
var jv, wi;
function Dp(G) {
this.name = "ExitStatus", this.message = "Program terminated with exit(" + G + ")", this.status = G;
}
function Oa(G) {
for (; G.length > 0; )
G.shift()(t10);
}
function ly() {
fr("");
}
function xm() {
return 4294901760;
}
function Su() {
return xm();
}
function my(G, ae, $e) {
ne.copyWithin(G >>> 0, ae >>> 0, ae + $e >>> 0);
}
function ym(G) {
try {
return O.grow(G - J.byteLength + 65535 >>> 16), ve(O.buffer), 1;
} catch (ae) {
}
}
function Ap(G) {
var ae = ne.length;
G = G >>> 0;
var $e = xm();
if (G > $e)
return false;
let at = (Gt, eo) => Gt + (eo - Gt % eo) % eo;
for (var _t = 1; _t <= 4; _t *= 2) {
var $t = ae * (1 + 0.2 / _t);
$t = Math.min($t, G + 100663296);
var Xe = Math.min($e, at(Math.max(G, $t), 65536)), Ge = ym(Xe);
if (Ge)
return true;
}
return false;
}
var hr = { varargs: void 0, get: function() {
hr.varargs += 4;
var G = ie[hr.varargs - 4 >>> 2];
return G;
}, getStr: function(G) {
var ae = j(G);
return ae;
} };
function bm(G) {
return 52;
}
function dy(G, ae, $e, at, _t) {
return 70;
}
var fy = [null, [], []];
function Xv(G, ae) {
var $e = fy[G];
ae === 0 || ae === 10 ? ((G === 1 ? _ : E)(U($e, 0)), $e.length = 0) : $e.push(ae);
}
function Yv(G, ae, $e, at) {
for (var _t = 0, $t = 0; $t < $e; $t++) {
var Xe = le[ae >>> 2], Ge = le[ae + 4 >>> 2];
ae += 8;
for (var Gt = 0; Gt < Ge; Gt++)
Xv(G, ne[Xe + Gt >>> 0]);
_t += Ge;
}
return le[at >>> 2] = _t, 0;
}
function Cm(G) {
var ae = t10["_" + G];
return ae;
}
function Iu(G, ae) {
re.set(G, ae >>> 0);
}
function hy(G, ae, $e, at, _t) {
var $t = { string: (yr) => {
var Ii = 0;
if (yr != null && yr !== 0) {
var Mm = (yr.length << 2) + 1;
Ii = il(Mm), X(yr, Ii, Mm);
}
return Ii;
}, array: (yr) => {
var Ii = il(yr.length);
return Iu(yr, Ii), Ii;
} };
function Xe(yr) {
return ae === "string" ? j(yr) : ae === "boolean" ? !!yr : yr;
}
var Ge = Cm(G), Gt = [], eo = 0;
if (at)
for (var Ma = 0; Ma < at.length; Ma++) {
var Om = $t[$e[Ma]];
Om ? (eo === 0 && (eo = Am()), Gt[Ma] = Om(at[Ma])) : Gt[Ma] = at[Ma];
}
var ul = Ge.apply(null, Gt);
function RC(yr) {
return eo !== 0 && Fm(eo), Xe(yr);
}
return ul = RC(ul), ul;
}
function gy(G, ae, $e, at) {
$e = $e || [];
var _t = $e.every((Xe) => Xe === "number" || Xe === "boolean"), $t = ae !== "string";
return $t && _t && !at ? Cm(G) : function() {
return hy(G, ae, $e, arguments, at);
};
}
var rl = { abort: ly, emscripten_get_heap_max: Su, emscripten_memcpy_big: my, emscripten_resize_heap: Ap, fd_close: bm, fd_seek: dy, fd_write: Yv }, xy = tl(), wm = t10.___wasm_call_ctors = function() {
return (wm = t10.___wasm_call_ctors = t10.asm.__wasm_call_ctors).apply(null, arguments);
}, Sm = t10._init = function() {
return (Sm = t10._init = t10.asm.init).apply(null, arguments);
}, yy = t10._init_with_threads_count = function() {
return (yy = t10._init_with_threads_count = t10.asm.init_with_threads_count).apply(null, arguments);
}, Im = t10._get_threads_count = function() {
return (Im = t10._get_threads_count = t10.asm.get_threads_count).apply(null, arguments);
}, by = t10._register_tensor = function() {
return (by = t10._register_tensor = t10.asm.register_tensor).apply(null, arguments);
}, Me = t10._dispose_data = function() {
return (Me = t10._dispose_data = t10.asm.dispose_data).apply(null, arguments);
}, ol = t10._dispose = function() {
return (ol = t10._dispose = t10.asm.dispose).apply(null, arguments);
}, Cy = t10._Abs = function() {
return (Cy = t10._Abs = t10.asm.Abs).apply(null, arguments);
}, vm = t10._Acos = function() {
return (vm = t10._Acos = t10.asm.Acos).apply(null, arguments);
}, Fp = t10._Acosh = function() {
return (Fp = t10._Acosh = t10.asm.Acosh).apply(null, arguments);
}, wy = t10._Add = function() {
return (wy = t10._Add = t10.asm.Add).apply(null, arguments);
}, Sy = t10._AddN = function() {
return (Sy = t10._AddN = t10.asm.AddN).apply(null, arguments);
}, Iy = t10._All = function() {
return (Iy = t10._All = t10.asm.All).apply(null, arguments);
}, vy = t10._Any = function() {
return (vy = t10._Any = t10.asm.Any).apply(null, arguments);
}, ky = t10._ArgMax = function() {
return (ky = t10._ArgMax = t10.asm.ArgMax).apply(null, arguments);
}, km = t10._ArgMin = function() {
return (km = t10._ArgMin = t10.asm.ArgMin).apply(null, arguments);
}, Nm = t10._Asin = function() {
return (Nm = t10._Asin = t10.asm.Asin).apply(null, arguments);
}, Ny = t10._Asinh = function() {
return (Ny = t10._Asinh = t10.asm.Asinh).apply(null, arguments);
}, Ty = t10._Atan = function() {
return (Ty = t10._Atan = t10.asm.Atan).apply(null, arguments);
}, _y = t10._Atan2 = function() {
return (_y = t10._Atan2 = t10.asm.Atan2).apply(null, arguments);
}, nl = t10._Atanh = function() {
return (nl = t10._Atanh = t10.asm.Atanh).apply(null, arguments);
}, $y = t10._AvgPool = function() {
return ($y = t10._AvgPool = t10.asm.AvgPool).apply(null, arguments);
}, Ey = t10._AvgPool3D = function() {
return (Ey = t10._AvgPool3D = t10.asm.AvgPool3D).apply(null, arguments);
}, Ry = t10._AvgPool3DGrad = function() {
return (Ry = t10._AvgPool3DGrad = t10.asm.AvgPool3DGrad).apply(null, arguments);
}, vu = t10._BatchMatMul = function() {
return (vu = t10._BatchMatMul = t10.asm.BatchMatMul).apply(null, arguments);
}, Dy = t10._Bincount = function() {
return (Dy = t10._Bincount = t10.asm.Bincount).apply(null, arguments);
}, Ay = t10._Ceil = function() {
return (Ay = t10._Ceil = t10.asm.Ceil).apply(null, arguments);
}, Tm = t10._ClipByValue = function() {
return (Tm = t10._ClipByValue = t10.asm.ClipByValue).apply(null, arguments);
}, Fy = t10._Conv2D = function() {
return (Fy = t10._Conv2D = t10.asm.Conv2D).apply(null, arguments);
}, sl = t10._Conv2DBackpropInput = function() {
return (sl = t10._Conv2DBackpropInput = t10.asm.Conv2DBackpropInput).apply(null, arguments);
}, Py = t10._Conv3D = function() {
return (Py = t10._Conv3D = t10.asm.Conv3D).apply(null, arguments);
}, Oy = t10._Conv3DBackpropFilterV2 = function() {
return (Oy = t10._Conv3DBackpropFilterV2 = t10.asm.Conv3DBackpropFilterV2).apply(null, arguments);
}, My = t10._Conv3DBackpropInputV2 = function() {
return (My = t10._Conv3DBackpropInputV2 = t10.asm.Conv3DBackpropInputV2).apply(null, arguments);
}, Si = t10._Cos = function() {
return (Si = t10._Cos = t10.asm.Cos).apply(null, arguments);
}, al = t10._Cosh = function() {
return (al = t10._Cosh = t10.asm.Cosh).apply(null, arguments);
}, Ly = t10._CropAndResize = function() {
return (Ly = t10._CropAndResize = t10.asm.CropAndResize).apply(null, arguments);
}, By = t10._Cumprod = function() {
return (By = t10._Cumprod = t10.asm.Cumprod).apply(null, arguments);
}, zy = t10._Cumsum = function() {
return (zy = t10._Cumsum = t10.asm.Cumsum).apply(null, arguments);
}, Vy = t10._DenseBincount = function() {
return (Vy = t10._DenseBincount = t10.asm.DenseBincount).apply(null, arguments);
}, _m = t10._DepthToSpace = function() {
return (_m = t10._DepthToSpace = t10.asm.DepthToSpace).apply(null, arguments);
}, $m = t10._DepthwiseConv2dNative = function() {
return ($m = t10._DepthwiseConv2dNative = t10.asm.DepthwiseConv2dNative).apply(null, arguments);
}, Wy = t10._Diag = function() {
return (Wy = t10._Diag = t10.asm.Diag).apply(null, arguments);
}, Uy = t10._Dilation2D = function() {
return (Uy = t10._Dilation2D = t10.asm.Dilation2D).apply(null, arguments);
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if (e == null && (e = []), typeof r == "boolean" || typeof r == "number" || typeof r == "string" || Tu(r) || r == null || Pt(r) && t10)
e.push(r);
else if (Array.isArray(r) || Pt(r))
for (let o = 0; o < r.length; ++o)
Es(r[o], e, t10);
else {
let o = -1;
for (let n of Object.keys(r))
/^([1-9]+[0-9]*|0)$/.test(n) && (o = Math.max(o, Number(n)));
for (let n = 0; n <= o; n++)
Es(r[n], e, t10);
}
return e;
}
var Ym = class {
constructor(e, t10) {
this.backendTimer = e, this.logger = t10, t10 == null && (this.logger = new QC());
}
profileKernel(e, t10, o) {
let n, s = () => {
n = o();
}, a, i = Fu();
if (this.backendTimer.timerAvailable())
a = this.backendTimer.time(s);
else {
s();
for (let u of n)
u.dataSync();
a = Promise.resolve({ kernelMs: Fu() - i });
}
if (P().getBool("CHECK_COMPUTATION_FOR_ERRORS"))
for (let u = 0; u < n.length; u++) {
let c = n[u];
c.data().then((l) => {
SG(l, c.dtype, e);
});
}
return { kernelName: e, outputs: n, inputs: t10, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") };
}
logKernelProfile(e) {
let { kernelName: t10, outputs: o, timeMs: n, inputs: s, extraInfo: a } = e;
o.forEach((i) => {
Promise.all([i.data(), n, a]).then((p) => {
this.logger.logKernelProfile(t10, i, p[0], p[1], s, p[2]);
});
});
}
};
function SG(r, e, t10) {
if (e !== "float32")
return false;
for (let o = 0; o < r.length; o++) {
let n = r[o];
if (isNaN(n) || !isFinite(n))
return console.warn(`Found ${n} in the result of '${t10}'`), true;
}
return false;
}
var QC = class {
logKernelProfile(e, t10, o, n, s, a) {
let i = typeof n == "number" ? Nu(`${n}ms`, 9) : n.error, p = Nu(e, 25), u = t10.rank, c = t10.size, l = Nu(t10.shape.toString(), 14), m = "";
for (let d in s) {
let f = s[d];
if (f != null) {
let h = f.shape || t10.shape, g = h.length;
m += `${d}: ${g}D ${g > 0 ? h : ""} `;
}
}
console.log(`%c${p} %c${i} %c${u}D ${l} %c${c} %c${m} %c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
}
};
function _0(r, e, t10) {
let o = {}, n = {};
for (let p = 0; p < e.length; p++)
o[e[p].id] = true;
for (let p = 0; p < r.length; p++) {
let u = r[p], c = u.inputs;
for (let l in c) {
let m = c[l], d = false;
for (let f = 0; f < e.length; f++)
if (o[m.id]) {
u.outputs.forEach((h) => o[h.id] = true), d = true, n[u.id] = true;
break;
}
if (d)
break;
}
}
let s = {};
s[t10.id] = true;
let a = {};
for (let p = r.length - 1; p >= 0; p--) {
let u = r[p], c = u.inputs;
for (let l = 0; l < u.outputs.length; l++)
if (s[u.outputs[l].id]) {
for (let m in c)
s[c[m].id] = true, a[u.id] = true;
break;
}
}
let i = [];
for (let p = 0; p < r.length; p++) {
let u = r[p];
if (n[u.id] && a[u.id]) {
let c = {};
for (let m in u.inputs) {
let d = u.inputs[m];
o[d.id] && (c[m] = d);
}
let l = Object.assign({}, u);
l.inputs = c, l.outputs = u.outputs, i.push(l);
}
}
return i;
}
function $0(r, e, t10, o) {
for (let n = e.length - 1; n >= 0; n--) {
let s = e[n], a = [];
if (s.outputs.forEach((p) => {
let u = r[p.id];
u != null ? a.push(u) : a.push(null);
}), s.gradient == null)
throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);
let i = s.gradient(a);
for (let p in s.inputs) {
if (!(p in i))
throw new Error(`Cannot backprop through input ${p}. Available gradients found: ${Object.keys(i)}.`);
let u = t10(() => i[p]());
if (u.dtype !== "float32")
throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${p} must have 'float32' dtype, but has '${u.dtype}'`);
let c = s.inputs[p];
if (!br(u.shape, c.shape))
throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${p}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`);
if (r[c.id] == null)
r[c.id] = u;
else {
let l = r[c.id];
r[c.id] = o(l, u), l.dispose();
}
}
}
}
var E0 = 20;
var gl = 3;
var ZC = 7;
function R0(r, e, t10, o) {
let n = Us(e), s = IG(r, e, t10, n), a = e.length, i = Qm(r, e, t10, n, s), p = ["Tensor"];
return o && (p.push(` dtype: ${t10}`), p.push(` rank: ${a}`), p.push(` shape: [${e}]`), p.push(" values:")), p.push(i.map((u) => " " + u).join(`
`)), p.join(`
`);
}
function IG(r, e, t10, o) {
let n = Ue(e), s = o[o.length - 1], a = new Array(s).fill(0), i = e.length, p = t10 === "complex64" ? yl(r) : r;
if (i > 1)
for (let u = 0; u < n / s; u++) {
let c = u * s;
for (let l = 0; l < s; l++)
a[l] = Math.max(a[l], xl(p[c + l], 0, t10).length);
}
return a;
}
function xl(r, e, t10) {
let o;
return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(ZC))} + ${parseFloat(r[1].toFixed(ZC))}j` : Bo(r) ? o = `'${r}'` : t10 === "bool" ? o = D0(r) : o = parseFloat(r.toFixed(ZC)).toString(), Nu(o, e);
}
function D0(r) {
return r === 0 ? "false" : "true";
}
function Qm(r, e, t10, o, n, s = true) {
let a = t10 === "complex64" ? 2 : 1, i = e[0], p = e.length;
if (p === 0) {
if (t10 === "complex64") {
let h = yl(r);
return [xl(h[0], 0, t10)];
}
return t10 === "bool" ? [D0(r[0])] : [r[0].toString()];
}
if (p === 1) {
if (i > E0) {
let g = gl * a, x = Array.from(r.slice(0, g)), b = Array.from(r.slice((i - gl) * a, i * a));
return t10 === "complex64" && (x = yl(x), b = yl(b)), ["[" + x.map((w, S) => xl(w, n[S], t10)).join(", ") + ", ..., " + b.map((w, S) => xl(w, n[i - gl + S], t10)).join(", ") + "]"];
}
return ["[" + (t10 === "complex64" ? yl(r) : Array.from(r)).map((g, x) => xl(g, n[x], t10)).join(", ") + "]"];
}
let u = e.slice(1), c = o.slice(1), l = o[0] * a, m = [];
if (i > E0) {
for (let h = 0; h < gl; h++) {
let g = h * l, x = g + l;
m.push(...Qm(r.slice(g, x), u, t10, c, n, false));
}
m.push("...");
for (let h = i - gl; h < i; h++) {
let g = h * l, x = g + l;
m.push(...Qm(r.slice(g, x), u, t10, c, n, h === i - 1));
}
} else
for (let h = 0; h < i; h++) {
let g = h * l, x = g + l;
m.push(...Qm(r.slice(g, x), u, t10, c, n, h === i - 1));
}
let d = p === 2 ? "," : "";
m[0] = "[" + (i > 0 ? m[0] + d : "");
for (let h = 1; h < m.length - 1; h++)
m[h] = " " + m[h] + d;
let f = `,
`;
for (let h = 2; h < p; h++)
f += `
`;
return m[m.length - 1] = " " + m[m.length - 1] + "]" + (s ? "" : f), m;
}
function yl(r) {
let e = [];
for (let t10 = 0; t10 < r.length; t10 += 2)
e.push([r[t10], r[t10 + 1]]);
return e;
}
var tt = class {
constructor(e, t10, o) {
if (this.dtype = t10, this.shape = e.slice(), this.size = Ue(e), o != null) {
let n = o.length;
$(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
}
if (t10 === "complex64")
throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");
this.values = o || Hm(t10, this.size), this.strides = Us(e);
}
set(e, ...t10) {
t10.length === 0 && (t10 = [0]), $(t10.length === this.rank, () => `The number of provided coordinates (${t10.length}) must match the rank (${this.rank})`);
let o = this.locToIndex(t10);
this.values[o] = e;
}
get(...e) {
e.length === 0 && (e = [0]);
let t10 = 0;
for (let n of e) {
if (n < 0 || n >= this.shape[t10]) {
let s = `Requested out of range element at ${e}. Buffer shape=${this.shape}`;
throw new Error(s);
}
t10++;
}
let o = e[e.length - 1];
for (let n = 0; n < e.length - 1; ++n)
o += this.strides[n] * e[n];
return this.values[o];
}
locToIndex(e) {
if (this.rank === 0)
return 0;
if (this.rank === 1)
return e[0];
let t10 = e[e.length - 1];
for (let o = 0; o < e.length - 1; ++o)
t10 += this.strides[o] * e[o];
return t10;
}
indexToLoc(e) {
if (this.rank === 0)
return [];
if (this.rank === 1)
return [e];
let t10 = new Array(this.shape.length);
for (let o = 0; o < t10.length - 1; ++o)
t10[o] = Math.floor(e / this.strides[o]), e -= t10[o] * this.strides[o];
return t10[t10.length - 1] = e, t10;
}
get rank() {
return this.shape.length;
}
toTensor() {
return Rs().makeTensor(this.values, this.shape, this.dtype);
}
};
var Rs = null;
var ec = null;
var vG = null;
function A0(r) {
Rs = r;
}
function F0(r) {
ec = r;
}
function P0(r) {
vG = r;
}
var pt = class {
constructor(e, t10, o, n) {
this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t10 || "float32", this.size = Ue(e), this.strides = Us(e), this.dataId = o, this.id = n, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
}
get rank() {
return this.shape.length;
}
async buffer() {
let e = await this.data();
return ec.buffer(this.shape, this.dtype, e);
}
bufferSync() {
return ec.buffer(this.shape, this.dtype, this.dataSync());
}
async array() {
let e = await this.data();
return ku(this.shape, e, this.dtype === "complex64");
}
arraySync() {
return ku(this.shape, this.dataSync(), this.dtype === "complex64");
}
async data() {
this.throwIfDisposed();
let e = Rs().read(this.dataId);
if (this.dtype === "string") {
let t10 = await e;
try {
return t10.map((o) => Jp(o));
} catch (o) {
throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
}
}
return e;
}
dataToGPU(e) {
return this.throwIfDisposed(), Rs().readToGPU(this.dataId, e);
}
dataSync() {
this.throwIfDisposed();
let e = Rs().readSync(this.dataId);
if (this.dtype === "string")
try {
return e.map((t10) => Jp(t10));
} catch (t10) {
throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
}
return e;
}
async bytes() {
this.throwIfDisposed();
let e = await Rs().read(this.dataId);
return this.dtype === "string" ? e : new Uint8Array(e.buffer);
}
dispose() {
this.isDisposed || (Rs().disposeTensor(this), this.isDisposedInternal = true);
}
get isDisposed() {
return this.isDisposedInternal;
}
throwIfDisposed() {
if (this.isDisposed)
throw new Error("Tensor is disposed.");
}
print(e = false) {
return ec.print(this, e);
}
clone() {
return this.throwIfDisposed(), ec.clone(this);
}
toString(e = false) {
let t10 = this.dataSync();
return R0(t10, this.shape, this.dtype, e);
}
cast(e) {
return this.throwIfDisposed(), ec.cast(this, e);
}
variable(e = true, t10, o) {
return this.throwIfDisposed(), Rs().makeVariable(this, e, t10, o);
}
};
Object.defineProperty(pt, Symbol.hasInstance, { value: (r) => !!r && r.data != null && r.dataSync != null && r.throwIfDisposed != null });
function kG() {
return ll("Tensor", () => pt);
}
kG();
var Qa = class extends pt {
constructor(e, t10, o, n) {
super(e.shape, e.dtype, e.dataId, n), this.trainable = t10, this.name = o;
}
assign(e) {
if (e.dtype !== this.dtype)
throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);
if (!br(e.shape, this.shape))
throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);
Rs().disposeTensor(this), this.dataId = e.dataId, Rs().incRef(this, null);
}
dispose() {
Rs().disposeVariable(this), this.isDisposedInternal = true;
}
};
Object.defineProperty(Qa, Symbol.hasInstance, { value: (r) => r instanceof pt && r.assign != null && r.assign instanceof Function });
var M0 = {};
He(M0, { assertTypesMatch: () => nw, getTensorsInContainer: () => bl, isTensorInList: () => TG, makeTypesMatch: () => Oe });
var JC;
(function(r) {
r.R0 = "R0", r.R1 = "R1", r.R2 = "R2", r.R3 = "R3", r.R4 = "R4", r.R5 = "R5", r.R6 = "R6";
})(JC || (JC = {}));
var ew;
(function(r) {
r.float32 = "float32", r.int32 = "int32", r.bool = "int32", r.complex64 = "complex64";
})(ew || (ew = {}));
var tw;
(function(r) {
r.float32 = "float32", r.int32 = "int32", r.bool = "bool", r.complex64 = "complex64";
})(tw || (tw = {}));
var rw;
(function(r) {
r.float32 = "float32", r.int32 = "float32", r.bool = "float32", r.complex64 = "complex64";
})(rw || (rw = {}));
var ow;
(function(r) {
r.float32 = "complex64", r.int32 = "complex64", r.bool = "complex64", r.complex64 = "complex64";
})(ow || (ow = {}));
var NG = { float32: rw, int32: ew, bool: tw, complex64: ow };
function dt(r, e) {
if (r === "string" || e === "string") {
if (r === "string" && e === "string")
return "string";
throw new Error(`Can not upcast ${r} with ${e}`);
}
return NG[r][e];
}
function Za(r) {
return dt(r, "int32");
}
function Zm(r) {
return r != null && typeof r == "object" && "texture" in r && r.texture instanceof WebGLTexture;
}
function Jm(r) {
return typeof GPUBuffer != "undefined" && r != null && typeof r == "object" && "buffer" in r && r.buffer instanceof GPUBuffer;
}
function Oe(r, e) {
if (r.dtype === e.dtype)
return [r, e];
let t10 = dt(r.dtype, e.dtype);
return [r.cast(t10), e.cast(t10)];
}
function nw(r, e) {
$(r.dtype === e.dtype, () => `The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`);
}
function TG(r, e) {
return e.some((t10) => t10.id === r.id);
}
function bl(r) {
let e = [];
return O0(r, e, /* @__PURE__ */ new Set()), e;
}
function O0(r, e, t10) {
if (r == null)
return;
if (r instanceof pt) {
e.push(r);
return;
}
if (!_G(r))
return;
let o = r;
for (let n in o) {
let s = o[n];
t10.has(s) || (t10.add(s), O0(s, e, t10));
}
}
function _G(r) {
return Array.isArray(r) || typeof r == "object";
}
function sw(r) {
return r.kernelName != null;
}
var ed = class {
constructor() {
this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() {
return Array.from(new Set(this.kernels.map((e) => e.name)));
} };
}
dispose() {
for (let e in this.registeredVariables)
this.registeredVariables[e].dispose();
}
};
var Qi = class {
constructor(e) {
this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new ed();
}
async ready() {
if (this.pendingBackendInit != null)
return this.pendingBackendInit.then(() => {
});
if (this.backendInstance != null)
return;
let e = this.getSortedBackends();
for (let t10 = 0; t10 < e.length; t10++) {
let o = e[t10];
if (await this.initializeBackend(o).success) {
await this.setBackend(o);
return;
}
}
throw new Error("Could not initialize any backends, all backend initializations failed.");
}
get backend() {
if (this.pendingBackendInit != null)
throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
if (this.backendInstance == null) {
let { name: e, asyncInit: t10 } = this.initializeBackendsAndReturnBest();
if (t10)
throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
this.setBackend(e);
}
return this.backendInstance;
}
backendNames() {
return Object.keys(this.registryFactory);
}
findBackend(e) {
if (!(e in this.registry))
if (e in this.registryFactory) {
let { asyncInit: t10 } = this.initializeBackend(e);
if (t10)
return null;
} else
return null;
return this.registry[e];
}
findBackendFactory(e) {
return e in this.registryFactory ? this.registryFactory[e].factory : null;
}
registerBackend(e, t10, o = 1) {
return e in this.registryFactory ? (ha(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t10, priority: o }, true);
}
async setBackend(e) {
if (this.registryFactory[e] == null)
throw new Error(`Backend name '${e}' not found in registry`);
if (this.backendName = e, this.registry[e] == null) {
this.backendInstance = null;
let { success: t10, asyncInit: o } = this.initializeBackend(e);
if (!(o ? await t10 : t10))
return false;
}
return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new Ym(this.backendInstance), true;
}
setupRegisteredKernels() {
Km(this.backendName).forEach((t10) => {
t10.setupFunc != null && t10.setupFunc(this.backendInstance);
});
}
disposeRegisteredKernels(e) {
Km(e).forEach((o) => {
o.disposeFunc != null && o.disposeFunc(this.registry[e]);
});
}
initializeBackend(e) {
let t10 = this.registryFactory[e];
if (t10 == null)
throw new Error(`Cannot initialize backend ${e}, no registration found.`);
try {
let o = t10.factory();
if (o && !(o instanceof ro) && typeof o.then == "function") {
let n = ++this.pendingBackendInitId, s = o.then((a) => n < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (n < this.pendingBackendInitId || (this.pendingBackendInit = null, ha(`Initialization of backend ${e} failed`), ha(a.stack || a.message)), false));
return this.pendingBackendInit = s, { success: s, asyncInit: true };
} else
return this.registry[e] = o, { success: true, asyncInit: false };
} catch (o) {
return ha(`Initialization of backend ${e} failed`), ha(o.stack || o.message), { success: false, asyncInit: false };
}
}
removeBackend(e) {
if (!(e in this.registryFactory))
throw new Error(`${e} backend not found in registry`);
this.backendName === e && this.pendingBackendInit != null && this.pendingBackendInitId++, e in this.registry && (this.disposeRegisteredKernels(e), this.registry[e].dispose(), delete this.registry[e]), delete this.registryFactory[e], this.backendName === e && (this.pendingBackendInit = null, this.backendName = null, this.backendInstance = null);
}
getSortedBackends() {
if (Object.keys(this.registryFactory).length === 0)
throw new Error("No backend found in registry.");
return Object.keys(this.registryFactory).sort((e, t10) => this.registryFactory[t10].priority - this.registryFactory[e].priority);
}
initializeBackendsAndReturnBest() {
let e = this.getSortedBackends();
for (let t10 = 0; t10 < e.length; t10++) {
let o = e[t10], { success: n, asyncInit: s } = this.initializeBackend(o);
if (s || n)
return { name: o, asyncInit: s };
}
throw new Error("Could not initialize any backends, all backend initializations failed.");
}
moveData(e, t10) {
let o = this.state.tensorInfo.get(t10), n = o.backend, s = this.readSync(t10), a = n.refCount(t10);
n.disposeData(t10, true), o.backend = e, e.move(t10, s, o.shape, o.dtype, a), this.shouldCheckForMemLeaks() && this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
}
tidy(e, t10) {
let o = null;
if (t10 == null) {
if (typeof e != "function")
throw new Error("Please provide a function to tidy()");
t10 = e;
} else {
if (typeof e != "string" && !(e instanceof String))
throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
if (typeof t10 != "function")
throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
o = e;
}
let n;
return this.scopedRun(() => this.startScope(o), () => this.endScope(n), () => (n = t10(), n instanceof Promise && console.error("Cannot return a Promise inside of tidy."), n));
}
scopedRun(e, t10, o) {
e();
try {
let n = o();
return t10(), n;
} catch (n) {
throw t10(), n;
}
}
nextTensorId() {
return Qi.nextTensorId++;
}
nextVariableId() {
return Qi.nextVariableId++;
}
clone(e) {
let t10 = T.runKernel(xo, { x: e }), o = { x: e }, n = (a) => ({ x: () => {
let i = "float32", p = { x: a }, u = { dtype: i };
return T.runKernel(ho, p, u);
} }), s = [];
return this.addTapeNode(this.state.activeScope.name, o, [t10], n, s, {}), t10;
}
runKernel(e, t10, o) {
if (this.backendName == null && this.backend, !(fl(e, this.backendName) != null))
throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);
return this.runKernelFunc({ kernelName: e, inputs: t10, attrs: o });
}
shouldCheckForMemLeaks() {
return this.ENV.getBool("IS_TEST");
}
checkKernelForMemLeak(e, t10, o) {
let n = this.backend.numDataIds(), s = 0;
o.forEach((p) => {
s += p.dtype === "complex64" ? 3 : 1;
});
let a = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1], i = n - t10 - s - a;
if (i > 0)
throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`);
}
runKernelFunc(e) {
let t10, o = [], n = this.isTapeOn(), s = this.state.numBytes, a = this.state.numTensors;
this.shouldCheckForMemLeaks() && this.state.numDataMovesStack.push(0);
let i;
this.backendName == null && this.backend;
let p, u = sw(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
if (sw(e)) {
let { kernelName: f, inputs: h, attrs: g } = e;
this.backendName == null && this.backend;
let x = fl(f, this.backendName);
$(x != null, () => `Cannot find registered kernel '${f}' for backend '${this.backendName}'`), i = () => {
let b = this.backend.numDataIds();
p = x.kernelFunc({ inputs: h, attrs: g, backend: this.backend });
let w = Array.isArray(p) ? p : [p];
this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(f, b, w);
let S = w.map((k) => k.rank != null ? k : this.makeTensorFromTensorInfo(k));
if (n) {
let k = this.getTensorsForGradient(f, h, S);
o = this.saveTensorsForBackwardMode(k);
}
return S;
};
} else {
let { forwardFunc: f } = e, h = (g) => {
n && (o = g.map((x) => this.keep(this.clone(x))));
};
i = () => {
let g = this.backend.numDataIds();
p = this.tidy(() => f(this.backend, h));
let x = Array.isArray(p) ? p : [p];
return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, g, x), x;
};
}
let { inputs: c, attrs: l } = e, m = sw(e) ? null : e.backwardsFunc, d;
return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
!this.ENV.getBool("DEBUG") && !this.state.profiling ? t10 = i() : (d = this.profiler.profileKernel(u, c, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t10 = d.outputs);
}), n && this.addTapeNode(u, c, t10, m, o, l), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - s, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(c).map((f) => c[f] != null ? c[f].shape : null), outputShapes: t10.map((f) => f.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(p) ? t10 : t10[0];
}
saveTensorsForBackwardMode(e) {
return e.map((o) => this.keep(this.clone(o)));
}
getTensorsForGradient(e, t10, o) {
let n = HC(e);
if (n != null) {
let s = n.inputsToSave || [], a = n.outputsToSave || [], i;
n.saveAllInputs ? ($(Array.isArray(t10), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t10).map((u) => t10[u])) : i = s.map((u) => t10[u]);
let p = o.filter((u, c) => a[c]);
return i.concat(p);
}
return [];
}
makeTensor(e, t10, o, n) {
if (e == null)
throw new Error("Values passed to engine.makeTensor() are null");
o = o || "float32", n = n || this.backend;
let s = e;
o === "string" && Bo(e[0]) && (s = e.map((p) => Yi(p)));
let a = n.write(s, t10, o), i = new pt(t10, o, a, this.nextTensorId());
if (this.trackTensor(i, n), o === "string") {
let p = this.state.tensorInfo.get(a), u = VC(s);
this.state.numBytes += u - p.bytes, p.bytes = u;
}
return i;
}
makeTensorFromDataId(e, t10, o, n) {
o = o || "float32";
let s = { dataId: e, shape: t10, dtype: o };
return this.makeTensorFromTensorInfo(s, n);
}
makeTensorFromTensorInfo(e, t10) {
let { dataId: o, shape: n, dtype: s } = e, a = new pt(n, s, o, this.nextTensorId());
return this.trackTensor(a, t10), a;
}
makeVariable(e, t10 = true, o, n) {
o = o || this.nextVariableId().toString(), n != null && n !== e.dtype && (e = e.cast(n));
let s = new Qa(e, t10, o, this.nextTensorId());
if (this.state.registeredVariables[s.name] != null)
throw new Error(`Variable with name ${s.name} was already registered`);
return this.state.registeredVariables[s.name] = s, this.incRef(s, this.backend), s;
}
trackTensor(e, t10) {
this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++;
let o = 0;
e.dtype !== "complex64" && e.dtype !== "string" && (o = e.size * Vp(e.dtype)), this.state.numBytes += o, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t10 || this.backend, dtype: e.dtype, shape: e.shape, bytes: o })), e instanceof Qa || this.track(e);
}
incRef(e, t10) {
this.trackTensor(e, t10), this.backend.incRef(e.dataId);
}
removeDataId(e, t10) {
this.state.tensorInfo.has(e) && this.state.tensorInfo.get(e).backend === t10 && (this.state.tensorInfo.delete(e), this.state.numDataBuffers--);
}
disposeTensor(e) {
if (!this.state.tensorInfo.has(e.dataId))
return;
let t10 = this.state.tensorInfo.get(e.dataId);
if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t10.bytes), e.dtype !== "complex64" && e.dtype !== "string") {
let o = e.size * Vp(e.dtype);
this.state.numBytes -= o;
}
t10.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t10.backend);
}
disposeVariables() {
for (let e in this.state.registeredVariables) {
let t10 = this.state.registeredVariables[e];
this.disposeVariable(t10);
}
}
disposeVariable(e) {
this.disposeTensor(e), this.state.registeredVariables[e.name] != null && delete this.state.registeredVariables[e.name];
}
memory() {
let e = this.backend.memory();
return e.numTensors = this.state.numTensors, e.numDataBuffers = this.state.numDataBuffers, e.numBytes = this.state.numBytes, this.state.numStringTensors > 0 && (e.unreliable = true, e.reasons == null && (e.reasons = []), e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")), e;
}
async profile(e) {
this.state.profiling = true;
let t10 = this.state.numBytes, o = this.state.numTensors;
this.state.activeProfile.kernels = [], this.state.activeProfile.result = await e(), this.state.profiling = false, this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((n) => n.totalBytesSnapshot)), this.state.activeProfile.newBytes = this.state.numBytes - t10, this.state.activeProfile.newTensors = this.state.numTensors - o;
for (let n of this.state.activeProfile.kernels)
n.kernelTimeMs = await n.kernelTimeMs, n.extraInfo = await n.extraInfo;
return this.state.activeProfile;
}
isTapeOn() {
return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
}
addTapeNode(e, t10, o, n, s, a) {
let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t10, outputs: o, saved: s }, p = HC(e);
p != null && (n = p.gradFunc), n != null && (i.gradient = (u) => (u = u.map((c, l) => {
if (c == null) {
let m = o[l], d = Up(m.size, m.dtype);
return this.makeTensor(d, m.shape, m.dtype);
}
return c;
}), n(u.length > 1 ? u : u[0], s, a))), this.state.activeTape.push(i);
}
keep(e) {
return e.kept = true, e;
}
startTape() {
this.state.gradientDepth === 0 && (this.state.activeTape = []), this.state.gradientDepth++;
}
endTape() {
this.state.gradientDepth--;
}
startScope(e) {
let t10 = { track: [], name: "unnamed scope", id: this.state.nextScopeId++ };
e && (t10.name = e), this.state.scopeStack.push(t10), this.state.activeScope = t10;
}
endScope(e) {
let t10 = bl(e), o = new Set(t10.map((s) => s.id));
for (let s = 0; s < this.state.activeScope.track.length; s++) {
let a = this.state.activeScope.track[s];
!a.kept && !o.has(a.id) && a.dispose();
}
let n = this.state.scopeStack.pop();
this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1], t10.forEach((s) => {
!s.kept && s.scopeId === n.id && this.track(s);
});
}
gradients(e, t10, o, n = false) {
if ($(t10.length > 0, () => "gradients() received an empty list of xs."), o != null && o.dtype !== "float32")
throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);
let s = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e));
$(s instanceof pt, () => "The result y returned by f() must be a tensor.");
let a = _0(this.state.activeTape, t10, s);
if (!n && a.length === 0 && t10.length > 0)
throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
return this.tidy("backward", () => {
let i = {};
i[s.id] = o == null ? $G(s.shape) : o, $0(i, a, (u) => this.tidy(u), EG);
let p = t10.map((u) => i[u.id]);
return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => {
for (let c of u.saved)
c.dispose();
}), this.state.activeTape = null), { value: s, grads: p };
});
}
customGrad(e) {
return $(Ws(e), () => "The f passed in customGrad(f) must be a function."), (...t10) => {
$(t10.every((i) => i instanceof pt), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
let o, n = {};
t10.forEach((i, p) => {
n[p] = i;
});
let s = (i, p) => (o = e(...t10, p), $(o.value instanceof pt, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), $(Ws(o.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), o.value), a = (i, p) => {
let u = o.gradFunc(i, p), c = Array.isArray(u) ? u : [u];
$(c.length === t10.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), $(c.every((m) => m instanceof pt), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
let l = {};
return c.forEach((m, d) => {
l[d] = () => m;
}), l;
};
return this.runKernelFunc({ forwardFunc: s, backwardsFunc: a, inputs: n });
};
}
readSync(e) {
return this.state.tensorInfo.get(e).backend.readSync(e);
}
read(e) {
return this.state.tensorInfo.get(e).backend.read(e);
}
readToGPU(e, t10) {
return this.state.tensorInfo.get(e).backend.readToGPU(e, t10);
}
async time(e) {
let t10 = Fu(), o = await this.backend.time(e);
return o.wallMs = Fu() - t10, o;
}
track(e) {
return this.state.activeScope != null && (e.scopeId = this.state.activeScope.id, this.state.activeScope.track.push(e)), e;
}
get registeredVariables() {
return this.state.registeredVariables;
}
reset() {
this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new ed();
for (let e in this.registry)
this.disposeRegisteredKernels(e), this.registry[e].dispose(), delete this.registry[e];
this.backendName = null, this.backendInstance = null, this.pendingBackendInit = null;
}
};
Qi.nextTensorId = 0;
Qi.nextVariableId = 0;
function $G(r) {
let e = pl(Ue(r), "float32");
return T.makeTensor(e, r, "float32");
}
function aw() {
let r = GC();
if (r._tfengine == null) {
let e = new cl(r);
r._tfengine = new Qi(e);
}
return l0(r._tfengine.ENV), A0(() => r._tfengine), r._tfengine;
}
var T = aw();
function EG(r, e) {
let t10 = { a: r, b: e };
return T.runKernel(no, t10);
}
var Zi = {};
He(Zi, { isBrowser: () => uw, isMobile: () => AG, mockIsMobile: () => DG });
function RG() {
return typeof navigator != "undefined" && navigator != null;
}
var iw;
function DG(r) {
iw = r;
}
function AG(r) {
if (iw !== void 0)
return iw;
if (r || RG()) {
if (r || (r = navigator), r.product === "ReactNative")
return true;
let e = r.userAgent || r.vendor || (typeof window != "undefined" ? window.opera : "");
if (!e) {
let t10 = r;
return t10.userAgentData && t10.userAgentData.mobile;
}
return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0, 4));
}
return false;
}
function uw() {
return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined";
}
var _r = P();
_r.registerFlag("DEBUG", () => false, (r) => {
r && console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
});
_r.registerFlag("IS_BROWSER", () => uw());
_r.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined");
_r.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
_r.registerFlag("IS_SAFARI", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Safari/.test(navigator.userAgent) && /Apple/.test(navigator.vendor));
_r.registerFlag("PROD", () => false);
_r.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => _r.getBool("DEBUG"));
_r.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
_r.registerFlag("IS_TEST", () => false);
_r.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => _r.getBool("DEBUG"));
_r.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
_r.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", () => false);
_r.registerFlag("USE_SETTIMEOUTCUSTOM", () => false);
function ar(r, e) {
let t10 = r;
if (Pt(r))
return e === "string" ? [] : [r.length];
if (Zm(r)) {
let n = r.channels || "RGBA";
return [r.height, r.width * n.length];
} else if (Jm(r))
return [r.buffer.size / (e == null ? 4 : Vp(e))];
if (!Array.isArray(r))
return [];
let o = [];
for (; Array.isArray(t10) || Pt(t10) && e !== "string"; )
o.push(t10.length), t10 = t10[0];
return Array.isArray(r) && P().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && B0(r, o, []), o;
}
function B0(r, e, t10) {
if (t10 = t10 || [], !Array.isArray(r) && !Pt(r)) {
$(e.length === 0, () => `Element arr[${t10.join("][")}] is a primitive, but should be an array/TypedArray of ${e[0]} elements`);
return;
}
$(e.length > 0, () => `Element arr[${t10.join("][")}] should be a primitive, but is an array of ${r.length} elements`), $(r.length === e[0], () => `Element arr[${t10.join("][")}] should have ${e[0]} elements, but has ${r.length} elements`);
let o = e.slice(1);
for (let n = 0; n < r.length; ++n)
B0(r[n], o, t10.concat(n));
}
function L0(r, e, t10, o) {
if (r !== "string_or_numeric") {
if (r == null)
throw new Error("Expected dtype cannot be null.");
if (r !== "numeric" && r !== e || r === "numeric" && e === "string")
throw new Error(`Argument '${t10}' passed to '${o}' must be ${r} tensor, but got ${e} tensor`);
}
}
function v(r, e, t10, o = "numeric") {
if (r instanceof pt)
return L0(o, r.dtype, e, t10), r;
let n = ki(r);
if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), L0(o, n, e, t10), r == null || !Pt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") {
let p = r == null ? "null" : r.constructor.name;
throw new Error(`Argument '${e}' passed to '${t10}' must be a Tensor or TensorLike, but got '${p}'`);
}
let s = ar(r, n);
!Pt(r) && !Array.isArray(r) && (r = [r]);
let i = n !== "string" ? Zp(r, n) : Es(r, [], true);
return T.makeTensor(i, s, n);
}
function Ja(r, e, t10, o = "numeric") {
if (!Array.isArray(r))
throw new Error(`Argument ${e} passed to ${t10} must be a \`Tensor[]\` or \`TensorLike[]\``);
return r.map((s, a) => v(s, `${e}[${a}]`, t10, o));
}
var pw = "__op";
function N(r) {
let e = Object.keys(r);
if (e.length !== 1)
throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${e.length} keys.`);
let t10 = e[0], o = r[t10];
t10.endsWith("_") && (t10 = t10.substring(0, t10.length - 1)), t10 = t10 + pw;
let n = (...s) => {
T.startScope(t10);
try {
let a = o(...s);
return Tu(a) && console.error("Cannot return a Promise inside of tidy."), T.endScope(a), a;
} catch (a) {
throw T.endScope(null), a;
}
};
return Object.defineProperty(n, "name", { value: t10, configurable: true }), n;
}
function FG(r, e) {
let t10 = v(r, "real", "complex"), o = v(e, "imag", "complex");
xt(t10.shape, o.shape, `real and imag shapes, ${t10.shape} and ${o.shape}, must match in call to tf.complex().`);
let n = { real: t10, imag: o };
return T.runKernel(Ti, n);
}
var $r = N({ complex_: FG });
function wr(r, e, t10, o) {
if (o == null)
o = ki(r);
else if (o === "complex64")
throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");
if (Jm(r) || Zm(r)) {
if (o !== "float32" && o !== "int32")
throw new Error(`Creating tensor from GPU data only supports 'float32'|'int32' dtype, while the dtype is ${o}.`);
return T.backend.createTensorFromGPUData(r, e || t10, o);
}
if (!Pt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string")
throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
if (e != null) {
Ct(e);
let n = Ue(e), s = Ue(t10);
$(n === s, () => `Based on the provided shape, [${e}], the tensor should have ${n} values but has ${s}`);
for (let a = 0; a < t10.length; ++a) {
let i = t10[a], p = a === t10.length - 1 ? i !== Ue(e.slice(a)) : true;
$(t10[a] === e[a] || !p, () => `Error creating a new Tensor. Inferred shape (${t10}) does not match the provided shape (${e}). `);
}
}
return !Pt(r) && !Array.isArray(r) && (r = [r]), e = e || t10, r = o !== "string" ? Zp(r, o) : Es(r, [], true), T.makeTensor(r, e, o);
}
function ir(r, e, t10) {
let o = ar(r, t10);
return wr(r, e, o, t10);
}
var Cl = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
var td = 4;
async function V0(r, e) {
let t10 = [], o = [], n = Array.isArray(r) ? r.map((a) => a.name) : Object.keys(r);
for (let a = 0; a < n.length; ++a) {
let i = n[a], p = Array.isArray(r) ? r[a].tensor : r[i];
if (p.dtype !== "float32" && p.dtype !== "int32" && p.dtype !== "bool" && p.dtype !== "string" && p.dtype !== "complex64")
throw new Error(`Unsupported dtype in weight '${i}': ${p.dtype}`);
let u = { name: i, shape: p.shape, dtype: p.dtype };
if (p.dtype === "string") {
let c = new Promise(async (l) => {
let m = await p.bytes(), d = m.reduce((g, x) => g + x.length, 0) + td * m.length, f = new Uint8Array(d), h = 0;
for (let g = 0; g < m.length; g++) {
let x = m[g], b = new Uint8Array(new Uint32Array([x.length]).buffer);
f.set(b, h), h += td, f.set(x, h), h += x.length;
}
l(f);
});
o.push(c);
} else
o.push(p.data());
e != null && (u.group = e), t10.push(u);
}
let s = await Promise.all(o);
return { data: PG(s), specs: t10 };
}
function rd(r, e) {
let t10 = {}, o, n = 0;
for (let s of e) {
let a = s.name, i = s.dtype, p = s.shape, u = Ue(p), c;
if ("quantization" in s) {
let l = s.quantization;
if (l.dtype === "uint8" || l.dtype === "uint16") {
if (!("min" in l && "scale" in l))
throw new Error(`Weight ${s.name} with quantization ${l.dtype} doesn't have corresponding metadata min and scale.`);
} else if (l.dtype === "float16") {
if (i !== "float32")
throw new Error(`Weight ${s.name} is quantized with ${l.dtype} which only supports weights of type float32 not ${i}.`);
} else
throw new Error(`Weight ${s.name} has unknown quantization dtype ${l.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
let m = Cl[l.dtype], d = r.slice(n, n + u * m), f = l.dtype === "uint8" ? new Uint8Array(d) : new Uint16Array(d);
if (i === "float32")
if (l.dtype === "uint8" || l.dtype === "uint16") {
c = new Float32Array(f.length);
for (let h = 0; h < f.length; h++) {
let g = f[h];
c[h] = g * l.scale + l.min;
}
} else if (l.dtype === "float16")
o === void 0 && (o = BG()), c = o(f);
else
throw new Error(`Unsupported quantization type ${l.dtype} for weight type float32.`);
else if (i === "int32") {
if (l.dtype !== "uint8" && l.dtype !== "uint16")
throw new Error(`Unsupported quantization type ${l.dtype} for weight type int32.`);
c = new Int32Array(f.length);
for (let h = 0; h < f.length; h++) {
let g = f[h];
c[h] = Math.round(g * l.scale + l.min);
}
} else
throw new Error(`Unsupported dtype in weight '${a}': ${i}`);
n += u * m;
} else if (i === "string") {
let l = Ue(s.shape);
c = [];
for (let m = 0; m < l; m++) {
let d = new Uint32Array(r.slice(n, n + td))[0];
n += td;
let f = new Uint8Array(r.slice(n, n + d));
c.push(f), n += d;
}
} else {
let l = Cl[i], m = r.slice(n, n + u * l);
if (i === "float32")
c = new Float32Array(m);
else if (i === "int32")
c = new Int32Array(m);
else if (i === "bool")
c = new Uint8Array(m);
else if (i === "complex64") {
c = new Float32Array(m);
let d = new Float32Array(c.length / 2), f = new Float32Array(c.length / 2);
for (let x = 0; x < d.length; x++)
d[x] = c[x * 2], f[x] = c[x * 2 + 1];
let h = ir(d, p, "float32"), g = ir(f, p, "float32");
t10[a] = $r(h, g), h.dispose(), g.dispose();
} else
throw new Error(`Unsupported dtype in weight '${a}': ${i}`);
n += u * l;
}
i !== "complex64" && (t10[a] = ir(c, p, i));
}
return t10;
}
function PG(r) {
if (r === null)
throw new Error(`Invalid input value: ${JSON.stringify(r)}`);
let e = 0, t10 = [];
r.forEach((s) => {
if (e += s.byteLength, t10.push(s.byteLength === s.buffer.byteLength ? s : new s.constructor(s)), !(s instanceof Float32Array || s instanceof Int32Array || s instanceof Uint8Array))
throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`);
});
let o = new Uint8Array(e), n = 0;
return t10.forEach((s) => {
o.set(new Uint8Array(s.buffer), n), n += s.byteLength;
}), o.buffer;
}
var cw = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
function z0(r) {
return cw ? Buffer.byteLength(r) : new Blob([r]).size;
}
function W0(r) {
if (cw)
return Buffer.from(r).toString("base64");
let e = new Uint8Array(r), t10 = "";
for (let o = 0, n = e.length; o < n; o++)
t10 += String.fromCharCode(e[o]);
return btoa(t10);
}
function U0(r) {
if (cw) {
let o = Buffer.from(r, "base64");
return o.buffer.slice(o.byteOffset, o.byteOffset + o.byteLength);
}
let e = atob(r), t10 = new Uint8Array(e.length);
for (let o = 0; o < e.length; ++o)
t10.set([e.charCodeAt(o)], o);
return t10.buffer;
}
function tc(r) {
if (r.length === 1)
return r[0];
let e = 0;
r.forEach((n) => {
e += n.byteLength;
});
let t10 = new Uint8Array(e), o = 0;
return r.forEach((n) => {
t10.set(new Uint8Array(n), o), o += n.byteLength;
}), t10.buffer;
}
function lw(r) {
let e = "/";
for (r = r.trim(); r.endsWith(e); )
r = r.slice(0, r.length - 1);
let t10 = r.split(e);
return t10[t10.length - 1];
}
function od(r, e) {
let t10 = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy, weightsManifest: e };
return r.signature != null && (t10.signature = r.signature), r.userDefinedMetadata != null && (t10.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (t10.modelInitializer = r.modelInitializer), r.initializerSignature != null && (t10.initializerSignature = r.initializerSignature), r.trainingConfig != null && (t10.trainingConfig = r.trainingConfig), t10;
}
function mw(r, e, t10) {
let o = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy };
if (r.trainingConfig != null && (o.trainingConfig = r.trainingConfig), r.weightsManifest != null) {
if (!e)
throw new Error("modelJSON has weightsManifest but weightSpecs is null");
if (!t10)
throw new Error("modelJSON has weightsManifest but weightData is null");
o.weightSpecs = e, o.weightData = t10;
}
return r.signature != null && (o.signature = r.signature), r.userDefinedMetadata != null && (o.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (o.modelInitializer = r.modelInitializer), r.initializerSignature != null && (o.initializerSignature = r.initializerSignature), o;
}
async function rc(r, e) {
let t10, o;
return r.weightsManifest != null && ([t10, o] = await e(r.weightsManifest)), mw(r, t10, o);
}
function ga(r) {
if (r.modelTopology instanceof ArrayBuffer)
throw new Error("Expected JSON model topology, received ArrayBuffer.");
return { dateSaved: /* @__PURE__ */ new Date(), modelTopologyType: "JSON", modelTopologyBytes: r.modelTopology == null ? 0 : z0(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : z0(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : r.weightData.byteLength };
}
function nd(r) {
let e = [];
for (let t10 of r)
e.push(...t10.weights);
return e;
}
function OG() {
let r = (t10) => {
let o = t10 << 13, n = 0;
for (; !(o & 8388608); )
n -= 8388608, o <<= 1;
return o &= -8388609, n += 947912704, o | n;
}, e = new Uint32Array(2048);
e[0] = 0;
for (let t10 = 1; t10 < 1024; t10++)
e[t10] = r(t10);
for (let t10 = 1024; t10 < 2048; t10++)
e[t10] = 939524096 + (t10 - 1024 << 13);
return e;
}
function MG() {
let r = new Uint32Array(64);
r[0] = 0, r[31] = 1199570944, r[32] = 2147483648, r[63] = 3347054592;
for (let e = 1; e < 31; e++)
r[e] = e << 23;
for (let e = 33; e < 63; e++)
r[e] = 2147483648 + (e - 32 << 23);
return r;
}
function LG() {
let r = new Uint32Array(64);
for (let e = 0; e < 64; e++)
r[e] = 1024;
return r[0] = r[32] = 0, r;
}
function BG() {
let r = OG(), e = MG(), t10 = LG();
return (o) => {
let n = new ArrayBuffer(4 * o.length), s = new Uint32Array(n);
for (let a = 0; a < o.length; a++) {
let i = o[a], p = r[t10[i >> 10] + (i & 1023)] + e[i >> 10];
s[a] = p;
}
return new Float32Array(n);
};
}
var ft = class {
constructor() {
this.saveRouters = [], this.loadRouters = [];
}
static getInstance() {
return ft.instance == null && (ft.instance = new ft()), ft.instance;
}
static registerSaveRouter(e) {
ft.getInstance().saveRouters.push(e);
}
static registerLoadRouter(e) {
ft.getInstance().loadRouters.push(e);
}
static getSaveHandlers(e) {
return ft.getHandlers(e, "save");
}
static getLoadHandlers(e, t10) {
return ft.getHandlers(e, "load", t10);
}
static getHandlers(e, t10, o) {
let n = [];
return (t10 === "load" ? ft.getInstance().loadRouters : ft.getInstance().saveRouters).forEach((a) => {
let i = a(e, o);
i !== null && n.push(i);
}), n;
}
};
var G0 = (r) => ft.registerSaveRouter(r);
var H0 = (r) => ft.registerLoadRouter(r);
var K0 = (r) => ft.getSaveHandlers(r);
var q0 = (r, e) => ft.getLoadHandlers(r, e);
var dw = "tensorflowjs";
var fw = 1;
var Pu = "models_store";
var Ji = "model_info_store";
function j0() {
if (!P().getBool("IS_BROWSER"))
throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");
let r = typeof window == "undefined" ? self : window, e = r.indexedDB || r.mozIndexedDB || r.webkitIndexedDB || r.msIndexedDB || r.shimIndexedDB;
if (e == null)
throw new Error("The current browser does not appear to support IndexedDB.");
return e;
}
function hw(r) {
let e = r.result;
e.createObjectStore(Pu, { keyPath: "modelPath" }), e.createObjectStore(Ji, { keyPath: "modelPath" });
}
var xa = class {
constructor(e) {
if (this.indexedDB = j0(), e == null || !e)
throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");
this.modelPath = e;
}
async save(e) {
if (e.modelTopology instanceof ArrayBuffer)
throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
return this.databaseAction(this.modelPath, e);
}
async load() {
return this.databaseAction(this.modelPath);
}
databaseAction(e, t10) {
return new Promise((o, n) => {
let s = this.indexedDB.open(dw, fw);
s.onupgradeneeded = () => hw(s), s.onsuccess = () => {
let a = s.result;
if (t10 == null) {
let i = a.transaction(Pu, "readonly"), u = i.objectStore(Pu).get(this.modelPath);
u.onsuccess = () => {
if (u.result == null)
return a.close(), n(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));
o(u.result.modelArtifacts);
}, u.onerror = (c) => (a.close(), n(u.error)), i.oncomplete = () => a.close();
} else {
let i = ga(t10), p = a.transaction(Ji, "readwrite"), u = p.objectStore(Ji), c;
try {
c = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i });
} catch (m) {
return n(m);
}
let l;
c.onsuccess = () => {
l = a.transaction(Pu, "readwrite");
let m = l.objectStore(Pu), d;
try {
d = m.put({ modelPath: this.modelPath, modelArtifacts: t10, modelArtifactsInfo: i });
} catch (f) {
return n(f);
}
d.onsuccess = () => o({ modelArtifactsInfo: i }), d.onerror = (f) => {
u = p.objectStore(Ji);
let h = u.delete(this.modelPath);
h.onsuccess = () => (a.close(), n(d.error)), h.onerror = (g) => (a.close(), n(d.error));
};
}, c.onerror = (m) => (a.close(), n(c.error)), p.oncomplete = () => {
l == null ? a.close() : l.oncomplete = () => a.close();
};
}
}, s.onerror = (a) => n(s.error);
});
}
};
xa.URL_SCHEME = "indexeddb://";
var X0 = (r) => P().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(xa.URL_SCHEME) ? zG(r.slice(xa.URL_SCHEME.length)) : null;
ft.registerSaveRouter(X0);
ft.registerLoadRouter(X0);
function zG(r) {
return new xa(r);
}
function VG(r) {
return r.startsWith(xa.URL_SCHEME) ? r.slice(xa.URL_SCHEME.length) : r;
}
var sd = class {
constructor() {
this.indexedDB = j0();
}
async listModels() {
return new Promise((e, t10) => {
let o = this.indexedDB.open(dw, fw);
o.onupgradeneeded = () => hw(o), o.onsuccess = () => {
let n = o.result, s = n.transaction(Ji, "readonly"), i = s.objectStore(Ji).getAll();
i.onsuccess = () => {
let p = {};
for (let u of i.result)
p[u.modelPath] = u.modelArtifactsInfo;
e(p);
}, i.onerror = (p) => (n.close(), t10(i.error)), s.oncomplete = () => n.close();
}, o.onerror = (n) => t10(o.error);
});
}
async removeModel(e) {
return e = VG(e), new Promise((t10, o) => {
let n = this.indexedDB.open(dw, fw);
n.onupgradeneeded = () => hw(n), n.onsuccess = () => {
let s = n.result, a = s.transaction(Ji, "readwrite"), i = a.objectStore(Ji), p = i.get(e), u;
p.onsuccess = () => {
if (p.result == null)
return s.close(), o(new Error(`Cannot find model with path '${e}' in IndexedDB.`));
{
let c = i.delete(e), l = () => {
u = s.transaction(Pu, "readwrite");
let d = u.objectStore(Pu).delete(e);
d.onsuccess = () => t10(p.result.modelArtifactsInfo), d.onerror = (f) => o(p.error);
};
c.onsuccess = l, c.onerror = (m) => (l(), s.close(), o(p.error));
}
}, p.onerror = (c) => (s.close(), o(p.error)), a.oncomplete = () => {
u == null ? s.close() : u.oncomplete = () => s.close();
};
}, n.onerror = (s) => o(n.error);
});
}
};
var ei = "/";
var oc = "tensorflowjs_models";
var Y0 = "info";
var WG = "model_topology";
var UG = "weight_specs";
var GG = "weight_data";
var HG = "model_metadata";
function Q0(r) {
return { info: [oc, r, Y0].join(ei), topology: [oc, r, WG].join(ei), weightSpecs: [oc, r, UG].join(ei), weightData: [oc, r, GG].join(ei), modelMetadata: [oc, r, HG].join(ei) };
}
function Z0(r) {
for (let e of Object.values(r))
window.localStorage.removeItem(e);
}
function KG(r) {
let e = r.split(ei);
if (e.length < 3)
throw new Error(`Invalid key format: ${r}`);
return e.slice(1, e.length - 1).join(ei);
}
function qG(r) {
return r.startsWith(ya.URL_SCHEME) ? r.slice(ya.URL_SCHEME.length) : r;
}
var ya = class {
constructor(e) {
if (!P().getBool("IS_BROWSER") || typeof window == "undefined" || typeof window.localStorage == "undefined")
throw new Error("The current environment does not support local storage.");
if (this.LS = window.localStorage, e == null || !e)
throw new Error("For local storage, modelPath must not be null, undefined or empty.");
this.modelPath = e, this.keys = Q0(this.modelPath);
}
async save(e) {
if (e.modelTopology instanceof ArrayBuffer)
throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
{
let t10 = JSON.stringify(e.modelTopology), o = JSON.stringify(e.weightSpecs), n = ga(e);
try {
this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, t10), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, W0(e.weightData));
let s = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, initializerSignature: e.initializerSignature != null ? e.initializerSignature : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(s)), { modelArtifactsInfo: n };
} catch (s) {
throw Z0(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`);
}
}
}
async load() {
let e = JSON.parse(this.LS.getItem(this.keys.info));
if (e == null)
throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);
if (e.modelTopologyType !== "JSON")
throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");
let t10 = {}, o = JSON.parse(this.LS.getItem(this.keys.topology));
if (o == null)
throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);
t10.modelTopology = o;
let n = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
if (n == null)
throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);
t10.weightSpecs = n;
let s = this.LS.getItem(this.keys.modelMetadata);
if (s != null) {
let i = JSON.parse(s);
t10.format = i.format, t10.generatedBy = i.generatedBy, t10.convertedBy = i.convertedBy, i.signature != null && (t10.signature = i.signature), i.userDefinedMetadata != null && (t10.userDefinedMetadata = i.userDefinedMetadata), i.modelInitializer != null && (t10.modelInitializer = i.modelInitializer), i.initializerSignature != null && (t10.initializerSignature = i.initializerSignature), i.trainingConfig != null && (t10.trainingConfig = i.trainingConfig);
}
let a = this.LS.getItem(this.keys.weightData);
if (a == null)
throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);
return t10.weightData = U0(a), t10;
}
};
ya.URL_SCHEME = "localstorage://";
var J0 = (r) => P().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(ya.URL_SCHEME) ? jG(r.slice(ya.URL_SCHEME.length)) : null;
ft.registerSaveRouter(J0);
ft.registerLoadRouter(J0);
function jG(r) {
return new ya(r);
}
var ad = class {
constructor() {
$(P().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), $(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
}
async listModels() {
let e = {}, t10 = oc + ei, o = ei + Y0;
for (let n = 0; n < this.LS.length; ++n) {
let s = this.LS.key(n);
if (s.startsWith(t10) && s.endsWith(o)) {
let a = KG(s);
e[a] = JSON.parse(this.LS.getItem(s));
}
}
return e;
}
async removeModel(e) {
e = qG(e);
let t10 = Q0(e);
if (this.LS.getItem(t10.info) == null)
throw new Error(`Cannot find model at path '${e}'`);
let o = JSON.parse(this.LS.getItem(t10.info));
return Z0(t10), o;
}
};
var nc = "://";
var Qt = class {
constructor() {
this.managers = {};
}
static getInstance() {
return Qt.instance == null && (Qt.instance = new Qt()), Qt.instance;
}
static registerManager(e, t10) {
$(e != null, () => "scheme must not be undefined or null."), e.endsWith(nc) && (e = e.slice(0, e.indexOf(nc))), $(e.length > 0, () => "scheme must not be an empty string.");
let o = Qt.getInstance();
$(o.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), o.managers[e] = t10;
}
static getManager(e) {
let t10 = Qt.getInstance().managers[e];
if (t10 == null)
throw new Error(`Cannot find model manager for scheme '${e}'`);
return t10;
}
static getSchemes() {
return Object.keys(Qt.getInstance().managers);
}
};
function id(r) {
if (r.indexOf(nc) === -1)
throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Qt.getSchemes().join(",")}`);
return { scheme: r.split(nc)[0], path: r.split(nc)[1] };
}
async function ek(r, e, t10 = false) {
$(r !== e, () => `Old path and new path are the same: '${r}'`);
let o = ft.getLoadHandlers(r);
$(o.length > 0, () => `Copying failed because no load handler is found for source URL ${r}.`), $(o.length < 2, () => `Copying failed because more than one (${o.length}) load handlers for source URL ${r}.`);
let n = o[0], s = ft.getSaveHandlers(e);
$(s.length > 0, () => `Copying failed because no save handler is found for destination URL ${e}.`), $(s.length < 2, () => `Copying failed because more than one (${o.length}) save handlers for destination URL ${e}.`);
let a = s[0], i = id(r).scheme, p = id(r).path, u = i === id(r).scheme, c = await n.load();
t10 && u && await Qt.getManager(i).removeModel(p);
let l = await a.save(c);
return t10 && !u && await Qt.getManager(i).removeModel(p), l.modelArtifactsInfo;
}
async function tk() {
let r = Qt.getSchemes(), e = {};
for (let t10 of r) {
let o = await Qt.getManager(t10).listModels();
for (let n in o) {
let s = t10 + nc + n;
e[s] = o[n];
}
}
return e;
}
async function rk(r) {
let e = id(r);
return Qt.getManager(e.scheme).removeModel(e.path);
}
async function ok(r, e) {
return ek(r, e, false);
}
async function nk(r, e) {
return ek(r, e, true);
}
var gw = class {
constructor() {
this.messageName = "setTimeoutCustom", this.functionRefs = [], this.handledMessageCount = 0, this.hasEventListener = false;
}
fetch(e, t10) {
return fetch(e, t10);
}
now() {
return performance.now();
}
encode(e, t10) {
if (t10 !== "utf-8" && t10 !== "utf8")
throw new Error(`Browser's encoder only supports utf-8, but got ${t10}`);
return this.textEncoder == null && (this.textEncoder = new TextEncoder()), this.textEncoder.encode(e);
}
decode(e, t10) {
return new TextDecoder(t10).decode(e);
}
setTimeoutCustom(e, t10) {
if (typeof window == "undefined" || !P().getBool("USE_SETTIMEOUTCUSTOM")) {
setTimeout(e, t10);
return;
}
this.functionRefs.push(e), setTimeout(() => {
window.postMessage({ name: this.messageName, index: this.functionRefs.length - 1 }, "*");
}, t10), this.hasEventListener || (this.hasEventListener = true, window.addEventListener("message", (o) => {
if (o.source === window && o.data.name === this.messageName) {
o.stopPropagation();
let n = this.functionRefs[o.data.index];
n(), this.handledMessageCount++, this.handledMessageCount === this.functionRefs.length && (this.functionRefs = [], this.handledMessageCount = 0);
}
}, true));
}
isTypedArray(e) {
return qm(e);
}
};
if (P().get("IS_BROWSER")) {
P().setPlatform("browser", new gw());
try {
Qt.registerManager(ya.URL_SCHEME, new ad());
} catch (r) {
}
try {
Qt.registerManager(xa.URL_SCHEME, new sd());
} catch (r) {
}
}
var XG = { importFetch: () => sk() };
var xw;
var yw = class {
constructor() {
this.util = ak(), this.textEncoder = new this.util.TextEncoder();
}
fetch(e, t10) {
return P().global.fetch != null ? P().global.fetch(e, t10) : (xw == null && (xw = XG.importFetch()), xw(e, t10));
}
now() {
let e = process.hrtime();
return e[0] * 1e3 + e[1] / 1e6;
}
encode(e, t10) {
if (t10 !== "utf-8" && t10 !== "utf8")
throw new Error(`Node built-in encoder only supports utf-8, but got ${t10}`);
return this.textEncoder.encode(e);
}
decode(e, t10) {
return e.length === 0 ? "" : new this.util.TextDecoder(t10).decode(e);
}
isTypedArray(e) {
return this.util.types.isFloat32Array(e) || this.util.types.isInt32Array(e) || this.util.types.isUint8Array(e) || this.util.types.isUint8ClampedArray(e);
}
};
P().get("IS_NODE") && !P().get("IS_BROWSER") && P().setPlatform("node", new yw());
function me(r, e = "float32", t10) {
return e = e || "float32", Ct(r), new tt(r, e, t10);
}
function YG(r, e) {
let t10 = v(r, "x", "cast");
if (!zC(e))
throw new Error(`Failed to cast to unknown dtype ${e}`);
if (e === "string" && t10.dtype !== "string" || e !== "string" && t10.dtype === "string")
throw new Error("Only strings can be casted to strings");
let o = { x: t10 }, n = { dtype: e };
return T.runKernel(ho, o, n);
}
var Ye = N({ cast_: YG });
function QG(r) {
let t10 = { x: v(r, "x", "clone", "string_or_numeric") };
return T.runKernel(xo, t10);
}
var Vr = N({ clone_: QG });
function ud(r, e = false) {
console.log(r.toString(e));
}
aw();
var ZG = { buffer: me, cast: Ye, clone: Vr, print: ud };
F0(ZG);
function fme() {
P().set("PROD", true);
}
function hme() {
P().set("DEBUG", true);
}
function gme() {
P().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled.");
}
function bw(r) {
P().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(r + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
}
P0(bw);
function xme() {
T.disposeVariables();
}
function ur() {
return T;
}
function yme() {
return T.memory();
}
function bme(r) {
return T.profile(r);
}
function De(r, e) {
return T.tidy(r, e);
}
function Ot(r) {
bl(r).forEach((t10) => t10.dispose());
}
function Er(r) {
return T.keep(r);
}
function Cme(r) {
return T.time(r);
}
function wme(r) {
return T.setBackend(r);
}
function Sme() {
return T.ready();
}
function Ime() {
return T.backendName;
}
function vme(r) {
T.removeBackend(r);
}
function kme(r) {
return T.findBackend(r);
}
function Nme(r) {
return T.findBackendFactory(r);
}
function eu(r, e, t10 = 1) {
return T.registerBackend(r, e, t10);
}
function Tme() {
return T.backend;
}
function _me(r, e) {
P().setPlatform(r, e);
}
function JG(r, e) {
let t10 = v(r, "a", "add"), o = v(e, "b", "add");
[t10, o] = Oe(t10, o);
let n = { a: t10, b: o };
return T.runKernel(no, n);
}
var be = N({ add_: JG });
function e4(r, e) {
let t10 = v(r, "a", "floorDiv"), o = v(e, "b", "floorDiv");
[t10, o] = Oe(t10, o);
let n = { a: t10, b: o };
return T.runKernel(Cn, n);
}
var pd = N({ floorDiv_: e4 });
function t4(r, e) {
let t10 = v(r, "a", "div"), o = v(e, "b", "div");
if ([t10, o] = Oe(t10, o), t10.dtype === "int32" && o.dtype === "int32")
return pd(t10, o);
let n = { a: t10, b: o }, s = {};
return T.runKernel(dn, n, s);
}
var Ke = N({ div_: t4 });
function r4(r, e) {
let t10 = v(r, "a", "mul"), o = v(e, "b", "mul");
[t10, o] = Oe(t10, o);
let n = { a: t10, b: o };
return T.runKernel(Kn, n);
}
var se = N({ mul_: r4 });
function o4(r) {
let e = v(r, "x", "abs");
if (e.dtype === "complex64") {
let t10 = { x: e };
return T.runKernel(_i, t10);
} else {
let t10 = { x: e };
return T.runKernel(Gs, t10);
}
}
var Zt = N({ abs_: o4 });
function n4(r) {
let t10 = { x: v(r, "x", "acos") };
return T.runKernel(zo, t10);
}
var ik = N({ acos_: n4 });
function s4(r) {
let t10 = { x: v(r, "x", "acosh") };
return T.runKernel(Vo, t10);
}
var uk = N({ acosh_: s4 });
function a4(r) {
$(Array.isArray(r), () => "The argument passed to tf.addN() must be a list of tensors"), $(r.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${r.length}`);
let e = r.map((n, s) => v(n, `tensors${s}`, "addN")), t10 = e[0];
e.forEach((n) => {
if (n.dtype !== t10.dtype)
throw new Error("All tensors passed to tf.addN() must have the same dtype");
}), e.forEach((n) => {
if (!br(n.shape, t10.shape))
throw new Error("All tensors passed to tf.addN() must have the same shape");
});
let o = e;
return T.runKernel(Wo, o);
}
var pk = N({ addN_: a4 });
function i4(r, e = null, t10 = false) {
let n = { x: v(r, "x", "all", "bool") }, s = { axis: e, keepDims: t10 };
return T.runKernel(Uo, n, s);
}
var ck = N({ all_: i4 });
function u4(r, e = null, t10 = false) {
let n = { x: v(r, "x", "any", "bool") }, s = { axis: e, keepDims: t10 };
return T.runKernel(Go, n, s);
}
var lk = N({ any_: u4 });
function p4(r, e = 0) {
let o = { x: v(r, "x", "argMax") }, n = { axis: e };
return T.runKernel(Hs, o, n);
}
var mk = N({ argMax_: p4 });
function c4(r, e = 0) {
let o = { x: v(r, "x", "argMin") }, n = { axis: e };
return T.runKernel(Ks, o, n);
}
var dk = N({ argMin_: c4 });
function l4(r) {
let t10 = { x: v(r, "x", "asin") };
return T.runKernel(Ho, t10);
}
var fk = N({ asin_: l4 });
function m4(r) {
let t10 = { x: v(r, "x", "asinh") };
return T.runKernel(Ko, t10);
}
var hk = N({ asinh_: m4 });
function d4(r) {
let t10 = { x: v(r, "x", "atan") };
return T.runKernel(qo, t10);
}
var gk = N({ atan_: d4 });
function f4(r, e) {
let t10 = v(r, "a", "atan2"), o = v(e, "b", "atan2");
[t10, o] = Oe(t10, o);
let n = { a: t10, b: o };
return T.runKernel(Xo, n);
}
var xk = N({ atan2_: f4 });
function h4(r) {
let t10 = { x: v(r, "x", "atanh") };
return T.runKernel(jo, t10);
}
var yk = N({ atanh_: h4 });
function g4(r, e, t10, o, n = "NHWC", s) {
let a = r[3], i = [...e, a], p = Ck(n);
return Mu(r, i, t10, s, o, null, null, p);
}
function ww(r, e, t10, o, n, s, a = "channelsLast") {
let [i, p] = wl(e), u;
if (a === "channelsLast")
u = [i, p, r[3], r[3]];
else if (a === "channelsFirst")
u = [i, p, r[1], r[1]];
else
throw new Error(`Unknown dataFormat ${a}`);
return Mu(r, u, t10, o, n, s, false, a);
}
function x4(r, e, t10, o, n, s, a = "NDHWC") {
let [i, p, u] = Cw(e), c, l;
if (a === "NDHWC")
l = "channelsLast", c = [i, p, u, r[4], r[4]];
else if (a === "NCDHW")
l = "channelsFirst", c = [i, p, u, r[1], r[1]];
else
throw new Error(`Unknown dataFormat ${a}`);
return bk(r, c, t10, o, n, false, l, s);
}
function Mu(r, e, t10, o, n, s, a = false, i = "channelsLast") {
let [p, u, c, l] = [-1, -1, -1, -1];
if (i === "channelsLast")
[p, u, c, l] = r;
else if (i === "channelsFirst")
[p, l, u, c] = r;
else
throw new Error(`Unknown dataFormat ${i}`);
let [m, d, , f] = e, [h, g] = wl(t10), [x, b] = wl(o), w = sc(m, x), S = sc(d, b), { padInfo: k, outHeight: _, outWidth: E } = C4(n, u, c, h, g, w, S, s, i), R = a ? f * l : f, D;
return i === "channelsFirst" ? D = [p, R, _, E] : i === "channelsLast" && (D = [p, _, E, R]), { batchSize: p, dataFormat: i, inHeight: u, inWidth: c, inChannels: l, outHeight: _, outWidth: E, outChannels: R, padInfo: k, strideHeight: h, strideWidth: g, filterHeight: m, filterWidth: d, effectiveFilterHeight: w, effectiveFilterWidth: S, dilationHeight: x, dilationWidth: b, inShape: r, outShape: D, filterShape: e };
}
function bk(r, e, t10, o, n, s = false, a = "channelsLast", i) {
let [p, u, c, l, m] = [-1, -1, -1, -1, -1];
if (a === "channelsLast")
[p, u, c, l, m] = r;
else if (a === "channelsFirst")
[p, m, u, c, l] = r;
else
throw new Error(`Unknown dataFormat ${a}`);
let [d, f, h, , g] = e, [x, b, w] = Cw(t10), [S, k, _] = Cw(o), E = sc(d, S), R = sc(f, k), D = sc(h, _), { padInfo: F, outDepth: O, outHeight: M, outWidth: L } = w4(n, u, c, l, x, b, w, E, R, D, i), B = s ? g * m : g, z;
return a === "channelsFirst" ? z = [p, B, O, M, L] : a === "channelsLast" && (z = [p, O, M, L, B]), { batchSize: p, dataFormat: a, inDepth: u, inHeight: c, inWidth: l, inChannels: m, outDepth: O, outHeight: M, outWidth: L, outChannels: B, padInfo: F, strideDepth: x, strideHeight: b, strideWidth: w, filterDepth: d, filterHeight: f, filterWidth: h, effectiveFilterDepth: E, effectiveFilterHeight: R, effectiveFilterWidth: D, dilationDepth: S, dilationHeight: k, dilationWidth: _, inShape: r, outShape: z, filterShape: e };
}
function y4(r, e, t10, o, n) {
o == null && (o = Sw(r, e, t10));
let s = r[0], a = r[1], i = Sl((s - e + 2 * o) / t10 + 1, n), p = Sl((a - e + 2 * o) / t10 + 1, n);
return [i, p];
}
function b4(r, e, t10, o, n, s) {
n == null && (n = Sw(r, e[0], o[0]));
let a = [0, 0, 0, t10];
for (let i = 0; i < 3; i++)
r[i] + 2 * n >= e[i] && (a[i] = Sl((r[i] - e[i] + 2 * n) / o[i] + 1, s));
return a;
}
function Sw(r, e, t10, o = 1) {
let n = sc(e, o);
return Math.floor((r[0] * (t10 - 1) - t10 + n) / 2);
}
function wl(r) {
return typeof r == "number" ? [r, r, r] : r.length === 2 ? [r[0], r[1], 1] : r;
}
function Cw(r) {
return typeof r == "number" ? [r, r, r] : r;
}
function sc(r, e) {
return e <= 1 ? r : r + (r - 1) * (e - 1);
}
function C4(r, e, t10, o, n, s, a, i, p) {
let u, c, l;
if (typeof r == "number") {
u = { top: r, bottom: r, left: r, right: r, type: r === 0 ? "VALID" : "NUMBER" };
let d = y4([e, t10], s, o, r, i);
c = d[0], l = d[1];
} else if (r === "same") {
c = Math.ceil(e / o), l = Math.ceil(t10 / n);
let m = Math.max(0, (c - 1) * o + s - e), d = Math.max(0, (l - 1) * n + a - t10), f = Math.floor(m / 2), h = m - f, g = Math.floor(d / 2), x = d - g;
u = { top: f, bottom: h, left: g, right: x, type: "SAME" };
} else if (r === "valid")
u = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((e - s + 1) / o), l = Math.ceil((t10 - a + 1) / n);
else if (typeof r == "object") {
let m = p === "channelsLast" ? r[1][0] : r[2][0], d = p === "channelsLast" ? r[1][1] : r[2][1], f = p === "channelsLast" ? r[2][0] : r[3][0], h = p === "channelsLast" ? r[2][1] : r[3][1];
u = { top: m, bottom: d, left: f, right: h, type: m === 0 && d === 0 && f === 0 && h === 0 ? "VALID" : "EXPLICIT" }, c = Sl((e - s + m + d) / o + 1, i), l = Sl((t10 - a + f + h) / n + 1, i);
} else
throw Error(`Unknown padding parameter: ${r}`);
return { padInfo: u, outHeight: c, outWidth: l };
}
function w4(r, e, t10, o, n, s, a, i, p, u, c) {
let l, m, d, f;
if (r === "valid" && (r = 0), typeof r == "number") {
l = { top: r, bottom: r, left: r, right: r, front: r, back: r, type: r === 0 ? "VALID" : "NUMBER" };
let g = b4([e, t10, o, 1], [i, p, u], 1, [n, s, a], r, c);
m = g[0], d = g[1], f = g[2];
} else if (r === "same") {
m = Math.ceil(e / n), d = Math.ceil(t10 / s), f = Math.ceil(o / a);
let h = (m - 1) * n + i - e, g = (d - 1) * s + p - t10, x = (f - 1) * a + u - o, b = Math.floor(h / 2), w = h - b, S = Math.floor(g / 2), k = g - S, _ = Math.floor(x / 2), E = x - _;
l = { top: S, bottom: k, left: _, right: E, front: b, back: w, type: "SAME" };
} else
throw Error(`Unknown padding parameter: ${r}`);
return { padInfo: l, outDepth: m, outHeight: d, outWidth: f };
}
function Sl(r, e) {
if (!e)
return Math.trunc(r);
switch (e) {
case "round":
return Math.round(r);
case "ceil":
return Math.ceil(r);
case "floor":
return Math.floor(r);
default:
throw new Error(`Unknown roundingMode ${e}`);
}
}
function Ou(r) {
let [e, t10, o] = wl(r);
return e === 1 && t10 === 1 && o === 1;
}
function gr(r, e) {
return Ou(r) || Ou(e);
}
function ba(r) {
return wl(r).every((e) => e > 0);
}
function Ck(r) {
if (r === "NHWC")
return "channelsLast";
if (r === "NCHW")
return "channelsFirst";
throw new Error(`Unknown dataFormat ${r}`);
}
function Lt(r, e, t10) {
if (t10 != null) {
if (typeof e == "string")
throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${e}.`);
if (typeof e == "number")
$(Ba(e), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${e}.`);
else if (typeof e == "object")
e.forEach((o) => {
o.forEach((n) => {
$(Ba(n), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${n}.`);
});
});
else
throw Error(`Error in ${r}: Unknown padding parameter: ${e}`);
}
}
function S4(r, e) {
let o = { x: v(r, "x", "reshape", "string_or_numeric") }, n = { shape: e };
return T.runKernel(ia, o, n);
}
var W = N({ reshape_: S4 });
function I4(r, e, t10, o, n) {
let s = v(r, "x", "avgPool", "float32"), a = 1;
$(gr(t10, a), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${t10} and dilations '${a}'`);
let i = s, p = false;
s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${i.rank}.`), Lt("avgPool", o, n);
let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n }, l = T.runKernel(Yo, u, c);
return l = Ye(l, s.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var cd = N({ avgPool_: I4 });
function v4(r, e, t10, o, n, s = "NDHWC") {
let a = v(r, "x", "avgPool3d", "float32"), i = a, p = false;
a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), $(typeof t10 == "number" && t10 > 0 || Array.isArray(t10) && t10[0] > 0 && t10[1] > 0 && t10[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${t10}'`), Lt("avgPool3d", o, n);
let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(qs, u, c);
return l = Ye(l, i.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l;
}
var wk = N({ avgPool3d_: v4 });
function k4(r, e = 0) {
$(r.length >= 1, () => "Pass at least one tensor to concat");
let t10 = Ja(r, "tensors", "concat", "string_or_numeric");
if (t10[0].dtype === "complex64" && t10.forEach((s) => {
if (s.dtype !== "complex64")
throw new Error(`Cannot concatenate complex64 tensors with a tensor
with dtype ${s.dtype}. `);
}), t10.length === 1)
return Vr(t10[0]);
let o = t10, n = { axis: e };
return T.runKernel(Ys, o, n);
}
var yt = N({ concat_: k4 });
function N4(r, e, t10 = false, o = false) {
let n = v(r, "a", "matMul"), s = v(e, "b", "matMul");
[n, s] = Oe(n, s);
let a = { a: n, b: s }, i = { transposeA: t10, transposeB: o };
return T.runKernel(Qo, a, i);
}
var Qe = N({ matMul_: N4 });
function T4(r) {
let t10 = { x: v(r, "x", "sigmoid", "float32") };
return T.runKernel(hs, t10);
}
var wa = N({ sigmoid_: T4 });
function _4(r, e, t10) {
let o = v(r, "x", "slice", "string_or_numeric");
if (o.rank === 0)
throw new Error("Slicing scalar is not possible");
let n = { x: o }, s = { begin: e, size: t10 };
return T.runKernel(pa, n, s);
}
var qe = N({ slice_: _4 });
function $4(r) {
let t10 = { x: v(r, "x", "tanh", "float32") };
return T.runKernel(ks, t10);
}
var Il = N({ tanh_: $4 });
function E4(r, e, t10, o, n, s) {
let a = v(r, "forgetBias", "basicLSTMCell"), i = v(e, "lstmKernel", "basicLSTMCell"), p = v(t10, "lstmBias", "basicLSTMCell"), u = v(o, "data", "basicLSTMCell"), c = v(n, "c", "basicLSTMCell"), l = v(s, "h", "basicLSTMCell"), m = yt([u, l], 1), d = Qe(m, i), f = be(d, p), h = f.shape[0], g = f.shape[1] / 4, x = [h, g], b = qe(f, [0, 0], x), w = qe(f, [0, g], x), S = qe(f, [0, g * 2], x), k = qe(f, [0, g * 3], x), _ = be(se(wa(b), Il(w)), se(c, wa(be(a, S)))), E = se(Il(_), wa(k));
return [_, E];
}
var Sk = N({ basicLSTMCell_: E4 });
function R4(r, e, t10) {
let o = v(r, "x", "batchToSpaceND"), n = e.reduce((i, p) => i * p);
$(o.rank >= 1 + e.length, () => `input rank is ${o.rank} but should be > than blockShape.length ${e.length}`), $(t10.length === e.length, () => `crops.length is ${t10.length} but should be equal to blockShape.length ${e.length}`), $(o.shape[0] % n === 0, () => `input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${n}`);
let s = { x: o }, a = { blockShape: e, crops: t10 };
return T.runKernel(js, s, a);
}
var ld = N({ batchToSpaceND_: R4 });
function Ik(r) {
let e;
return r.rank === 0 || r.rank === 1 ? e = W(r, [1, 1, 1, r.size]) : r.rank === 2 ? e = W(r, [1, 1, r.shape[0], r.shape[1]]) : r.rank === 3 ? e = W(r, [1, r.shape[0], r.shape[1], r.shape[2]]) : e = r, e;
}
function D4(r, e, t10, o, n, s) {
s == null && (s = 1e-3);
let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u;
n != null && (u = v(n, "scale", "batchNorm"));
let c;
o != null && (c = v(o, "offset", "batchNorm")), $(i.rank === p.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), $(c == null || i.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), $(u == null || i.rank === u.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
let m = { x: Ik(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(wn, m, d);
return W(f, a.shape);
}
var tu = N({ batchNorm_: D4 });
function A4(r, e, t10, o, n, s) {
let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u;
n != null && (u = v(n, "scale", "batchNorm"));
let c;
return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`), $(i.rank === 2 || i.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`), $(p.rank === 2 || p.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), tu(a, i, p, c, u, s);
}
var vk = N({ batchNorm2d_: A4 });
function F4(r, e, t10, o, n, s) {
let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u;
n != null && (u = v(n, "scale", "batchNorm"));
let c;
return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`), $(i.rank === 3 || i.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`), $(p.rank === 3 || p.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), tu(a, i, p, c, u, s);
}
var kk = N({ batchNorm3d_: F4 });
function P4(r, e, t10, o, n, s) {
let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u;
n != null && (u = v(n, "scale", "batchNorm"));
let c;
return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`), $(i.rank === 4 || i.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`), $(p.rank === 4 || p.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), tu(a, i, p, c, u, s);
}
var Nk = N({ batchNorm4d_: P4 });
function O4(r, e, t10) {
let o = v(r, "x", "bincount"), n = v(e, "weights", "bincount");
$(o.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${o.dtype}`), $(t10 >= 0, () => `size must be non-negative, but got ${t10}.`), $(n.size === o.size || n.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);
let s = { x: o, weights: n }, a = { size: t10 };
return T.runKernel(Zo, s, a);
}
var md = N({ bincount_: O4 });
function M4(r, e) {
let t10 = v(r, "x", "bitwiseAnd"), o = v(e, "y", "bitwiseAnd");
if (!br(t10.shape, o.shape))
throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${t10.shape}, y: ${o.shape}`);
if (t10.dtype !== "int32" || o.dtype !== "int32")
throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${t10.dtype} and type of y: ${o.dtype}`);
let n = { a: t10, b: o };
return T.runKernel(ml, n);
}
var Tk = N({ bitwiseAnd_: M4 });
function L4(r, e) {
let t10 = v(r, "s0", "broadcastArgs", "int32"), o = v(e, "s1", "broadcastArgs", "int32");
if (t10.rank !== 1)
throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t10.rank}`);
if (o.rank !== 1)
throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`);
let n = { s0: t10, s1: o };
return T.runKernel(Xs, n);
}
var _k = N({ broadcastArgs_: L4 });
function B4(r, e) {
let t10 = v(r, "broadcastTo", "x"), o = t10.shape;
if (Ct(e), e.length < t10.rank)
throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t10.rank}.`);
if (e.length > t10.rank) {
let u = t10.shape.slice();
for (; u.length < e.length; )
u.unshift(1);
t10 = W(t10, u);
}
let n = t10.shape, s = Array.from(e);
for (let u = e.length - 1; u >= 0; u--)
if (n[u] === e[u])
s[u] = 1;
else if (t10.shape[u] !== 1)
throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);
if (s.map((u, c) => u > 1 ? c : -1).filter((u) => u >= 0).length === 0)
return Vr(t10);
let i = { x: t10 }, p = { reps: s };
return T.runKernel(so, i, p);
}
var ru = N({ broadcastTo_: B4 });
function z4(r) {
let t10 = { x: v(r, "x", "ceil", "float32") };
return T.runKernel(Jo, t10);
}
var $k = N({ ceil_: z4 });
function Sa(r, e, t10) {
Ct(r), t10 = t10 || ki(e);
let o = { shape: r, value: e, dtype: t10 };
return T.runKernel(ea, {}, o);
}
function V4(r, e, t10) {
let o = v(r, "x", "clipByValue");
if ($(e <= t10, () => `Error in clip: min (${e}) must be less than or equal to max (${t10}).`), e === t10)
return Sa(o.shape, e, o.dtype);
let n = { x: o }, s = { clipValueMin: e, clipValueMax: t10 };
return T.runKernel(go, n, s);
}
var Ek = N({ clipByValue_: V4 });
function W4(r) {
return yt(r, 0);
}
var Rk = N({ concat1d_: W4 });
function U4(r, e) {
return yt(r, e);
}
var Dk = N({ concat2d_: U4 });
function G4(r, e) {
return yt(r, e);
}
var Ak = N({ concat3d_: G4 });
function H4(r, e) {
return yt(r, e);
}
var Fk = N({ concat4d_: H4 });
function K4(r, e, t10, o, n = "NHWC", s = [1, 1], a) {
let i = v(r, "x", "conv2d", "float32"), p = v(e, "filter", "conv2d", "float32"), u = i, c = false;
i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`), Lt("conv2d", o, a);
let l = n === "NHWC" ? u.shape[3] : u.shape[1];
$(l === p.shape[2], () => `Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`), $(gr(t10, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), $(ba(s), () => "Error in conv2D: Dilated rates should be larger than 0."), $(ba(t10), () => "Error in conv2D: Strides should be larger than 0.");
let m = { x: u, filter: p }, d = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(en, m, d);
return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var ou = N({ conv2d_: K4 });
function q4(r, e, t10, o, n = "NWC", s = 1, a) {
let i = v(r, "x", "conv1d"), p = v(e, "filter", "conv1d"), u = i, c = false;
i.rank === 2 && (c = true, u = W(i, [1, i.shape[0], i.shape[1]])), $(u.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${u.rank}.`), $(p.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`), Lt("conv1d", o, a), $(u.shape[2] === p.shape[1], () => `Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`), $(gr(t10, s), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${t10} and dilation '${s}'`), $(ba(s), () => "Error in conv1D: Dilated rates should be larger than 0."), $(ba(t10), () => "Error in conv1D: Stride should be larger than 0."), $(n === "NWC", () => `Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`);
let l = W(p, [1, p.shape[0], p.shape[1], p.shape[2]]), m = W(u, [u.shape[0], 1, u.shape[1], u.shape[2]]), g = ou(m, l, [1, t10], o, "NHWC", [1, s], a);
return c ? W(g, [g.shape[2], g.shape[3]]) : W(g, [g.shape[0], g.shape[2], g.shape[3]]);
}
var Pk = N({ conv1d_: q4 });
function j4(r, e, t10, o, n, s = "NHWC", a) {
$(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);
let i = r, p = e, u = false;
e.rank === 3 && (u = true, p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]]), i = [1, r[0], r[1], r[2]]), $(i.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`), $(p.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`), $(t10.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${t10.rank}`);
let c = s === "NHWC" ? i[3] : i[1], l = s === "NHWC" ? p.shape[3] : p.shape[1];
$(c === t10.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t10.shape[2]}.`), $(l === t10.shape[3], () => `Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t10.shape[3]}.`), Lt("conv2dDerInput", n, a);
let m = { dy: p, filter: t10 }, d = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, inputShape: i }, f = T.runKernel(tn, m, d);
return u ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var dd = N({ conv2DBackpropInput_: j4 });
function X4(r, e, t10, o, n, s) {
let a = v(r, "x", "conv2dTranspose"), i = v(e, "filter", "conv2dTranspose");
return dd(t10, a, i, o, n, "NHWC", s);
}
var Ok = N({ conv2dTranspose_: X4 });
function Y4(r, e, t10, o, n = "NDHWC", s = [1, 1, 1]) {
let a = v(r, "x", "conv3d"), i = v(e, "filter", "conv3d"), p = a, u = false;
a.rank === 4 && (u = true, p = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(p.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${p.rank}.`), $(i.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`), $(p.shape[4] === i.shape[3], () => `Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`), $(gr(t10, s), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), $(n === "NDHWC", () => `Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`), $(ba(s), () => "Error in conv3D: Dilated rates should be larger than 0."), $(ba(t10), () => "Error in conv3D: Strides should be larger than 0.");
let c = { x: p, filter: i }, l = { strides: t10, pad: o, dataFormat: n, dilations: s }, m = T.runKernel(rn, c, l);
return u ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m;
}
var Mk = N({ conv3d_: Y4 });
function Q4(r, e, t10, o, n) {
$(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);
let s = r, a = e, i = false;
e.rank === 4 && (i = true, a = W(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 p = s[4], u = a.shape[4];
$(s.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`), $(a.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`), $(t10.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${t10.rank}`), $(p === t10.shape[3], () => `Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t10.shape[3]}.`), $(u === t10.shape[4], () => `Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t10.shape[4]}.`);
let c = { dy: a, filter: t10 }, l = { pad: n, strides: o, inputShape: s }, m = T.runKernel(on, c, l);
return i ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m;
}
var Lk = N({ conv3DBackpropInput_: Q4 });
function Z4(r, e, t10, o, n) {
let s = v(r, "x", "conv3dTranspose"), a = v(e, "filter", "conv3dTranspose");
return Lk(t10, s, a, o, n);
}
var Bk = N({ conv3dTranspose_: Z4 });
function J4(r) {
let t10 = { x: v(r, "x", "cos", "float32") };
return T.runKernel(nn, t10);
}
var zk = N({ cos_: J4 });
function eH(r) {
let t10 = { x: v(r, "x", "cosh", "float32") };
return T.runKernel(sn, t10);
}
var Vk = N({ cosh_: eH });
function tH(r, e = 0, t10 = false, o = false) {
let s = { x: v(r, "x", "cumprod") }, a = { axis: e, exclusive: t10, reverse: o };
return T.runKernel(an, s, a);
}
var Wk = N({ cumprod_: tH });
function rH(r, e = 0, t10 = false, o = false) {
let s = { x: v(r, "x", "cumsum") }, a = { axis: e, exclusive: t10, reverse: o };
return T.runKernel(un, s, a);
}
var Uk = N({ cumsum_: rH });
function oH(r, e, t10, o = false) {
let n = v(r, "x", "denseBincount"), s = v(e, "weights", "denseBincount");
$(n.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${n.dtype}`), $(n.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`), $(t10 >= 0, () => `size must be non-negative, but got ${t10}.`), $(s.size === n.size || s.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);
let a = { x: n, weights: s }, i = { size: t10, binaryOutput: o };
return T.runKernel(Qs, a, i);
}
var Gk = N({ denseBincount_: oH });
function nH(r, e, t10 = "NHWC") {
let o = v(r, "x", "depthToSpace", "float32"), n = t10 === "NHWC" ? o.shape[1] : o.shape[2], s = t10 === "NHWC" ? o.shape[2] : o.shape[3], a = t10 === "NHWC" ? o.shape[3] : o.shape[1];
$(e > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${e}`), $(n * e >= 0, () => `Negative dimension size caused by overflow when multiplying
${n} and ${e} for depthToSpace with input shape
${o.shape}`), $(s * e >= 0, () => `Negative dimension size caused by overflow when multiplying
${s} and ${e} for depthToSpace with input shape
${o.shape}`), $(a % (e * e) === 0, () => `Dimension size must be evenly divisible by ${e * e} but is ${a} for depthToSpace with input shape ${o.shape}`);
let i = { x: o }, p = { blockSize: e, dataFormat: t10 };
return T.runKernel(cn, i, p);
}
var Hk = N({ depthToSpace_: nH });
function sH(r, e, t10, o, n = "NHWC", s = [1, 1], a) {
let i = v(r, "x", "depthwiseConv2d", "float32"), p = v(e, "filter", "depthwiseConv2d", "float32"), u = i, c = false;
i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`);
let l = n === "NHWC" ? u.shape[3] : u.shape[1];
$(l === p.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`), Lt("depthwiseConv2d", o, a);
let m = { x: u, filter: p }, d = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(ln, m, d);
return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var ac = N({ depthwiseConv2d_: sH });
function aH(r) {
let t10 = { x: v(r, "x", "diag") };
return T.runKernel(Zs, t10);
}
var Kk = N({ diag_: aH });
function iH(r, e, t10, o, n = [1, 1], s = "NHWC") {
let a = v(r, "x", "dilation2d"), i = v(e, "filter", "dilation2d");
$(a.rank === 3 || a.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`), $(i.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`), $(s === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);
let p = a, u = false;
a.rank === 3 && (p = W(a, [1, a.shape[0], a.shape[1], a.shape[2]]), u = true), $(p.shape[3] === i.shape[2], () => `Error in dilation2d: input and filter must have the same depth: ${p.shape[3]} vs ${i.shape[2]}`);
let c = { x: p, filter: i }, l = { strides: t10, pad: o, dilations: n }, m = T.runKernel(mn, c, l);
return u ? W(m, [m.shape[1], m.shape[2], m.shape[3]]) : m;
}
var qk = N({ dilation2d_: iH });
var Sr = {};
He(Sr, { assertAndGetBroadcastShape: () => rt, getBroadcastDims: () => jk, getReductionAxes: () => fd });
function jk(r, e) {
let t10 = r.length, o = [];
for (let n = 0; n < t10; n++) {
let s = t10 - 1 - n, a = r[s] || 1;
(e[e.length - 1 - n] || 1) > 1 && a === 1 && o.unshift(s);
}
return o;
}
function fd(r, e) {
let t10 = [];
for (let o = 0; o < e.length; o++) {
let n = r[r.length - o - 1], s = e.length - o - 1, a = e[s];
(n == null || n === 1 && a > 1) && t10.unshift(s);
}
return t10;
}
function rt(r, e) {
let t10 = Math.max(r.length, e.length), o = new Array(t10);
for (let n = 0; n < t10; n++) {
let s = r[r.length - n - 1];
s == null && (s = 1);
let a = e[e.length - n - 1];
if (a == null && (a = 1), s === 1)
o[t10 - n - 1] = a;
else if (a === 1)
o[t10 - n - 1] = s;
else if (s !== a) {
let i = `Operands could not be broadcast together with shapes ${r} and ${e}.`;
throw Error(i);
} else
o[t10 - n - 1] = s;
}
return o;
}
function uH(r, e) {
let t10 = v(r, "a", "equal", "string_or_numeric"), o = v(e, "b", "equal", "string_or_numeric");
[t10, o] = Oe(t10, o), rt(t10.shape, o.shape);
let n = { a: t10, b: o };
return T.runKernel(hn, n);
}
var hd = N({ equal_: uH });
function pH(r, e, t10) {
let o = v(e, "a", "where"), n = v(t10, "b", "where"), s = v(r, "condition", "where", "bool"), a = rt(rt(s.shape, o.shape), n.shape), i = ru(s, a), p = ru(o, a), u = ru(n, a), c = { condition: i, t: p, e: u };
return T.runKernel(ua, c);
}
var io = N({ where_: pH });
function cH(r) {
let t10 = { x: v(r, "x", "zerosLike") };
return T.runKernel(fa, t10);
}
var Ht = N({ zerosLike_: cH });
function lH(r, e) {
let t10 = v(r, "a", "div"), o = v(e, "b", "div");
[t10, o] = Oe(t10, o);
let n = Ke(t10, o), s = Ht(n), a = hd(o, s);
return io(a, s, n);
}
var Xk = N({ divNoNan_: lH });
function mH(r, e) {
let t10 = v(r, "t1", "dot"), o = v(e, "t2", "dot");
$((t10.rank === 1 || t10.rank === 2) && (o.rank === 1 || o.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${t10.rank} and ${o.rank}.`);
let n = t10.rank === 1 ? t10.size : t10.shape[1], s = o.rank === 1 ? o.size : o.shape[0];
if ($(n === s, () => `Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`), t10.rank === 1 && o.rank === 1) {
let a = W(t10, [1, -1]), i = W(o, [-1, 1]), p = Qe(a, i);
return W(p, []);
} else if (t10.rank === 1 && o.rank === 2) {
let a = W(t10, [1, -1]), i = W(o, [o.shape[0], o.shape[1]]), p = Qe(a, i);
return W(p, [p.size]);
} else if (t10.rank === 2 && o.rank === 1) {
let a = W(o, [-1, 1]), i = Qe(t10, a);
return W(i, [i.size]);
} else {
let a = W(o, [o.shape[0], o.shape[1]]);
return Qe(t10, a);
}
}
var Yk = N({ dot_: mH });
function dH(r, ...e) {
let t10 = e.map((n, s) => v(n, `tensors${s}`, "einsum")), o = { equation: r };
return T.runKernel(Fi, t10, o);
}
var Qk = N({ einsum_: dH });
function fH(r) {
let t10 = { x: v(r, "x", "elu", "float32") };
return T.runKernel(fn, t10);
}
var gd = N({ elu_: fH });
function hH(r, e) {
let t10 = v(r, "x", "ensureShape", "string_or_numeric");
if (!OC(t10.shape, e))
throw new Error(`EnsureShape: Shape of tensor ${t10.shape} is not compatible with expected shape ${e}`);
return r;
}
var Zk = N({ ensureShape_: hH });
function gH(r) {
let e = v(r, "x", "erf");
$(e.dtype === "int32" || e.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), e.dtype === "int32" && (e = Ye(e, "float32"));
let t10 = { x: e };
return T.runKernel(Wa, t10);
}
var Jk = N({ erf_: gH });
function Iw(r, e) {
for (let t10 = 0; t10 < r.length; ++t10)
if (r[r.length - t10 - 1] !== e - 1 - t10)
return false;
return true;
}
function e2(r, e, t10) {
let o = r.length + e.length, n = [], s = 0, a = 0;
for (let i = 0; i < o; i++)
t10.indexOf(i) === -1 ? n.push(r[s++]) : n.push(e[a++]);
return n;
}
function xH(r, e) {
let t10 = [], o = r.length;
for (let s = 0; s < o; s++)
e.indexOf(s) === -1 && t10.push(r[s]);
let n = e.map((s) => r[s]);
return [t10, n];
}
function ti(r, e) {
let t10 = e.map((o) => 1);
return e2(r, t10, e);
}
function yH(r, e, t10) {
$(Iw(e, t10), () => `${r} supports only inner-most axes for now. Got axes ${e} and rank-${t10} input.`);
}
function bH(r, e) {
if (Iw(r, e))
return null;
let t10 = [];
for (let o = 0; o < e; ++o)
r.indexOf(o) === -1 && t10.push(o);
return r.forEach((o) => t10.push(o)), t10;
}
function CH(r) {
return r.map((e, t10) => [t10, e]).sort((e, t10) => e[1] - t10[1]).map((e) => e[0]);
}
function wH(r, e) {
let t10 = [];
for (let o = e - r; o < e; ++o)
t10.push(o);
return t10;
}
function IH(r, e = null, t10 = false) {
let n = { x: v(r, "x", "max") }, s = { reductionIndices: e, keepDims: t10 };
return T.runKernel(Ln, n, s);
}
var Ia = N({ max_: IH });
function vH(r, e = null, t10 = false) {
let n = { x: v(r, "x", "min") }, s = { axis: e, keepDims: t10 };
return T.runKernel(Wn, n, s);
}
var vl = N({ min_: vH });
function kH(r, e) {
let t10 = v(r, "base", "pow"), o = v(e, "exp", "pow");
[t10, o] = Oe(t10, o);
let n = { a: t10, b: o };
return T.runKernel(Zn, n);
}
var ri = N({ pow_: kH });
function ke(r, e) {
if ((Pt(r) && e !== "string" || Array.isArray(r)) && e !== "complex64")
throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
if (e === "string" && Pt(r) && !(r instanceof Uint8Array))
throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
return wr(r, [], [], e);
}
function NH(r) {
let t10 = { x: v(r, "x", "sqrt", "float32") };
return T.runKernel(xs, t10);
}
var Rr = N({ sqrt_: NH });
function TH(r) {
let e = v(r, "x", "square"), t10 = {};
return T.runKernel("Square", { x: e }, t10);
}
var Jt = N({ square_: TH });
function _H(r, e = null, t10 = false) {
let o = v(r, "x", "sum");
o.dtype === "bool" && (o = Ye(o, "int32"));
let n = { x: o }, s = { axis: e, keepDims: t10 };
return T.runKernel(ys, n, s);
}
var ot = N({ sum_: _H });
function $H(r, e = "euclidean", t10 = null, o = false) {
r = v(r, "x", "norm");
let n = t2(r, e, t10), s = n.shape;
if (o) {
let a = vi(t10, r.shape);
s = ti(n.shape, a);
}
return W(n, s);
}
function t2(r, e, t10 = null) {
if (r.rank === 0)
return Zt(r);
if (r.rank !== 1 && t10 === null)
return t2(W(r, [-1]), e, t10);
if (r.rank === 1 || typeof t10 == "number" || Array.isArray(t10) && t10.length === 1) {
if (e === 1)
return ot(Zt(r), t10);
if (e === 1 / 0)
return Ia(Zt(r), t10);
if (e === -1 / 0)
return vl(Zt(r), t10);
if (e === "euclidean" || e === 2)
return Rr(ot(ri(Zt(r), ke(2, "int32")), t10));
throw new Error(`Error in norm: invalid ord value: ${e}`);
}
if (Array.isArray(t10) && t10.length === 2) {
if (e === 1)
return Ia(ot(Zt(r), t10[0]), t10[1] - 1);
if (e === 1 / 0)
return Ia(ot(Zt(r), t10[1]), t10[0]);
if (e === -1 / 0)
return vl(ot(Zt(r), t10[1]), t10[0]);
if (e === "fro" || e === "euclidean")
return Rr(ot(Jt(r), t10));
throw new Error(`Error in norm: invalid ord value: ${e}`);
}
throw new Error(`Error in norm: invalid axis: ${t10}`);
}
var Lu = N({ norm_: $H });
function EH(r, e = null, t10 = false) {
return Lu(r, "euclidean", e, t10);
}
var r2 = N({ euclideanNorm_: EH });
function RH(r) {
let t10 = { x: v(r, "x", "exp") };
return T.runKernel(gn, t10);
}
var ko = N({ exp_: RH });
function DH(r, e = 0) {
let t10 = v(r, "x", "expandDims", "string_or_numeric");
$(e <= t10.rank, () => "Axis must be <= rank of the tensor");
let o = { input: t10 }, n = { dim: e };
return T.runKernel(Js, o, n);
}
var oi = N({ expandDims_: DH });
function AH(r) {
let t10 = { x: v(r, "x", "expm1") };
return T.runKernel(xn, t10);
}
var o2 = N({ expm1_: AH });
function FH(r, e) {
let t10 = v(r, "x", "tile", "string_or_numeric");
$(t10.rank === e.length, () => `Error in transpose: rank of input ${t10.rank} must match length of reps ${e}.`);
let o = { x: t10 }, n = { reps: e };
return T.runKernel(so, o, n);
}
var nu = N({ tile_: FH });
function PH(r, e, t10, o = "float32") {
e == null && (e = r);
let n = me([r, e], o), s = r <= e ? r : e;
for (let i = 0; i < s; ++i)
n.set(1, i, i);
let a = W(n.toTensor(), [r, e]);
if (t10 == null)
return a;
if (t10.length === 1)
return nu(oi(a, 0), [t10[0], 1, 1]);
if (t10.length === 2)
return nu(oi(oi(a, 0), 0), [t10[0], t10[1], 1, 1]);
if (t10.length === 3)
return nu(oi(oi(oi(a, 0), 0), 0), [t10[0], t10[1], t10[2], 1, 1]);
throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${t10.length}D.`);
}
var xd = N({ eye_: PH });
function OH(r) {
let t10 = { x: v(r, "x", "floor", "float32") };
return T.runKernel(bn, t10);
}
var yd = N({ floor_: OH });
function MH(r, e, t10 = 0, o = 0) {
let n = v(r, "x", "gather"), s = v(e, "indices", "gather", "int32"), a = { x: n, indices: s }, i = { axis: t10, batchDims: o };
return T.runKernel(ta, a, i);
}
var bd = N({ gather_: MH });
function LH(r, e) {
let t10 = v(r, "a", "greater", "string_or_numeric"), o = v(e, "b", "greater", "string_or_numeric");
[t10, o] = Oe(t10, o), rt(t10.shape, o.shape);
let n = { a: t10, b: o };
return T.runKernel(In, n);
}
var Bu = N({ greater_: LH });
function BH(r, e) {
let t10 = v(r, "a", "greaterEqual", "string_or_numeric"), o = v(e, "b", "greaterEqual", "string_or_numeric");
[t10, o] = Oe(t10, o), rt(t10.shape, o.shape);
let n = { a: t10, b: o };
return T.runKernel(vn, n);
}
var Cd = N({ greaterEqual_: BH });
function zH(r) {
let t10 = { input: v(r, "input", "imag") };
return T.runKernel(Mi, t10);
}
var su = N({ imag_: zH });
function VH(r) {
let t10 = { x: v(r, "x", "isFinite") };
return T.runKernel(kn, t10);
}
var n2 = N({ isFinite_: VH });
function WH(r) {
let t10 = { x: v(r, "x", "isInf") };
return T.runKernel(Nn, t10);
}
var s2 = N({ isInf_: WH });
function UH(r) {
let t10 = { x: v(r, "x", "isNaN") };
return T.runKernel(Tn, t10);
}
var a2 = N({ isNaN_: UH });
function GH(r, e = 0.2) {
let o = { x: v(r, "x", "leakyRelu") }, n = { alpha: e };
return T.runKernel(_n, o, n);
}
var wd = N({ leakyRelu_: GH });
function HH(r, e) {
let t10 = v(r, "a", "less", "string_or_numeric"), o = v(e, "b", "less", "string_or_numeric");
[t10, o] = Oe(t10, o), rt(t10.shape, o.shape);
let n = { a: t10, b: o };
return T.runKernel($n, n);
}
var kl = N({ less_: HH });
function KH(r, e) {
let t10 = v(r, "a", "lessEqual", "string_or_numeric"), o = v(e, "b", "lessEqual", "string_or_numeric");
[t10, o] = Oe(t10, o), rt(t10.shape, o.shape);
let n = { a: t10, b: o };
return T.runKernel(En, n);
}
var ic = N({ lessEqual_: KH });
function i2(r, e, t10) {
if (t10 <= 0)
throw new Error("The number of values should be positive.");
let o = { start: r, stop: e, num: t10 };
return T.runKernel(Rn, {}, o);
}
function qH(r, e = 5, t10 = 1, o = 1, n = 0.5) {
let s = v(r, "x", "localResponseNormalization");
$(s.rank === 4 || s.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
rank ${s.rank}.`), $(Ba(e), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`);
let a = s, i = false;
s.rank === 3 && (i = true, a = W(s, [1, s.shape[0], s.shape[1], s.shape[2]]));
let p = { x: a }, u = { depthRadius: e, bias: t10, alpha: o, beta: n }, c = T.runKernel(Mn, p, u);
return i ? W(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
}
var u2 = N({ localResponseNormalization_: qH });
function jH(r) {
let t10 = { x: v(r, "x", "log", "float32") };
return T.runKernel(Dn, t10);
}
var ni = N({ log_: jH });
function XH(r) {
let t10 = { x: v(r, "x", "log1p") };
return T.runKernel(An, t10);
}
var Sd = N({ log1p_: XH });
function YH(r) {
return $(Ws(r), () => "The f passed in grad(f) must be a function"), (e, t10) => {
let o = v(e, "x", "tf.grad", "string_or_numeric"), n = t10 != null ? v(t10, "dy", "tf.grad") : null;
return T.tidy(() => {
let { value: s, grads: a } = T.gradients(() => r(o), [o], n);
return n != null && xt(s.shape, n.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), Id(a), a[0];
});
};
}
function QH(r) {
return $(Ws(r), () => "The f passed in grads(f) must be a function"), (e, t10) => {
$(Array.isArray(e), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
let o = Ja(e, "args", "tf.grads", "string_or_numeric"), n = t10 != null ? v(t10, "dy", "tf.grads") : null;
return T.tidy(() => {
let { value: s, grads: a } = T.gradients(() => r(...o), o, n);
return n != null && xt(s.shape, n.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), Id(a), a;
});
};
}
function ZH(r) {
return $(Ws(r), () => "The f passed in valueAndGrad(f) must be a function"), (e, t10) => {
$(e instanceof pt, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), $(t10 == null || t10 instanceof pt, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
let { grads: o, value: n } = T.gradients(() => r(e), [e], t10);
return Id(o), { grad: o[0], value: n };
};
}
function JH(r) {
return $(Ws(r), () => "The f passed in valueAndGrads(f) must be a function"), (e, t10) => {
$(Array.isArray(e) && e.every((n) => n instanceof pt), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), $(t10 == null || t10 instanceof pt, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
let o = T.gradients(() => r(...e), e, t10);
return t10 != null && xt(o.value.shape, t10.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), Id(o.grads), o;
};
}
function vw(r, e) {
$(Ws(r), () => "The f passed in variableGrads(f) must be a function"), $(e == null || Array.isArray(e) && e.every((u) => u instanceof Qa), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
let t10 = e != null;
if (!t10) {
e = [];
for (let u in T.registeredVariables)
e.push(T.registeredVariables[u]);
}
let o = t10 ? e.filter((u) => !u.trainable) : null, n = e.length;
e = e.filter((u) => u.trainable), $(e.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${n} variables is trainable.`);
let s = true, { value: a, grads: i } = T.gradients(r, e, null, s);
$(i.some((u) => u != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), $(a.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`);
let p = {};
return e.forEach((u, c) => {
i[c] != null && (p[u.name] = i[c]);
}), o != null && o.forEach((u) => p[u.name] = null), { value: a, grads: p };
}
function Ir(r) {
return T.customGrad(r);
}
function Id(r) {
if (r.filter((t10) => t10 == null).length > 0)
throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
the f you passed encloses all operations that lead from x to y.`);
}
function eK(r) {
let t10 = { x: v(r, "x", "neg") };
return T.runKernel(oa, t10);
}
var pr = N({ neg_: eK });
function tK(r) {
let t10 = { x: v(r, "x", "softplus") };
return T.runKernel(gs, t10);
}
var vd = N({ softplus_: tK });
function rK(r) {
let e = v(r, "x", "logSigmoid");
return Ir((o) => ({ value: pr(vd(pr(o))), gradFunc: (a) => se(a, wa(pr(o))) }))(e);
}
var p2 = N({ logSigmoid_: rK });
function oK(r, e) {
let t10 = v(r, "a", "sub"), o = v(e, "b", "sub");
[t10, o] = Oe(t10, o);
let n = { a: t10, b: o };
return T.runKernel(Is, n);
}
var Te = N({ sub_: oK });
function nK(r, e = -1) {
let t10 = v(r, "logits", "logSoftmax");
if (e === -1 && (e = t10.rank - 1), e !== t10.rank - 1)
throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${t10.rank} and axis was ${e}`);
return Ir((n, s) => {
let i = Ia(n, e, true), p = Te(n, i), u = Te(Ye(p, "float32"), ni(ot(ko(p), e, true)));
return s([u]), { value: u, gradFunc: (l, m) => {
let [d] = m, f = true, h = ko(d);
return Te(l, se(ot(l, e, f), h));
} };
})(t10);
}
var c2 = N({ logSoftmax_: nK });
function sK(r, e = null, t10 = false) {
let o = v(r, "x", "logSumExp"), n = vi(e, o.shape), s = Ia(o, n, true), a = Te(o, s), i = ko(a), p = ot(i, n), u = ni(p), c = be(W(s, u.shape), u);
if (t10) {
let l = ti(c.shape, n);
return W(c, l);
}
return c;
}
var kd = N({ logSumExp_: sK });
function aK(r, e) {
let t10 = v(r, "a", "logicalAnd", "bool"), o = v(e, "b", "logicalAnd", "bool");
rt(t10.shape, o.shape);
let n = { a: t10, b: o };
return T.runKernel(Fn, n);
}
var zu = N({ logicalAnd_: aK });
function iK(r) {
let t10 = { x: v(r, "x", "logicalNot", "bool") };
return T.runKernel(Pn, t10);
}
var Nd = N({ logicalNot_: iK });
function uK(r, e) {
let t10 = v(r, "a", "logicalOr", "bool"), o = v(e, "b", "logicalOr", "bool");
rt(t10.shape, o.shape);
let n = { a: t10, b: o };
return T.runKernel(On, n);
}
var Td = N({ logicalOr_: uK });
function pK(r, e) {
let t10 = v(r, "a", "logicalXor", "bool"), o = v(e, "b", "logicalXor", "bool");
return rt(t10.shape, o.shape), zu(Td(r, e), Nd(zu(r, e)));
}
var l2 = N({ logicalXor_: pK });
var _d = 2147483648;
function cK(r, e, t10 = "left") {
let o = v(r, "sortedSequence", "searchSorted"), n = v(e, "values", "searchSorted"), s = o.shape[o.shape.length - 1], a = n.shape[n.shape.length - 1], i = W(o, [-1, s]), p = W(n, [-1, a]);
if (i.rank < 2)
throw new Error("Sorted input argument must be at least 2-dimensional");
if (i.shape[0] !== p.shape[0])
throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");
if (Ue(p.shape) >= _d)
throw new Error(`values tensor size must less than ${_d}`);
if (i.shape[1] >= _d)
throw new Error(`trailing dim_size must less than ${_d} for int32 output type, was ${i.shape[1]}`);
let u = { sortedSequence: i, values: p }, c = { side: t10 };
return T.runKernel(ls, u, c);
}
var Nl = N({ searchSorted_: cK });
function m2(r, e) {
return Nl(r, e, "left");
}
function lK(r, e, t10, o, n) {
let s = v(r, "x", "maxPool"), a = 1, i = s, p = false;
s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${i.rank}.`), $(gr(t10, a), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${t10} and dilations '${a}'`), Lt("maxPool", o, n);
let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n }, l = T.runKernel(zn, u, c);
return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var $d = N({ maxPool_: lK });
function mK(r, e = [1, 1, 1], t10, o, n, s = "NDHWC") {
let a = v(r, "x", "maxPool3d"), i = a, p = false;
a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), Lt("maxPool3d", o, n);
let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(ra, u, c);
return p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l;
}
var d2 = N({ maxPool3d_: mK });
function dK(r, e, t10, o, n = false) {
let a = { x: v(r, "x", "maxPoolWithArgmax") }, i = { filterSize: e, strides: t10, pad: o, includeBatchInIndex: n }, p = T.runKernel(Bi, a, i);
return { result: p[0], indexes: p[1] };
}
var f2 = N({ maxPoolWithArgmax_: dK });
function fK(r, e) {
let t10 = v(r, "a", "maximum"), o = v(e, "b", "maximum");
[t10, o] = Oe(t10, o), t10.dtype === "bool" && (t10 = Ye(t10, "int32"), o = Ye(o, "int32")), rt(t10.shape, o.shape);
let n = { a: t10, b: o };
return T.runKernel(Bn, n);
}
var Ed = N({ maximum_: fK });
function hK(r, e = null, t10 = false) {
let n = { x: v(r, "x", "mean") }, s = { axis: e, keepDims: t10 };
return T.runKernel(Vn, n, s);
}
var Vu = N({ mean_: hK });
function Wr(r, e = "float32") {
if (Ct(r), e === "complex64") {
let o = Wr(r, "float32"), n = Wr(r, "float32");
return $r(o, n);
}
let t10 = Up(Ue(r), e);
return T.makeTensor(t10, r, e);
}
function va(r, e = "float32") {
if (Ct(r), e === "complex64") {
let o = va(r, "float32"), n = Wr(r, "float32");
return $r(o, n);
}
let t10 = pl(Ue(r), e);
return T.makeTensor(t10, r, e);
}
function h2(r, e, { indexing: t10 = "xy" } = {}) {
if (t10 !== "xy" && t10 !== "ij")
throw new TypeError(`${t10} is not a valid third argument to meshgrid`);
if (r === void 0)
return [];
let o = v(r, "x", "meshgrid", r instanceof pt ? r.dtype : "float32");
if (e === void 0)
return [o];
let n = v(e, "y", "meshgrid", e instanceof pt ? e.dtype : "float32"), s = Ue(o.shape), a = Ue(n.shape);
return t10 === "xy" ? (o = W(o, [1, -1]), n = W(n, [-1, 1]), [Qe(va([a, 1], o.dtype), o), Qe(n, va([1, s], n.dtype))]) : (o = W(o, [-1, 1]), n = W(n, [1, -1]), [Qe(o, va([1, a], o.dtype)), Qe(va([s, 1], n.dtype), n)]);
}
function gK(r, e) {
let t10 = v(r, "a", "minimum"), o = v(e, "b", "minimum");
[t10, o] = Oe(t10, o), t10.dtype === "bool" && (t10 = Ye(t10, "int32"), o = Ye(o, "int32")), rt(t10.shape, o.shape);
let n = { a: t10, b: o };
return T.runKernel(Un, n);
}
var Wu = N({ minimum_: gK });
function xK(r, e, t10) {
$(t10 === "reflect" || t10 === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${t10}.`);
let o = v(r, "x", "mirrorPad");
if (o.rank === 0)
throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");
$(e.length === o.rank, () => `Padding doesn't match input. Must be ${o.rank}. Got ${e.length}.`);
let n = t10 === "reflect" ? 1 : 0;
for (let i = 0; i < o.rank; i++)
$(e[i].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), $(e[i][0] >= 0 && e[i][0] <= o.shape[i] - n && e[i][1] >= 0 && e[i][1] <= o.shape[i] - n, () => `Padding in dimension ${i} cannot be greater than or equal to ${o.shape[i] - n} or less than 0 for input of shape ${o.shape}`);
let s = { paddings: e, mode: t10 }, a = { x: o };
return T.runKernel(Gn, a, s);
}
var g2 = N({ mirrorPad_: xK });
function yK(r, e) {
let t10 = v(r, "a", "mod"), o = v(e, "b", "mod");
[t10, o] = Oe(t10, o);
let n = { a: t10, b: o };
return T.runKernel(Ga, n);
}
var x2 = N({ mod_: yK });
function bK(r, e = null, t10 = false) {
r = v(r, "x", "moments");
let o = vi(e, r.shape), n = Vu(r, o, t10), s = n.shape;
t10 || (s = ti(n.shape, o));
let a = Jt(Te(Ye(r, "float32"), W(n, s))), i = Vu(a, o, t10);
return { mean: n, variance: i };
}
var y2 = N({ moments_: bK });
function CK(r, e, t10, o) {
let n = v(e, "data", "multiRNNCell"), s = Ja(t10, "c", "multiRNNCell"), a = Ja(o, "h", "multiRNNCell"), i = n, p = [];
for (let l = 0; l < r.length; l++) {
let m = r[l](i, s[l], a[l]);
p.push(m[0]), p.push(m[1]), i = m[1];
}
let u = [], c = [];
for (let l = 0; l < p.length; l += 2)
u.push(p[l]), c.push(p[l + 1]);
return [u, c];
}
var b2 = N({ multiRNNCell_: CK });
function wK(r, e, t10, o = false) {
let n = v(r, "logits", "multinomial"), s = n.size, a = n.rank;
if (s < 2)
throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`);
if (a > 2)
throw new Error(`Rank of probabilities must be 1 or 2, but is ${a}`);
t10 = t10 || Math.random();
let p = { logits: a === 1 ? W(n, [1, -1]) : n }, u = { numSamples: e, seed: t10, normalized: o }, c = T.runKernel(Hn, p, u);
return a === 1 ? W(c, [c.size]) : c;
}
var C2 = N({ multinomial_: wK });
function SK(r, e) {
let t10 = v(r, "a", "notEqual", "string_or_numeric"), o = v(e, "b", "notEqual", "string_or_numeric");
[t10, o] = Oe(t10, o), rt(t10.shape, o.shape);
let n = { a: t10, b: o };
return T.runKernel(qn, n);
}
var Rd = N({ notEqual_: SK });
function IK(r, e, t10 = 1, o = 0, n = "int32") {
if (e < 2)
throw new Error(`Error in oneHot: depth must be >=2, but it is ${e}`);
let a = { indices: v(r, "indices", "oneHot", "int32") }, i = { dtype: n, depth: e, onValue: t10, offValue: o };
return T.runKernel(Yn, a, i);
}
var Tl = N({ oneHot_: IK });
function vK(r) {
let t10 = { x: v(r, "x", "onesLike") };
return T.runKernel(na, t10);
}
var w2 = N({ onesLike_: vK });
function kK(r, e) {
let t10 = v(r, "v1", "outerProduct"), o = v(e, "v2", "outerProduct");
$(t10.rank === 1 && o.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${t10.rank} and ${o.rank}.`);
let n = W(t10, [-1, 1]), s = W(o, [1, -1]);
return Qe(n, s);
}
var S2 = N({ outerProduct_: kK });
function NK(r, e, t10 = 0) {
let o = v(r, "x", "pad");
if (o.rank === 0)
throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
let n = { paddings: e, constantValue: t10 }, s = { x: o };
return T.runKernel(Qn, s, n);
}
var ka = N({ pad_: NK });
function TK(r, e, t10 = 0) {
return $(e.length === 2, () => "Invalid number of paddings. Must be length of 2."), ka(r, [e], t10);
}
var I2 = N({ pad1d_: TK });
function _K(r, e, t10 = 0) {
return $(e.length === 2 && e[0].length === 2 && e[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), ka(r, e, t10);
}
var v2 = N({ pad2d_: _K });
function $K(r, e, t10 = 0) {
return $(e.length === 3 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), ka(r, e, t10);
}
var k2 = N({ pad3d_: $K });
function EK(r, e, t10 = 0) {
return $(e.length === 4 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2 && e[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), ka(r, e, t10);
}
var N2 = N({ pad4d_: EK });
function RK(r, e, t10) {
let o = v(r, "x", "spaceToBatchND");
$(o.rank >= 1 + e.length, () => `input rank ${o.rank} should be > than [blockShape] ${e.length}`), $(t10.length === e.length, () => `paddings.shape[0] ${t10.length} must be equal to [blockShape] ${e.length}`), $(o.shape.reduce((a, i, p) => p > 0 && p <= e.length ? a && (i + t10[p - 1][0] + t10[p - 1][1]) % e[p - 1] === 0 : a, true), () => `input spatial dimensions ${o.shape.slice(1)} with paddings ${t10.toString()} must be divisible by blockShapes ${e.toString()}`);
let n = { x: o }, s = { blockShape: e, paddings: t10 };
return T.runKernel(ca, n, s);
}
var Dd = N({ spaceToBatchND_: RK });
function DK(r, e, t10, o, n, s, a) {
n == null && (n = [1, 1]), s == null && (s = 1), o === 0 && (o = "valid");
let i = v(r, "x", "maxPool"), p = i, u = false;
i.rank === 3 && (u = true, p = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(gr(s, n), () => `Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${n}'`);
let c = ww(p.shape, e, s, n, o), l = [c.dilationHeight, c.dilationWidth], m;
o === "same" ? m = FK([c.filterHeight, c.filterWidth], l) : m = [[0, 0], [0, 0]];
let d = l[0] === 1 && l[1] === 1, [f, h] = AK([c.inHeight, c.inWidth], l, m), g = d ? o : "valid", x = d ? p : Dd(p, l, f), w = (t10 === "avg" ? () => cd(x, e, s, g, a) : () => $d(x, e, s, g, a))(), S = d ? w : ld(w, l, h);
return u ? W(S, [S.shape[1], S.shape[2], S.shape[3]]) : S;
}
function AK(r, e, t10) {
let o = t10.map((c) => c[0]), n = t10.map((c) => c[1]), s = r.concat(o, n), a = e.map((c, l) => (c - s[l] % c) % c), i = n.map((c, l) => c + a[l]), p = e.map((c, l) => [o[l], i[l]]), u = e.map((c, l) => [0, a[l]]);
return [p, u];
}
function FK(r, e) {
let o = r.map((a, i) => a + (a - 1) * (e[i] - 1)).map((a) => a - 1), n = o.map((a) => Math.floor(a / 2)), s = o.map((a, i) => a - n[i]);
return o.map((a, i) => [n[i], s[i]]);
}
var T2 = N({ pool_: DK });
function PK(r, e) {
let t10 = v(r, "x", "prelu"), o = v(e, "alpha", "prelu"), n = { x: t10, alpha: o };
return T.runKernel(Jn, n);
}
var Ad = N({ prelu_: PK });
function OK(r, e = null, t10 = false) {
let o = v(r, "x", "prod");
o.dtype === "bool" && (o = Ye(o, "int32"));
let n = { x: o }, s = { axis: e, keepDims: t10 };
return T.runKernel(es, n, s);
}
var _2 = N({ prod_: OK });
function MK(r, e, t10, o) {
let n = r.map((c, l) => v(c, `tensors${l}`, "raggedGather", "int32")), s = v(e, "paramsDenseValues", "raggedGather"), a = v(t10, "indices", "raggedGather", "int32"), i = { paramsNestedSplits: n, paramsDenseValues: s, indices: a }, p = { outputRaggedRank: o }, u = T.runKernel(Kp, i, p);
return { outputNestedSplits: u.slice(0, u.length - 1), outputDenseValues: u[u.length - 1] };
}
var $2 = N({ raggedGather_: MK });
function LK(r, e, t10) {
let o = v(r, "starts", "raggedRange"), n = v(e, "limits", "raggedRange", o.dtype), s = v(t10, "deltas", "raggedRange", o.dtype), a = { starts: o, limits: n, deltas: s }, i = T.runKernel(qp, a);
return { rtNestedSplits: i[0], rtDenseValues: i[1] };
}
var E2 = N({ raggedRange_: LK });
function BK(r, e, t10, o, n) {
let s = v(r, "shape", "raggedTensorToTensor", "int32"), a = v(e, "values", "raggedTensorToTensor"), i = v(t10, "defaultValue", "raggedTensorToTensor", a.dtype), p = o.map((l, m) => v(l, `tensors${m}`, "raggedTensorToTensor", "int32")), u = { shape: s, values: a, defaultValue: i, rowPartitionTensors: p }, c = { rowPartitionTypes: n };
return T.runKernel(jp, u, c);
}
var R2 = N({ raggedTensorToTensor_: BK });
function zK(r, e, t10) {
Ct(r);
let o = Ue(r), n = null;
if (t10 == null || t10 === "float32")
n = new Float32Array(o);
else if (t10 === "int32")
n = new Int32Array(o);
else if (t10 === "bool")
n = new Uint8Array(o);
else
throw new Error(`Unknown data type ${t10}`);
for (let s = 0; s < o; s++)
n[s] = e();
return T.makeTensor(n, r, t10);
}
var D2 = N({ rand_: zK });
var Ld = Bp(Rw());
var Z2 = {};
He(Z2, { TEST_EPSILON_FLOAT16: () => X2, createVideoElement: () => JK, encodeStrings: () => Q2, expectArrayBuffersEqual: () => ZK, expectArraysClose: () => jK, expectArraysEqual: () => YK, expectNumbersClose: () => Y2, expectPromiseToFail: () => XK, expectValuesInRange: () => QK, play: () => eq, testEpsilon: () => Pd });
var qK = 1e-3;
var X2 = 0.1;
function jK(r, e, t10) {
return t10 == null && (t10 = Pd()), Dw(r, e, (o, n) => Aw(o, n, t10));
}
function Pd() {
return T.backend.floatPrecision() === 32 ? qK : X2;
}
function Dw(r, e, t10) {
let o = true;
if ((Pt(r) || Pt(e)) && (o = false), Pt(r) && Pt(e) && (o = true), o) {
let a = r.constructor.name, i = e.constructor.name;
if (a !== i)
throw new Error(`Arrays are of different type. Actual: ${a}. Expected: ${i}`);
}
if (Array.isArray(r) && Array.isArray(e)) {
let a = ar(r), i = ar(e);
if (!br(a, i))
throw new Error(`Arrays have different shapes. Actual: [${a}]. Expected: [${i}]`);
}
let n = Pt(r) ? r : Es(r), s = Pt(e) ? e : Es(e);
if (n.length !== s.length)
throw new Error(`Arrays have different lengths actual: ${n.length} vs expected: ${s.length}.
Actual: ${n}.
Expected: ${s}.`);
for (let a = 0; a < s.length; ++a) {
let i = n[a], p = s[a];
if (!t10(i, p))
throw new Error(`Arrays differ: actual[${a}] = ${i}, expected[${a}] = ${p}.
Actual: ${n}.
Expected: ${s}.`);
}
typeof expect != "undefined" && expect().nothing();
}
function XK(r, e) {
r().then(() => e.fail(), () => e()), typeof expect != "undefined" && expect().nothing();
}
function YK(r, e) {
let t10 = typeof e == "string" || typeof e == "number" || typeof e == "boolean" ? [e] : e;
return Bo(r) || Bo(r[0]) || Bo(e) || Bo(e[0]) ? Dw(r, t10, (o, n) => o == n) : Dw(r, e, (o, n) => Aw(o, n, 0));
}
function Y2(r, e, t10) {
if (t10 == null && (t10 = Pd()), !Aw(r, e, t10))
throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`);
typeof expect != "undefined" && expect().nothing();
}
function Aw(r, e, t10) {
return !isFinite(r) && !isFinite(e) ? true : !(isNaN(r) || isNaN(e) || Math.abs(r - e) > t10);
}
function QK(r, e, t10) {
for (let o = 0; o < r.length; o++)
if (r[o] < e || r[o] > t10)
throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t10}`);
}
function ZK(r, e) {
let t10 = new Float32Array(r), o = new Float32Array(e);
if (t10.length !== o.length)
throw new Error(`Expected ArrayBuffer to be of length ${o.length}, but it was ${t10.length}`);
for (let n = 0; n < o.length; n++)
if (t10[n] !== o[n])
throw new Error(`Expected ArrayBuffer value at ${n} to be ${o[n]} but got ${t10[n]} instead`);
}
function Q2(r) {
for (let e = 0; e < r.length; e++) {
let t10 = r[e];
Array.isArray(t10) ? Q2(t10) : r[e] = Yi(t10);
}
return r;
}
function JK(r) {
let e = document.createElement("video");
return "playsInline" in e && (e.playsInline = true), e.muted = true, e.loop = true, e.style.position = "fixed", e.style.left = "0px", e.style.top = "0px", e.preload = "auto", e.appendChild(r), new Promise((t10) => {
e.addEventListener("loadeddata", (o) => t10(e)), e.load();
});
}
async function eq(r) {
await r.play(), "requestVideoFrameCallback" in r && await new Promise((e) => {
r.requestVideoFrameCallback(e);
});
}
var Gu = class {
constructor(e, t10, o, n, s) {
this.mean = e, this.stdDev = t10, this.dtype = o, this.nextVal = NaN, this.truncated = n, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2);
let a = s || Math.random();
this.random = Ld.alea(a.toString());
}
nextValue() {
if (!isNaN(this.nextVal)) {
let n = this.nextVal;
return this.nextVal = NaN, n;
}
let e, t10, o = false;
for (; !o; ) {
let n, s, a;
do
n = 2 * this.random() - 1, s = 2 * this.random() - 1, a = n * n + s * s;
while (a >= 1 || a === 0);
let i = Math.sqrt(-2 * Math.log(a) / a);
e = this.mean + this.stdDev * n * i, t10 = this.mean + this.stdDev * s * i, (!this.truncated || this.isValidTruncated(e)) && (o = true);
}
return (!this.truncated || this.isValidTruncated(t10)) && (this.nextVal = this.convertValue(t10)), this.convertValue(e);
}
convertValue(e) {
return this.dtype == null || this.dtype === "float32" ? e : Math.round(e);
}
isValidTruncated(e) {
return e <= this.upper && e >= this.lower;
}
};
var Od = class {
constructor(e, t10, o, n) {
this.alpha = e, this.beta = 1 / t10, this.dtype = o;
let s = n || Math.random();
this.randu = Ld.alea(s.toString()), this.randn = new Gu(0, 1, o, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d);
}
nextValue() {
let e, t10, o, n, s, a;
for (; ; ) {
do
n = this.randn.nextValue(), a = 1 + this.c * n;
while (a <= 0);
if (a *= a * a, e = n * n, t10 = 1 - 0.331 * e * e, o = 0.5 * e + this.d * (1 - a + Math.log(a)), s = this.randu(), s < t10 || Math.log(s) < o)
break;
}
return a = 1 / this.beta * this.d * a, this.alpha < 1 && (a *= Math.pow(this.randu(), 1 / this.alpha)), this.convertValue(a);
}
convertValue(e) {
return this.dtype === "float32" ? e : Math.round(e);
}
};
var Md = class {
constructor(e = 0, t10 = 1, o, n) {
if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t10 - e, this.dtype = o, n == null && (n = Math.random()), typeof n == "number" && (n = n.toString()), !this.canReturnFloat() && this.range <= 1)
throw new Error(`The difference between ${e} - ${t10} <= 1 and dtype is not float`);
this.random = Ld.alea(n);
}
convertValue(e) {
return this.canReturnFloat() ? e : Math.round(e);
}
nextValue() {
return this.convertValue(this.min + this.range * this.random());
}
};
function tq(r, e, t10 = 1, o = "float32", n) {
if (Ct(r), t10 == null && (t10 = 1), o == null && (o = "float32"), o !== "float32" && o !== "int32")
throw new Error(`Unsupported data type ${o}`);
let s = new Od(e, t10, o, n), a = me(r, o);
for (let i = 0; i < a.values.length; i++)
a.values[i] = s.nextValue();
return a.toTensor();
}
var J2 = N({ randomGamma_: tq });
function rq(r, e = 0, t10 = 1, o, n) {
if (Ct(r), o != null && o === "bool")
throw new Error(`Unsupported data type ${o}`);
let s = new Gu(e, t10, o, false, n), a = me(r, o);
for (let i = 0; i < a.values.length; i++)
a.values[i] = s.nextValue();
return a.toTensor();
}
var Bd = N({ randomNormal_: rq });
function oq(r, e, t10) {
if (e != null && e === "bool")
throw new Error(`Unsupported data type ${e}`);
return Bd(r, 0, 1, e, t10);
}
var e1 = N({ randomStandardNormal_: oq });
function nq(r, e = 0, t10 = 1, o = "float32", n) {
Ct(r);
let s = me(r, o), a = new Md(e, t10, null, n);
for (let i = 0; i < s.values.length; i++)
s.values[i] = a.nextValue();
return s.toTensor();
}
var uc = N({ randomUniform_: nq });
function sq(r, e, t10, o) {
return uc(r, e, t10, "int32", o);
}
var t1 = N({ randomUniformInt_: sq });
function au(r, e, t10 = 1, o = "float32") {
if (t10 === 0)
throw new Error("Cannot have a step of zero");
let n = { start: r, stop: e, step: t10, dtype: o };
return T.runKernel(aa, {}, n);
}
function aq(r) {
let t10 = { input: v(r, "input", "real") };
return T.runKernel(zi, t10);
}
var si = N({ real_: aq });
function iq(r) {
let t10 = { x: v(r, "x", "reciprocal") };
return T.runKernel(ts, t10);
}
var r1 = N({ reciprocal_: iq });
function uq(r) {
let t10 = { x: v(r, "x", "relu") };
return T.runKernel(rs, t10);
}
var iu = N({ relu_: uq });
function pq(r) {
let t10 = { x: v(r, "x", "relu6") };
return T.runKernel(ss, t10);
}
var zd = N({ relu6_: pq });
function cq(r, e) {
let o = { x: v(r, "x", "reverse") }, n = { dims: e };
return T.runKernel(as, o, n);
}
var uo = N({ reverse_: cq });
function lq(r) {
let e = v(r, "x", "reverse");
return $(e.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${e.rank}.`), uo(e, 0);
}
var o1 = N({ reverse1d_: lq });
function mq(r, e) {
let t10 = v(r, "x", "reverse");
return $(t10.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${t10.rank}.`), uo(t10, e);
}
var n1 = N({ reverse2d_: mq });
function dq(r, e) {
let t10 = v(r, "x", "reverse");
return $(t10.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${t10.rank}.`), uo(t10, e);
}
var s1 = N({ reverse3d_: dq });
function fq(r, e) {
let t10 = v(r, "x", "reverse");
return $(t10.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${t10.rank}.`), uo(t10, e);
}
var a1 = N({ reverse4d_: fq });
function hq(r) {
let t10 = { x: v(r, "x", "round") };
return T.runKernel(is, t10);
}
var Vd = N({ round_: hq });
function gq(r) {
let t10 = { x: v(r, "x", "rsqrt", "float32") };
return T.runKernel(us, t10);
}
var i1 = N({ rsqrt_: gq });
function xq(r) {
let t10 = { x: v(r, "x", "selu") };
return T.runKernel(ms, t10);
}
var u1 = N({ selu_: xq });
function yq(r, e, t10, o, n, s = [1, 1], a = "NHWC") {
let i = v(r, "x", "separableConv2d"), p = v(e, "depthwiseFilter", "separableConv2d"), u = v(t10, "pointwiseFilter", "separableConv2d"), c = i, l = false;
if (i.rank === 3 && (l = true, c = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), a === "NCHW")
throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
$(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), $(p.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${p.rank}.`), $(u.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${p.rank}.`), $(u.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`), $(u.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);
let m = p.shape[2], d = p.shape[3];
$(u.shape[2] === m * d, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${m * d}, but got ${u.shape[2]}.`);
let f = ac(c, p, o, n, a, s), g = ou(f, u, 1, "valid", a);
return l ? W(g, [g.shape[1], g.shape[2], g.shape[3]]) : g;
}
var p1 = N({ separableConv2d_: yq });
async function bq(r, e) {
let t10 = v(r, "x", "setdiff1d"), o = v(e, "y", "setdiff1d");
$(t10.dtype === o.dtype, () => `x and y should have the same dtype, but got x (${t10.dtype}) and y (${o.dtype}).`), $(t10.rank === 1, () => `x should be 1D tensor, but got x (${t10.shape}).`), $(o.rank === 1, () => `y should be 1D tensor, but got y (${o.shape}).`);
let n = await t10.data(), s = await o.data(), a = new Set(s), i = 0;
for (let c = 0; c < n.length; c++)
a.has(n[c]) || i++;
let p = new tt([i], t10.dtype), u = new tt([i], "int32");
for (let c = 0, l = 0; c < n.length; c++)
a.has(n[c]) || (p.values[l] = n[c], u.values[l] = c, l++);
return [p.toTensor(), u.toTensor()];
}
var c1 = bq;
function Cq(r) {
let t10 = { x: v(r, "x", "sign") };
return T.runKernel(fs, t10);
}
var l1 = N({ sign_: Cq });
function wq(r) {
let t10 = { x: v(r, "x", "sin", "float32") };
return T.runKernel(ds, t10);
}
var m1 = N({ sin_: wq });
function Sq(r) {
let t10 = { x: v(r, "x", "sinh") };
return T.runKernel(ja, t10);
}
var d1 = N({ sinh_: Sq });
function Iq(r, e, t10) {
let o = v(r, "x", "slice1d");
return $(o.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${o.rank} tensor`), qe(o, [e], [t10]);
}
var f1 = N({ slice1d_: Iq });
function vq(r, e, t10) {
let o = v(r, "x", "slice2d");
return $(o.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${o.rank} tensor`), qe(o, e, t10);
}
var h1 = N({ slice2d_: vq });
function kq(r, e, t10) {
let o = v(r, "x", "slice3d");
return $(o.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${o.rank} tensor`), qe(o, e, t10);
}
var g1 = N({ slice3d_: kq });
function Nq(r, e, t10) {
let o = v(r, "x", "slice4d");
return $(o.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${o.rank} tensor`), qe(o, e, t10);
}
var x1 = N({ slice4d_: Nq });
function Tq(r, e = -1) {
let t10 = v(r, "logits", "softmax", "float32");
if (e === -1 && (e = t10.rank - 1), e !== t10.rank - 1)
throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${t10.rank} and dim was ${e}`);
let o = { logits: t10 }, n = { dim: e };
return T.runKernel(bs, o, n);
}
var y1 = N({ softmax_: Tq });
function _q(r) {
$(r.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`);
let e = { input: r };
return T.runKernel(Pi, e);
}
var pc = N({ fft_: _q });
function $q(r) {
$(r.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`);
let e = { input: r };
return T.runKernel(Oi, e);
}
var Hu = N({ ifft_: $q });
function Eq(r) {
let e = r.shape[r.shape.length - 1], t10 = r.size / e, o;
if (e <= 2) {
let n = W(r, [t10, e]);
o = Hu(n);
} else {
let n = [t10, 2 * (e - 1)], s = W(si(r), [t10, e]), a = W(su(r), [t10, e]), i = uo(qe(s, [0, 1], [t10, e - 2]), 1), p = se(uo(qe(a, [0, 1], [t10, e - 2]), 1), ke(-1)), u = yt([s, i], 1), c = yt([a, p], 1), l = W($r(u, c), [n[0], n[1]]);
o = Hu(l);
}
if (o = si(o), r.rank === 3 && r.shape[0] !== 0) {
let n = o, s = r.shape[0];
o = W(o, [s, o.shape[0] / s, o.shape[1]]), n.dispose();
}
return o;
}
var Wd = N({ irfft_: Eq });
function Rq(r, e, t10 = 0) {
let n = { x: v(r, "x", "split") }, s = { numOrSizeSplits: e, axis: t10 };
return T.runKernel(la, n, s);
}
var ai = N({ split_: Rq });
function Dq(r, e) {
$(r.dtype === "float32", () => `The dtype for rfft() must be real value but got ${r.dtype}`);
let t10 = r.shape[r.shape.length - 1], o = r.size / t10, n;
if (e != null && e < t10) {
let f = r.shape.map((g) => 0), h = r.shape.map((g) => g);
h[r.shape.length - 1] = e, n = qe(r, f, h), t10 = e;
} else if (e != null && e > t10) {
let f = r.shape.map((h) => h);
f[r.shape.length - 1] = e - t10, n = yt([r, Wr(f)], r.shape.length - 1), t10 = e;
} else
n = r;
let s = Ht(n), a = W($r(n, s), [o, t10]), i = pc(a), p = Math.floor(t10 / 2) + 1, u = si(i), c = su(i), l = ai(u, [p, t10 - p], u.shape.length - 1), m = ai(c, [p, t10 - p], c.shape.length - 1), d = n.shape.slice();
return d[n.shape.length - 1] = p, W($r(l[0], m[0]), d);
}
var cc = N({ rfft_: Dq });
function Aq(r, e) {
let t10 = v(r, "a", "squaredDifference"), o = v(e, "b", "squaredDifference");
[t10, o] = Oe(t10, o), rt(t10.shape, o.shape);
let n = { a: t10, b: o }, s = {};
return T.runKernel(ws, n, s);
}
var Ud = N({ squaredDifference_: Aq });
function Fq(r, e) {
let t10 = v(r, "x", "squeeze", "string_or_numeric");
return W(t10, MC(t10.shape, e).newShape);
}
var lc = N({ squeeze_: Fq });
function Pq(r, e = 0) {
let t10 = Ja(r, "tensors", "stack", "string_or_numeric");
$(t10.length >= 1, () => "Pass at least one tensor to tf.stack"), t10.length > 0 && $(e <= t10[0].rank, () => "Axis must be <= rank of the tensor");
let o = t10, n = { axis: e };
return T.runKernel(sa, o, n);
}
var vr = N({ stack_: Pq });
function Oq(r, e = 0) {
let o = { x: v(r, "x", "step") }, n = { alpha: e };
return T.runKernel(yo, o, n);
}
var Gd = N({ step_: Oq });
function Mq(r, e, t10, o, n = 0, s = 0, a = 0, i = 0, p = 0) {
let c = { x: v(r, "x", "stridedSlice", "string_or_numeric") }, l = { begin: e, end: t10, strides: o, beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p };
return T.runKernel(Ss, c, l);
}
var b1 = N({ stridedSlice_: Mq });
function Lq(r) {
let t10 = { x: v(r, "x", "tan", "float32") };
return T.runKernel(vs, t10);
}
var C1 = N({ tan_: Lq });
function xr(r, e) {
oo(r);
let t10 = ar(r, e);
if (t10.length !== 1)
throw new Error("tensor1d() requires values to be a flat/TypedArray");
return wr(r, null, t10, e);
}
function uu(r, e, t10) {
if (oo(r), e != null && e.length !== 2)
throw new Error("tensor2d() requires shape to have two numbers");
let o = ar(r, t10);
if (o.length !== 2 && o.length !== 1)
throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
if (o.length === 1 && e == null)
throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
return wr(r, e, o, t10);
}
function Hd(r, e, t10) {
if (oo(r), e != null && e.length !== 3)
throw new Error("tensor3d() requires shape to have three numbers");
let o = ar(r, t10);
if (o.length !== 3 && o.length !== 1)
throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
if (o.length === 1 && e == null)
throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
return wr(r, e, o, t10);
}
function w1(r, e, t10) {
if (oo(r), e != null && e.length !== 4)
throw new Error("tensor4d() requires shape to have four numbers");
let o = ar(r, t10);
if (o.length !== 4 && o.length !== 1)
throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
if (o.length === 1 && e == null)
throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
return wr(r, e, o, t10);
}
function S1(r, e, t10) {
if (oo(r), e != null && e.length !== 5)
throw new Error("tensor5d() requires shape to have five numbers");
let o = ar(r, t10);
if (o.length !== 5 && o.length !== 1)
throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
if (o.length === 1 && e == null)
throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
return wr(r, e, o, t10);
}
function I1(r, e, t10) {
if (oo(r), e != null && e.length !== 6)
throw new Error("tensor6d() requires shape to have six numbers");
let o = ar(r, t10);
if (o.length !== 6 && o.length !== 1)
throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
if (o.length === 1 && e == null)
throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
return e = e || o, wr(r, e, o, t10);
}
var pu = {};
He(pu, { calculateShapes: () => v1, validateInput: () => mc, validateUpdateShape: () => Fw });
function Fw(r, e, t10) {
let o = e.rank > 1 ? e.shape[e.rank - 1] : 1, n = e.rank > 1 ? e.rank - 1 : 1, s = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${t10.shape}, indices.shape: ${e.shape}, shape: ${r}, sliceDim: ${o}, and batchDim: ${n}.`;
if (t10.rank < n)
throw new Error(s + ` update.rank < ${n}. `);
if (r.length < o + (t10.rank - n))
throw new Error(s + ` Output shape length < ${o + (t10.rank - n)}`);
if (t10.rank !== n + r.length - o)
throw new Error(s + ` update.rank != ${n + r.length - o}`);
for (let a = 0; a < n; ++a)
if (t10.shape[a] !== e.shape[a])
throw new Error(s + ` updates.shape[${a}] (${t10.shape[a]}) != indices.shape[${a}] (${e.shape[a]}).`);
for (let a = 0; a < t10.rank - n; ++a)
if (t10.shape[a + n] !== r[a + o])
throw new Error(s + ` updates.shape[${a + n}] (${t10.shape[a + n]}) != shape[${a + n}] (${r[a + n]})`);
}
function mc(r, e, t10) {
if (e.rank < 1)
throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${e.rank}.`);
if (r.rank < 1)
throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${r.rank}.`);
if (e.dtype !== "int32")
throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${e.dtype}`);
if (t10.length < 1)
throw new Error(`Output rank must be greater or equal to 1, but got shape: ${t10}`);
if (t10.length === 0) {
if (e.size === 0)
throw new Error(`Indices specified for empty output. indices shape: ${e.shape}`);
if (r.size === 0)
throw new Error(`Updates specified for empty output. updates shape: ${r.shape}`);
}
Fw(t10, e, r);
}
function v1(r, e, t10) {
let o = e.shape.length, n = o > 1 ? e.shape[o - 1] : 1, s = t10.length, a = 1;
for (let l = n; l < s; ++l)
a *= t10[l];
let i = n < 1 ? 1 : n, p = Ue(e.shape) / i, u = [...Us(t10.slice(0, n)), 1], c = Ue(t10);
return { sliceRank: n, numUpdates: p, sliceSize: a, strides: u, outputSize: c };
}
function Bq(r, e, t10) {
let o = v(r, "tensor", "tensorScatterupdate"), n = v(e, "indices", "tensorScatterupdate", "int32"), s = v(t10, "updates", "tensorScatterupdate");
if (mc(s, n, o.shape), o.dtype !== s.dtype)
throw new Error(`tensor and updates must have the same dtype, instead they are ${o.dtype} and ${s.dtype}.`);
let a = { tensor: o, indices: n, updates: s }, i = {};
return T.runKernel(cs, a, i);
}
var k1 = N({ tensorScatterUpdate_: Bq });
function zq(r, e = 1, t10 = true) {
let o = v(r, "x", "topk");
if (o.rank === 0)
throw new Error("topk() expects the input to be of rank 1 or higher");
let n = o.shape[o.shape.length - 1];
if (e < 0)
throw new Error(`'k' passed to topk() must be >= 0 but got ${e}`);
if (e > n)
throw new Error(`'k' passed to topk() must be <= the last dimension (${n}) but got ${e}`);
let s = { x: o }, a = { k: e, sorted: t10 }, [i, p] = T.runKernel(Ns, s, a);
return { values: i, indices: p };
}
var N1 = N({ topk_: zq });
function Vq(r, e = 0, t10 = 1, o, n) {
if (Ct(r), o != null && o === "bool")
throw new Error("Unsupported data type $ { dtype }");
let s = new Gu(e, t10, o, true, n), a = me(r, o);
for (let i = 0; i < a.values.length; i++)
a.values[i] = s.nextValue();
return a.toTensor();
}
var T1 = N({ truncatedNormal_: Vq });
function Wq(r, e = 0) {
let t10 = v(r, "x", "unique", "string_or_numeric");
$(t10.rank > 0, () => "The input tensor must be at least 1D");
let o = { x: t10 }, n = { axis: e }, [s, a] = T.runKernel(qi, o, n);
return { values: s, indices: a };
}
var _1 = N({ unique_: Wq });
function Uq(r, e, t10) {
let o = v(r, "x", "unsortedSegmentSum"), n = v(e, "segmentIds", "unsortedSegmentSum", "int32");
$(Ba(t10), () => "numSegments must be of dtype int");
let s = { x: o, segmentIds: n }, a = { numSegments: t10 };
return T.runKernel(ji, s, a);
}
var $1 = N({ unsortedSegmentSum_: Uq });
function Gq(r, e = 0) {
let t10 = v(r, "x", "unstack", "string_or_numeric");
$(e >= -t10.shape.length && e < t10.shape.length, () => `Axis = ${e} is not in [-${t10.shape.length}, ${t10.shape.length})`);
let o = { value: t10 }, n = { axis: e };
return T.runKernel(da, o, n);
}
var po = N({ unstack_: Gq });
function E1(r, e) {
return Nl(r, e, "right");
}
function R1(r, e = true, t10, o) {
return T.makeVariable(r, e, t10, o);
}
function Kd(r, e) {
let t10 = [];
for (let s = 0; s < e.length; s++)
e[s] && t10.push(s);
let o = me(r, "int32"), n = me([t10.length, r.length], "int32");
for (let s = 0; s < t10.length; s++) {
let a = o.indexToLoc(t10[s]), i = s * r.length;
n.values.set(a, i);
}
return n.toTensor();
}
async function Hq(r) {
let e = v(r, "condition", "whereAsync", "bool"), t10 = await e.data(), o = Kd(e.shape, t10);
return r !== e && e.dispose(), o;
}
var qd = Hq;
async function Kq(r, e, t10) {
let o = v(r, "tensor", "boolMask"), n = v(e, "mask", "boolMask", "bool"), s = t10 == null ? 0 : t10, a = n.rank, i = o.shape;
$(a > 0, () => "mask cannot be scalar"), xt(i.slice(s, s + a), n.shape, "mask's shape must match the first K dimensions of tensor's shape,");
let p = 1;
for (let h = s; h < s + a; h++)
p *= i[h];
let u = i.slice(0, s).concat([p], i.slice(s + a)), c = W(o, u), l = W(n, [-1]), m = await qd(l), d = lc(m, [1]), f = bd(c, d, s);
return r !== o && o.dispose(), e !== n && n.dispose(), d.dispose(), c.dispose(), l.dispose(), m.dispose(), f;
}
var qq = Kq;
function jq(r, e, t10) {
let o = v(r, "x", "transpose");
if (e == null && (e = o.shape.map((a, i) => i).reverse()), $(o.rank === e.length, () => `Error in transpose: rank of input ${o.rank} must match length of perm ${e}.`), e.forEach((a) => {
$(a >= 0 && a < o.rank, () => `All entries in 'perm' must be between 0 and ${o.rank - 1} but got ${e}`);
}), o.rank <= 1)
return o.clone();
let n = { x: o }, s = { perm: e };
return o.dtype === "complex64" ? De(() => {
let a = si(o), i = su(o);
return a = T.runKernel(ao, { x: a }, s), i = T.runKernel(ao, { x: i }, s), t10 && (i = pr(i)), $r(a, i);
}) : T.runKernel(ao, n, s);
}
var dc = N({ transpose_: jq });
function Xq(r, e, t10, o, n = true) {
let s = v(r, "v", "movingAverage"), a = v(e, "x", "movingAverage"), i = v(t10, "decay", "movingAverage");
nw(s, a), $(br(s.shape, a.shape), () => "Shape mismatch in v and x");
let p = ke(1), u = Te(p, i), c = se(Te(a, s), u);
if (n) {
$(o != null, () => "When using zeroDebias: true, step is required.");
let l = v(o, "step", "movingAverage");
c = Ke(c, Te(p, ri(i, l)));
}
return be(s, c);
}
var Yq = N({ movingAverage_: Xq });
function Qq(r, e, t10) {
Ct(t10);
let o = v(r, "indices", "scatterND", "int32"), n = v(e, "updates", "scatterND");
mc(n, o, t10);
let s = { indices: o, updates: n }, a = { shape: t10 };
return T.runKernel(ps, s, a);
}
var Zq = N({ scatterND_: Qq });
function D1(r, e, t10, o) {
if (r.dtype !== "int32")
throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`);
if (r.rank > 2)
throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`);
let n = r.rank > 0 ? r.shape[0] : 1, s = r.rank > 1 ? r.shape[1] : 1;
if (t10.length !== s)
throw new Error(`outputShape has incorrect number of elements:, ${t10.length}, should be: ${s}.`);
let a = e.size;
if (!(e.rank === 0 || e.rank === 1 && a === n))
throw new Error(`sparseValues has incorrect shape ${e.shape}, should be [] or [${n}]`);
if (e.dtype !== o.dtype)
throw new Error("sparseValues.dtype must match defaultValues.dtype");
}
function e6(r, e, t10, o = 0) {
Ct(t10);
let n = v(r, "sparseIndices", "sparseToDense", "int32"), s = v(e, "sparseValues", "sparseToDense", "string_or_numeric"), a = v(o, "defaultValue", "sparseToDense", s.dtype);
D1(n, s, t10, a);
let i = { sparseIndices: n, sparseValues: s, defaultValue: a }, p = { outputShape: t10 };
return T.runKernel(Cs, i, p);
}
var t6 = N({ sparseToDense_: e6 });
function r6(r, e) {
let t10 = v(e, "indices", "gatherND", "int32"), n = { params: v(r, "x", "gatherND", "string_or_numeric"), indices: t10 };
return T.runKernel(Sn, n);
}
var o6 = N({ gatherND_: r6 });
function A1(r, e) {
if (e == null)
return r.shape.slice();
if (br(r.shape, e))
return e;
if (r.shape.length === e.length) {
let t10 = [];
for (let o = 0; o < r.shape.length; o++)
e[o] == null && r.shape[o] != null ? t10.push(r.shape[o]) : t10.push(e[o]);
return t10;
}
return e;
}
function n6(r, e, t10, o) {
let n = v(r, "x", "dropout");
if ($(n.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${n.dtype} tensor instead.`), $(e >= 0 && e < 1, () => `rate must be a float in the range [0, 1), but got ${e}.`), e === 0)
return r instanceof pt ? n.clone() : n;
let s = A1(n, t10), a = 1 - e, i = Ke(yd(be(uc(s, 0, 1, "float32", o), a)), a);
return se(n, i);
}
var s6 = N({ dropout_: n6 });
function Pw(r) {
return Math.floor(Math.pow(2, Math.ceil(Math.log(r) / Math.log(2))));
}
function _l(r, e, t10) {
let o = 1 - r % 2, n = new Float32Array(r);
for (let s = 0; s < r; ++s) {
let a = 2 * Math.PI * s / (r + o - 1);
n[s] = e - t10 * Math.cos(a);
}
return xr(n, "float32");
}
async function a6(r, e, t10 = 1) {
let o = v(r, "predictions", "inTopK"), n = v(e, "targets", "inTopK");
$(o.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${o.rank}`), $(o.rank - 1 === n.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${o.rank} and targets rank ${n.rank}`), xt(o.shape.slice(0, o.shape.length - 1), n.shape, "predictions's shape should be align with the targets' shape, except the last dimension.");
let s = o.shape[o.shape.length - 1];
$(t10 > 0 && t10 <= s, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${t10}`);
let a = await o.data(), i = await n.data(), [p, u] = [a.length / s, s], c = LC("bool", p);
for (let l = 0; l < p; l++) {
let m = l * u, d = a.subarray(m, m + u), f = [];
for (let h = 0; h < d.length; h++)
f.push({ value: d[h], index: h });
f.sort((h, g) => g.value - h.value), c[l] = 0;
for (let h = 0; h < t10; h++)
if (f[h].index === i[l]) {
c[l] = 1;
break;
}
}
return r !== o && o.dispose(), e !== n && n.dispose(), ir(c, n.shape, "bool");
}
var i6 = a6;
var Ow = {};
He(Ow, { conv2d: () => P1, depthwiseConv2d: () => L1, matMul: () => B1 });
function u6(r, e, t10, o, n, s = "NHWC", a) {
let i = r;
r.rank === 3 && (i = W(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
let p = e;
p.rank === 3 && (p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]])), $(i.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`), $(p.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${p.shape}.`), $(t10.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${t10}.`);
let u = s === "NHWC" ? i.shape[3] : i.shape[1], c = s === "NHWC" ? p.shape[3] : p.shape[1];
$(u === t10[2], () => `Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${t10[2]}.`), $(c === t10[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${t10[3]}).`), Lt("conv2dDerFilter", n, a);
let l = { x: i, dy: p }, m = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, filterShape: t10 };
return T.runKernel($i, l, m);
}
var F1 = N({ conv2DBackpropFilter_: u6 });
function Ku(r, e, t10) {
if (t10 == null || t10 === "linear")
return r;
if (t10 === "relu")
return se(r, Gd(e));
throw new Error(`Cannot compute gradient for fused activation ${t10}.`);
}
function qu(r, e) {
let t10 = e, o = fd(r.shape, e.shape);
return o.length > 0 && (t10 = ot(t10, o)), W(t10, r.shape);
}
function ju(r, e, t10, o) {
if (e === "linear")
return r;
if (e === "relu")
return iu(r);
if (e === "elu")
return gd(r);
if (e === "relu6")
return zd(r);
if (e === "prelu")
return Ad(r, t10);
if (e === "leakyrelu")
return wd(r, o);
if (e === "sigmoid")
return wa(r);
throw new Error(`Unknown fused activation ${e}.`);
}
var Xu = (r, e) => !(r > 0) || e === "linear";
function p6({ x: r, filter: e, strides: t10, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) {
if (p = p || "linear", Xu(T.state.gradientDepth, p) === false) {
$(n === "NHWC", () => `Error in fused conv2d: got dataFormat of ${n} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);
let _ = ou(r, e, t10, o, n, s, a);
return i != null && (_ = be(_, i)), ju(_, p, u, c);
}
let l = v(r, "x", "conv2d", "float32"), m = v(e, "filter", "conv2d", "float32"), d = l, f = false;
l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`), Lt("fused conv2d", o, a);
let h = n === "NHWC" ? d.shape[3] : d.shape[1];
$(m.shape[2] === h, () => `Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`), $(gr(t10, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`);
let g = Mu(d.shape, m.shape, t10, s, o, a), x;
i != null && (x = v(i, "bias", "fused conv2d"), [x] = Oe(x, l), n === "NHWC" ? rt(g.outShape, x.shape) : ($(x.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`), $(x.shape.length === 0 || x.shape[0] === g.outChannels || x.shape[0] === 1, () => `Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`)));
let b;
if (u != null) {
let _ = u.shape;
if ($(_.length <= 1 || _.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${_.length}.`), _.length === 1)
$(_[0] === 1 || _[0] === g.outChannels, () => `Error in fused conv2d: PReLU activation weights (${_}) is not compatible with the number of output channels (${g.outChannels}).`);
else if (_.length === 3)
try {
rt(_, g.outShape);
} catch (E) {
let R = `Error in fused conv2d: PReLU activation weights (${_}) is not compatible with the output shape of the conv2d (${g.outShape}).`;
throw Error(R);
}
b = v(u, "prelu weights", "fused conv2d");
}
let w = (_, E) => {
$(n === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${n} but only NHWC is currently supported.`);
let [R, D, F, O] = E, M = Ku(_, F, p);
$(Ou(s), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
let L = dd(D.shape, M, R, t10, o), B = F1(D, M, R.shape, t10, o), z = [L, B];
if (O != null) {
let U = qu(O, M);
z.push(U);
}
return z;
}, S = { x: d, filter: m, bias: x, preluActivationWeights: b }, k = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c };
return i == null ? Ir((E, R, D) => {
let F = T.runKernel(Co, S, k);
return D([R, E, F]), f && (F = W(F, [F.shape[1], F.shape[2], F.shape[3]])), { value: F, gradFunc: w };
})(d, m) : Ir((E, R, D, F) => {
let O = T.runKernel(Co, S, k);
return F([R, E, O, D]), f && (O = W(O, [O.shape[1], O.shape[2], O.shape[3]])), { value: O, gradFunc: w };
})(d, m, x);
}
var P1 = N({ fusedConv2d_: p6 });
function c6(r, e, t10, o, n, s = [1, 1], a) {
let i = r;
r.rank === 3 && (i = W(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
let p = e;
p.rank === 3 && (p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
let u = { x: i, dy: p }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, filterShape: t10 };
return T.runKernel(Ei, u, c);
}
var O1 = N({ depthwiseConv2dNativeBackpropFilter_: c6 });
function l6(r, e, t10, o, n, s = [1, 1], a) {
let i = e, p = false;
e.rank === 3 && (p = true, i = W(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
let u = { dy: i, filter: t10 }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, inputShape: r }, l = T.runKernel(Ri, u, c);
return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var M1 = N({ depthwiseConv2dNativeBackpropInput_: l6 });
function m6({ x: r, filter: e, strides: t10, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) {
if (Xu(T.state.gradientDepth, p) === false) {
let k = ac(r, e, t10, o, n, s, a);
return i != null && (k = be(k, i)), ju(k, p, u, c);
}
let l = v(r, "x", "depthwiseConv2d", "float32"), m = v(e, "filter", "depthwiseConv2d", "float32"), d = l, f = false;
l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`), $(d.shape[3] === m.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`), s == null && (s = [1, 1]), $(gr(t10, s), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), Lt("fused depthwiseConv2d", o, a);
let h = Mu(d.shape, m.shape, t10, s, o, a, true), g;
i != null && (g = v(i, "bias", "fused conv2d"), [g] = Oe(g, l), rt(h.outShape, g.shape));
let x;
u != null && (x = v(u, "prelu weights", "fused depthwiseConv2d"));
let b = (k, _) => {
$(Ou(s), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);
let [E, R, D, F] = _, O = Ku(k, D, p), M = M1(R.shape, O, E, t10, o, s, a), L = O1(R, O, E.shape, t10, o, s, a);
if (F != null) {
let B = qu(g, O);
return [M, L, B];
}
return [M, L];
}, w = { x: d, filter: m, bias: g, preluActivationWeights: x }, S = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c };
return i == null ? Ir((_, E, R) => {
let D = T.runKernel(wo, w, S);
return R([E, _, D]), f && (D = W(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: b };
})(d, m) : Ir((_, E, R, D) => {
let F = T.runKernel(wo, w, S);
return D([E, _, F, R]), f && (F = W(F, [F.shape[1], F.shape[2], F.shape[3]])), { value: F, gradFunc: b };
})(d, m, g);
}
var L1 = N({ fusedDepthwiseConv2d_: m6 });
function d6({ a: r, b: e, transposeA: t10 = false, transposeB: o = false, bias: n, activation: s = "linear", preluActivationWeights: a, leakyreluAlpha: i = 0.2 }) {
if (Xu(T.state.gradientDepth, s) === false) {
let O = Qe(r, e, t10, o);
return n != null && (O = be(O, n)), ju(O, s, a, i);
}
let p = v(r, "a", "fused matMul"), u = v(e, "b", "fused matMul");
[p, u] = Oe(p, u);
let c = t10 ? p.shape[p.rank - 2] : p.shape[p.rank - 1], l = o ? u.shape[u.rank - 1] : u.shape[u.rank - 2], m = t10 ? p.shape[p.rank - 1] : p.shape[p.rank - 2], d = o ? u.shape[u.rank - 2] : u.shape[u.rank - 1], f = p.shape.slice(0, -2), h = u.shape.slice(0, -2), g = Ue(f), x = Ue(h);
$(c === l, () => `Error in fused matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${p.shape} and ${u.shape} and transposeA=${t10} and transposeB=${o} must match.`);
let w = rt(p.shape.slice(0, -2), u.shape.slice(0, -2)).concat([m, d]), S = t10 ? W(p, [g, c, m]) : W(p, [g, m, c]), k = o ? W(u, [x, d, l]) : W(u, [x, l, d]), _;
n != null && (_ = v(n, "bias", "fused matMul"), [_] = Oe(_, p), rt(w, _.shape));
let E;
a != null && (E = v(a, "prelu weights", "fused matMul"));
let R = (O, M) => {
let [L, B, z, U] = M, j = Ku(W(O, z.shape), z, s), H, X;
if (!t10 && !o ? (H = Qe(j, B, false, true), X = Qe(L, j, true, false)) : !t10 && o ? (H = Qe(j, B, false, false), X = Qe(j, L, true, false)) : t10 && !o ? (H = Qe(B, j, false, true), X = Qe(L, j, false, false)) : (H = Qe(B, j, true, true), X = Qe(j, L, true, true)), n != null) {
let J = qu(U, j);
return [H, X, J];
} else
return [H, X];
}, D = { a: S, b: k, bias: _, preluActivationWeights: E }, F = { transposeA: t10, transposeB: o, activation: s, leakyreluAlpha: i };
return n == null ? Ir((M, L, B) => {
let z = T.runKernel(bo, D, F);
return B([M, L, z]), { value: W(z, w), gradFunc: R };
})(S, k) : Ir((M, L, B, z) => {
let U = T.runKernel(bo, D, F);
return z([M, L, U, B]), { value: W(U, w), gradFunc: R };
})(S, k, _);
}
var B1 = N({ fusedMatMul_: d6 });
function f6(r) {
return _l(r, 0.54, 0.46);
}
var z1 = N({ hammingWindow_: f6 });
function h6(r) {
return _l(r, 0.5, 0.5);
}
var jd = N({ hannWindow_: h6 });
function g6(r, e, t10, o = false, n = 0) {
let s = 0, a = [];
for (; s + e <= r.size; )
a.push(qe(r, s, e)), s += t10;
if (o)
for (; s < r.size; ) {
let i = s + e - r.size, p = yt([qe(r, s, e - i), Sa([i], n)]);
a.push(p), s += t10;
}
return a.length === 0 ? uu([], [0, e]) : W(yt(a), [a.length, e]);
}
var Xd = N({ frame_: g6 });
function x6(r, e, t10, o, n = jd) {
o == null && (o = Pw(e));
let s = Xd(r, e, t10), a = se(s, n(e));
return cc(a, o);
}
var V1 = N({ stft_: x6 });
function y6(r, e, t10, o, n = "bilinear", s = 0) {
let a = v(r, "image", "cropAndResize"), i = v(e, "boxes", "cropAndResize", "float32"), p = v(t10, "boxInd", "cropAndResize", "int32"), u = i.shape[0];
$(a.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${a.rank}.`), $(i.rank === 2 && i.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`), $(p.rank === 1 && p.shape[0] === u, () => `Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`), $(o.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${o.length}.`), $(o[0] >= 1 && o[1] >= 1, () => `cropSize must be atleast [1,1], but was ${o}`), $(n === "bilinear" || n === "nearest", () => `method must be bilinear or nearest, but was ${n}`);
let c = { image: a, boxes: i, boxInd: p }, l = { method: n, extrapolationValue: s, cropSize: o };
return T.runKernel(pn, c, l);
}
var W1 = N({ cropAndResize_: y6 });
function b6(r) {
let e = v(r, "image", "flipLeftRight", "float32");
$(e.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${e.rank}.`);
let t10 = { image: e };
return T.runKernel(yn, t10, {});
}
var U1 = N({ flipLeftRight_: b6 });
function C6(r) {
let e = v(r, "image", "grayscaleToRGB"), t10 = e.rank - 1, o = e.shape[t10];
$(e.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${e.rank}.`), $(o === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${o}.`);
let n = new Array(e.rank);
return n.fill(1, 0, t10), n[t10] = 3, nu(e, n);
}
var G1 = N({ grayscaleToRGB_: C6 });
function w6(r, e, t10 = 0, o = 0.5) {
let n = v(r, "image", "rotateWithOffset", "float32");
$(n.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${n.rank}.`);
let s = { image: n }, a = { radians: e, fillValue: t10, center: o };
return T.runKernel(_s, s, a);
}
var H1 = N({ rotateWithOffset_: w6 });
function No(r, e, t10, o, n, s) {
o == null && (o = 0.5), n == null && (n = Number.NEGATIVE_INFINITY), s == null && (s = 0);
let a = r.shape[0];
return t10 = Math.min(t10, a), $(0 <= o && o <= 1, () => `iouThreshold must be in [0, 1], but was '${o}'`), $(r.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${r.rank}'`), $(r.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`), $(e.rank === 1, () => "scores must be a 1D tensor"), $(e.shape[0] === a, () => `scores has incompatible shape with boxes. Expected ${a}, but was ${e.shape[0]}`), $(0 <= s && s <= 1, () => `softNmsSigma must be in [0, 1], but was '${s}'`), { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n, softNmsSigma: s };
}
function S6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY) {
let s = v(r, "boxes", "nonMaxSuppression", "float32"), a = v(e, "scores", "nonMaxSuppression", "float32"), i = No(s, a, t10, o, n);
t10 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold;
let p = { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n };
return T.runKernel(jn, { boxes: s, scores: a }, p);
}
var K1 = N({ nonMaxSuppression_: S6 });
function q1(r, e, t10) {
let o = I6(r, e, t10), n = o < 0 ? -(o + 1) : o;
r.splice(n, 0, e);
}
function I6(r, e, t10) {
return k6(r, e, t10 || v6);
}
function v6(r, e) {
return r > e ? 1 : r < e ? -1 : 0;
}
function k6(r, e, t10) {
let o = 0, n = r.length, s = 0, a = false;
for (; o < n; ) {
s = o + (n - o >>> 1);
let i = t10(e, r[s]);
i > 0 ? o = s + 1 : (n = s, a = !i);
}
return a ? o : -o - 1;
}
function Yd(r, e, t10, o, n) {
return Mw(r, e, t10, o, n, 0);
}
function Qd(r, e, t10, o, n, s) {
return Mw(r, e, t10, o, n, 0, false, s, true);
}
function Zd(r, e, t10, o, n, s) {
return Mw(r, e, t10, o, n, s, true);
}
function Mw(r, e, t10, o, n, s, a = false, i = false, p = false) {
let u = [];
for (let g = 0; g < e.length; g++)
e[g] > n && u.push({ score: e[g], boxIndex: g, suppressBeginIndex: 0 });
u.sort(j1);
let c = s > 0 ? -0.5 / s : 0, l = [], m = [];
for (; l.length < t10 && u.length > 0; ) {
let g = u.pop(), { score: x, boxIndex: b, suppressBeginIndex: w } = g;
if (x < n)
break;
let S = false;
for (let k = l.length - 1; k >= w; --k) {
let _ = N6(r, b, l[k]);
if (_ >= o) {
S = true;
break;
}
if (g.score = g.score * T6(o, c, _), g.score <= n)
break;
}
g.suppressBeginIndex = l.length, S || (g.score === x ? (l.push(b), m.push(g.score)) : g.score > n && q1(u, g, j1));
}
let d = l.length, f = t10 - d;
i && f > 0 && (l.push(...new Array(f).fill(0)), m.push(...new Array(f).fill(0)));
let h = { selectedIndices: l };
return a && (h.selectedScores = m), p && (h.validOutputs = d), h;
}
function N6(r, e, t10) {
let o = r.subarray(e * 4, e * 4 + 4), n = r.subarray(t10 * 4, t10 * 4 + 4), s = Math.min(o[0], o[2]), a = Math.min(o[1], o[3]), i = Math.max(o[0], o[2]), p = Math.max(o[1], o[3]), u = Math.min(n[0], n[2]), c = Math.min(n[1], n[3]), l = Math.max(n[0], n[2]), m = Math.max(n[1], n[3]), d = (i - s) * (p - a), f = (l - u) * (m - c);
if (d <= 0 || f <= 0)
return 0;
let h = Math.max(s, u), g = Math.max(a, c), x = Math.min(i, l), b = Math.min(p, m), w = Math.max(x - h, 0) * Math.max(b - g, 0);
return w / (d + f - w);
}
function T6(r, e, t10) {
let o = Math.exp(e * t10 * t10);
return t10 <= r ? o : 0;
}
function j1(r, e) {
return r.score - e.score || r.score === e.score && e.boxIndex - r.boxIndex;
}
async function _6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY) {
let s = v(r, "boxes", "nonMaxSuppressionAsync"), a = v(e, "scores", "nonMaxSuppressionAsync"), i = No(s, a, t10, o, n);
t10 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold;
let p = await Promise.all([s.data(), a.data()]), u = p[0], c = p[1], { selectedIndices: l } = Yd(u, c, t10, o, n);
return s !== r && s.dispose(), a !== e && a.dispose(), xr(l, "int32");
}
var X1 = _6;
function $6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) {
let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = No(a, i, t10, o, n, s);
t10 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma;
let u = { boxes: a, scores: i }, c = { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }, l = T.runKernel(Xn, u, c);
return { selectedIndices: l[0], selectedScores: l[1] };
}
var Y1 = N({ nonMaxSuppressionWithScore_: $6 });
async function E6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) {
let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = No(a, i, t10, o, n, s);
t10 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma;
let u = await Promise.all([a.data(), i.data()]), c = u[0], l = u[1], { selectedIndices: m, selectedScores: d } = Zd(c, l, t10, o, n, s);
return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: xr(m, "int32"), selectedScores: xr(d) };
}
var Q1 = E6;
function R6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) {
let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = No(a, i, t10, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, m = { boxes: a, scores: i }, d = { maxOutputSize: u, iouThreshold: c, scoreThreshold: l, padToMaxOutputSize: s }, f = T.runKernel(Ha, m, d);
return { selectedIndices: f[0], validOutputs: f[1] };
}
var Z1 = N({ nonMaxSuppressionPadded_: R6 });
async function D6(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) {
let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = No(a, i, t10, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, [m, d] = await Promise.all([a.data(), i.data()]), { selectedIndices: f, validOutputs: h } = Qd(m, d, u, c, l, s);
return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: xr(f, "int32"), validOutputs: ke(h, "int32") };
}
var J1 = D6;
function A6(r, e, t10 = false, o = false) {
let n = v(r, "images", "resizeBilinear");
$(n.rank === 3 || n.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`), $(e.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${e}.`), $(o === false || t10 === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");
let s = n, a = false;
n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]]));
let [] = e, i = { images: s }, p = { alignCorners: t10, halfPixelCenters: o, size: e }, u = T.runKernel(ns, i, p);
return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u;
}
var eN = N({ resizeBilinear_: A6 });
function F6(r, e, t10 = false, o = false) {
let n = v(r, "images", "resizeNearestNeighbor");
$(n.rank === 3 || n.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`), $(e.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`), $(n.dtype === "float32" || n.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), $(o === false || t10 === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
let s = n, a = false;
n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]]));
let [] = e, i = { images: s }, p = { alignCorners: t10, halfPixelCenters: o, size: e }, u = T.runKernel(os, i, p);
return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u;
}
var tN = N({ resizeNearestNeighbor_: F6 });
function P6(r, e = "binary", t10 = false, o = 0.5) {
let n = v(r, "image", "threshold"), s = 0.2989, a = 0.587, i = 0.114, p = n.shape[0] * n.shape[1], u = se(xr([o]), 255), c, l, m, d;
if ($(n.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${n.rank}.`), $(n.shape[2] === 3 || n.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${n.shape[2]}.`), $(n.dtype === "int32" || n.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${n.dtype}.`), $(e === "otsu" || e === "binary", () => `Method must be binary or otsu, but was ${e}`), n.shape[2] === 3) {
[c, l, m] = ai(n, [1, 1, 1], -1);
let g = se(c, s), x = se(l, a), b = se(m, i);
d = be(be(g, x), b);
} else
d = r;
if (e === "otsu") {
let g = md(Ye(Vd(d), "int32"), ir([]), 256);
u = O6(g, p);
}
let f = t10 ? ic(d, u) : Bu(d, u);
return Ye(se(f, 255), "int32");
}
function O6(r, e) {
let t10 = xr([-1]), o = xr([0]), n = xr([0]), s, a, i, p, u, c;
for (let l = 0; l < r.size - 1; l++) {
s = qe(r, 0, l + 1), a = qe(r, l + 1), u = Ke(ot(s), e), c = Ke(ot(a), e);
let m = ot(se(s, au(0, s.size)));
i = Ke(m, ot(s));
let d = Sa(a.shape, s.size), f = be(au(0, a.size), d), h = se(a, f);
p = Ke(ot(h), ot(a));
let g = Te(i, p), x = Te(i, p), b = se(u, c);
n = se(se(b, g), x);
let w = Bu(n, o);
o = io(w, n, o), t10 = io(w, xr([l]), t10);
}
return t10;
}
var rN = N({ threshold_: P6 });
function M6(r, e, t10 = "nearest", o = "constant", n = 0, s) {
let a = v(r, "image", "transform", "float32"), i = v(e, "transforms", "transform", "float32");
$(a.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${a.rank}.`), $(i.rank === 2 && (i.shape[0] === a.shape[0] || i.shape[0] === 1) && i.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), $(s == null || s.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${s}.`);
let p = { image: a, transforms: i }, u = { interpolation: t10, fillMode: o, fillValue: n, outputShape: s };
return T.runKernel(Ts, p, u);
}
var oN = N({ transform_: M6 });
function L6(r, e, t10) {
let o = v(r, "a", "bandPart");
$(o.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${o.rank}.`);
let n = o.shape, [s, a] = o.shape.slice(-2), i, p;
typeof e == "number" ? ($(e % 1 === 0, () => `bandPart(): numLower must be an integer, got ${e}.`), $(e <= s, () => `bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`), i = v(e < 0 ? s : e, "numLower", "bandPart")) : ($(e.dtype === "int32", () => "bandPart(): numLower's dtype must be an int32."), i = io(kl(e, 0), s, Wu(e, s))), typeof t10 == "number" ? ($(t10 % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${t10}.`), $(t10 <= a, () => `bandPart(): numUpper (${t10}) must not be greater than the number of columns (${a}).`), p = v(t10 < 0 ? a : t10, "numUpper", "bandPart")) : ($(t10.dtype === "int32", () => "bandPart(): numUpper's dtype must be an int32."), p = io(kl(t10, 0), a, Wu(t10, a)));
let u = W(au(0, s, 1, "int32"), [-1, 1]), c = au(0, a, 1, "int32"), l = Te(u, c), m = zu(ic(l, i), Cd(l, pr(p))), d = Wr([s, a], o.dtype);
return W(vr(po(W(o, [-1, s, a])).map((f) => io(m, f, d))), n);
}
var nN = N({ bandPart_: L6 });
function B6(r) {
let e;
if (Array.isArray(r)) {
e = false, $(r != null && r.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
let n = r[0].shape[0];
for (let s = 1; s < r.length; ++s)
$(r[s].shape[0] === n, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`);
} else
e = true, r = ai(r, r.shape[0], 0).map((n) => lc(n, [0]));
$(r.length <= r[0].shape[0], () => `Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);
let t10 = [], o = r;
for (let n = 0; n < r.length; ++n)
t10.push(T.tidy(() => {
let s = o[n];
if (n > 0)
for (let a = 0; a < n; ++a) {
let i = se(ot(se(t10[a], s)), t10[a]);
s = Te(s, i);
}
return Ke(s, Lu(s, "euclidean"));
}));
return e ? vr(t10, 0) : t10;
}
var sN = N({ gramSchmidt_: B6 });
function z6(r, e = false) {
if ($(r.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`), r.rank === 2)
return aN(r, e);
{
let t10 = r.shape.slice(0, r.shape.length - 2).reduce((p, u) => p * u), o = po(W(r, [t10, r.shape[r.shape.length - 2], r.shape[r.shape.length - 1]]), 0), n = [], s = [];
o.forEach((p) => {
let [u, c] = aN(p, e);
n.push(u), s.push(c);
});
let a = W(vr(n, 0), r.shape), i = W(vr(s, 0), r.shape);
return [a, i];
}
}
function aN(r, e = false) {
return T.tidy(() => {
$(r.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);
let t10 = r.shape[0], o = r.shape[1], n = xd(t10), s = Vr(r), a = uu([[1]], [1, 1]), i = Vr(a), p = t10 >= o ? o : t10;
for (let u = 0; u < p; ++u) {
let c = s, l = i, m = n;
[i, s, n] = T.tidy(() => {
let d = qe(s, [u, u], [t10 - u, 1]), f = Lu(d), h = qe(s, [u, u], [1, 1]), g = io(Bu(h, 0), uu([[-1]]), uu([[1]])), x = Te(h, se(g, f)), b = Ke(d, x);
b.shape[0] === 1 ? i = Vr(a) : i = yt([a, qe(b, [1, 0], [b.shape[0] - 1, b.shape[1]])], 0);
let w = pr(Ke(Qe(g, x), f)), S = qe(s, [u, 0], [t10 - u, o]), k = se(w, i), _ = dc(i);
if (u === 0)
s = Te(S, Qe(k, Qe(_, S)));
else {
let D = Te(S, Qe(k, Qe(_, S)));
s = yt([qe(s, [0, 0], [u, o]), D], 0);
}
let E = dc(k), R = qe(n, [0, u], [t10, n.shape[1] - u]);
if (u === 0)
n = Te(R, Qe(Qe(R, i), E));
else {
let D = Te(R, Qe(Qe(R, i), E));
n = yt([qe(n, [0, 0], [t10, u]), D], 1);
}
return [i, s, n];
}), Ot([c, l, m]);
}
return !e && t10 > o && (n = qe(n, [0, 0], [t10, o]), s = qe(s, [0, 0], [o, o])), [n, s];
});
}
var iN = N({ qr_: z6 });
var Et;
(function(r) {
r[r.NONE = 0] = "NONE", r[r.MEAN = 1] = "MEAN", r[r.SUM = 2] = "SUM", r[r.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS";
})(Et || (Et = {}));
function V6(r, e, t10 = Et.SUM_BY_NONZERO_WEIGHTS) {
let o = v(r, "losses", "computeWeightedLoss"), n = null;
e != null && (n = v(e, "weights", "computeWeightedLoss"));
let s = n == null ? o : se(o, n);
if (t10 === Et.NONE)
return s;
if (t10 === Et.SUM)
return ot(s);
if (t10 === Et.MEAN) {
if (n == null)
return Vu(s);
{
let a = o.size / n.size, i = Ke(ot(s), ot(n));
return a > 1 ? Ke(i, ke(a)) : i;
}
}
if (t10 === Et.SUM_BY_NONZERO_WEIGHTS) {
if (n == null)
return Ke(ot(s), ke(o.size));
{
let a = se(n, va(o.shape)), i = Ye(ot(Rd(a, ke(0))), "float32");
return Ke(ot(s), i);
}
}
throw Error(`Unknown reduction: ${t10}`);
}
var cr = N({ computeWeightedLoss_: V6 });
function W6(r, e, t10, o = Et.SUM_BY_NONZERO_WEIGHTS) {
let n = v(r, "labels", "absoluteDifference"), s = v(e, "predictions", "absoluteDifference"), a = null;
t10 != null && (a = v(t10, "weights", "absoluteDifference")), xt(n.shape, s.shape, "Error in absoluteDifference: ");
let i = Zt(Te(n, s));
return cr(i, a, o);
}
var uN = N({ absoluteDifference_: W6 });
function U6(r, e, t10, o, n = Et.SUM_BY_NONZERO_WEIGHTS) {
let s = v(r, "labels", "cosineDistance"), a = v(e, "predictions", "cosineDistance"), i = null;
o != null && (i = v(o, "weights", "cosineDistance")), xt(s.shape, a.shape, "Error in cosineDistance: ");
let p = ke(1), u = Te(p, ot(se(s, a), t10, true));
return cr(u, i, n);
}
var pN = N({ cosineDistance_: U6 });
function G6(r, e, t10, o = Et.SUM_BY_NONZERO_WEIGHTS) {
let n = v(r, "labels", "hingeLoss"), s = v(e, "predictions", "hingeLoss"), a = null;
t10 != null && (a = v(t10, "weights", "hingeLoss")), xt(n.shape, s.shape, "Error in hingeLoss: ");
let i = ke(1);
n = Te(se(ke(2), n), i);
let p = iu(Te(i, se(n, s)));
return cr(p, a, o);
}
var cN = N({ hingeLoss_: G6 });
function H6(r, e, t10, o = 1, n = Et.SUM_BY_NONZERO_WEIGHTS) {
let s = v(r, "labels", "huberLoss"), a = v(e, "predictions", "huberLoss"), i = null;
t10 != null && (i = v(t10, "weights", "huberLoss")), xt(s.shape, a.shape, "Error in huberLoss: ");
let p = ke(o), u = Zt(Te(a, s)), c = Wu(u, p), l = Te(u, c), m = be(se(ke(0.5), Jt(c)), se(p, l));
return cr(m, i, n);
}
var lN = N({ huberLoss_: H6 });
function K6(r, e, t10, o = 1e-7, n = Et.SUM_BY_NONZERO_WEIGHTS) {
let s = v(r, "labels", "logLoss"), a = v(e, "predictions", "logLoss"), i = null;
t10 != null && (i = v(t10, "weights", "logLoss")), xt(s.shape, a.shape, "Error in logLoss: ");
let p = ke(1), u = ke(o), c = pr(se(s, ni(be(a, u)))), l = se(Te(p, s), ni(be(Te(p, a), u))), m = Te(c, l);
return cr(m, i, n);
}
var mN = N({ logLoss_: K6 });
function q6(r, e, t10, o = Et.SUM_BY_NONZERO_WEIGHTS) {
let n = v(r, "labels", "meanSquaredError"), s = v(e, "predictions", "meanSquaredError"), a = null;
t10 != null && (a = v(t10, "weights", "meanSquaredError")), xt(n.shape, s.shape, "Error in meanSquaredError: ");
let i = Ud(n, s);
return cr(i, a, o);
}
var dN = N({ meanSquaredError_: q6 });
function j6(r, e) {
let t10 = v(r, "labels", "sigmoidCrossEntropyWithLogits"), o = v(e, "logits", "sigmoidCrossEntropyWithLogits");
xt(t10.shape, o.shape, "Error in sigmoidCrossEntropyWithLogits: ");
let n = iu(o), s = se(o, t10), a = Sd(ko(pr(Zt(o))));
return be(Te(n, s), a);
}
function X6(r, e, t10, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) {
let s = v(r, "multiClassLabels", "sigmoidCrossEntropy"), a = v(e, "logits", "sigmoidCrossEntropy"), i = null;
if (t10 != null && (i = v(t10, "weights", "sigmoidCrossEntropy")), xt(s.shape, a.shape, "Error in sigmoidCrossEntropy: "), o > 0) {
let u = ke(o), c = ke(1), l = ke(0.5);
s = be(se(s, Te(c, u)), se(l, u));
}
let p = j6(s, a);
return cr(p, i, n);
}
var fN = N({ sigmoidCrossEntropy_: X6 });
function Y6(r, e, t10 = -1) {
if (t10 === -1 && (t10 = e.rank - 1), t10 !== e.rank - 1)
throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t10}`);
return Ir((n, s, a) => {
let p = kd(s, [t10], true), u = Te(Ye(s, "float32"), p);
a([n, u]);
let c = pr(se(u, n));
return { value: ot(c, [t10]), gradFunc: (d, f) => {
let [h, g] = f, x = ti(d.shape, [t10]);
return [se(W(d, x), Te(Ye(h, "float32"), ko(g))), se(W(d, x), Te(ko(g), Ye(h, "float32")))];
} };
})(r, e);
}
function Q6(r, e, t10, o = 0, n = Et.SUM_BY_NONZERO_WEIGHTS) {
let s = v(r, "onehotLabels", "softmaxCrossEntropy"), a = v(e, "logits", "softmaxCrossEntropy"), i = null;
if (t10 != null && (i = v(t10, "weights", "softmaxCrossEntropy")), xt(s.shape, a.shape, "Error in softmaxCrossEntropy: "), o > 0) {
let u = ke(o), c = ke(1), l = ke(s.shape[1]);
s = be(se(s, Te(c, u)), Ke(u, l));
}
let p = Y6(s, a);
return cr(p, i, n);
}
var hN = N({ softmaxCrossEntropy_: Q6 });
function Z6(r, e, t10, o) {
let n = v(r, "indices", "sparseFillEmptyRows", "int32"), s = v(e, "values", "sparseFillEmptyRows"), a = v(t10, "denseShape", "sparseFillEmptyRows", "int32"), i = v(o, "defaultValue", "sparseFillEmptyRows", s.dtype);
if (n.rank !== 2)
throw new Error(`Indices should be Tensor2D but received shape
${n.shape}`);
if (s.rank !== 1)
throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);
if (a.rank !== 1)
throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);
if (i.rank !== 0)
throw new Error(`Default value should be a scalar but received shape ${i.shape}`);
let p = { indices: n, values: s, denseShape: a, defaultValue: i }, u = T.runKernel(Vi, p);
return { outputIndices: u[0], outputValues: u[1], emptyRowIndicator: u[2], reverseIndexMap: u[3] };
}
var gN = N({ sparseFillEmptyRows_: Z6 });
function J6(r, e, t10) {
let o = v(r, "inputIndices", "sparseReshape", "int32"), n = v(e, "inputShape", "sparseReshape", "int32"), s = v(t10, "newShape", "sparseReshape", "int32");
if (o.rank !== 2)
throw new Error(`Input indices should be Tensor2D but received shape
${o.shape}`);
if (n.rank !== 1)
throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);
if (s.rank !== 1)
throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);
let a = { inputIndices: o, inputShape: n, newShape: s }, i = T.runKernel(Xa, a);
return { outputIndices: i[0], outputShape: i[1] };
}
var xN = N({ sparseReshape_: J6 });
function ej(r, e, t10) {
let o = v(r, "data", "sparseSegmentMean"), n = v(e, "indices", "sparseSegmentMean", "int32"), s = v(t10, "segmentIds", "sparseSegmentMean", "int32");
if (o.rank < 1)
throw new Error("Data should be at least 1 dimensional but received scalar");
if (n.rank !== 1)
throw new Error(`Indices should be Tensor1D but received shape
${n.shape}`);
if (s.rank !== 1)
throw new Error(`Segment ids should be Tensor1D but received shape
${s.shape}`);
let a = { data: o, indices: n, segmentIds: s };
return T.runKernel(Wi, a);
}
var yN = N({ sparseSegmentMean_: ej });
function tj(r, e, t10) {
let o = v(r, "data", "sparseSegmentSum"), n = v(e, "indices", "sparseSegmentSum", "int32"), s = v(t10, "segmentIds", "sparseSegmentSum", "int32");
if (o.rank < 1)
throw new Error("Data should be at least 1 dimensional but received scalar");
if (n.rank !== 1)
throw new Error(`Indices should be Tensor1D but received shape
${n.shape}`);
if (s.rank !== 1)
throw new Error(`Segment ids should be Tensor1D but received shape
${s.shape}`);
let a = { data: o, indices: n, segmentIds: s };
return T.runKernel(Ui, a);
}
var bN = N({ sparseSegmentSum_: tj });
function rj(r, e, t10, o, n, s, a, i) {
let p = v(r, "data", "stringNGrams", "string");
if (p.dtype !== "string")
throw new Error("Data must be of datatype string");
if (p.shape.length !== 1)
throw new Error(`Data must be a vector, saw: ${p.shape}`);
let u = v(e, "dataSplits", "stringNGrams");
if (u.dtype !== "int32")
throw new Error("Data splits must be of datatype int32");
let c = { separator: t10, nGramWidths: o, leftPad: n, rightPad: s, padWidth: a, preserveShortSequences: i }, l = { data: p, dataSplits: u }, m = T.runKernel(ma, l, c);
return { nGrams: m[0], nGramsSplits: m[1] };
}
var CN = N({ stringNGrams_: rj });
function oj(r, e, t10 = true) {
let o = v(r, "input", "stringSplit", "string"), n = v(e, "delimiter", "stringSplit", "string");
if (o.rank !== 1)
throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);
if (n.rank !== 0)
throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);
let s = { skipEmpty: t10 }, a = { input: o, delimiter: n }, i = T.runKernel(Hi, a, s);
return { indices: i[0], values: i[1], shape: i[2] };
}
var wN = N({ stringSplit_: oj });
function nj(r, e) {
let t10 = v(r, "input", "stringToHashBucketFast", "string"), o = { numBuckets: e };
if (e <= 0)
throw new Error("Number of buckets must be at least 1");
let n = { input: t10 };
return T.runKernel(Ki, n, o);
}
var SN = N({ stringToHashBucketFast_: nj });
function sj(r, e, t10, o = true) {
let n = v(r, "input", "staticRegexReplace", "string"), s = { pattern: e, rewrite: t10, replaceGlobal: o };
return T.runKernel(_u, { x: n }, s);
}
var IN = N({ staticRegexReplace_: sj });
var aj = { fft: pc, ifft: Hu, rfft: cc, irfft: Wd };
var ij = { hammingWindow: z1, hannWindow: jd, frame: Xd, stft: V1 };
var uj = { flipLeftRight: U1, grayscaleToRGB: G1, resizeNearestNeighbor: tN, resizeBilinear: eN, rotateWithOffset: H1, cropAndResize: W1, nonMaxSuppression: K1, nonMaxSuppressionAsync: X1, nonMaxSuppressionWithScore: Y1, nonMaxSuppressionWithScoreAsync: Q1, nonMaxSuppressionPadded: Z1, nonMaxSuppressionPaddedAsync: J1, threshold: rN, transform: oN };
var pj = { bandPart: nN, gramSchmidt: sN, qr: iN };
var cj = { absoluteDifference: uN, computeWeightedLoss: cr, cosineDistance: pN, hingeLoss: cN, huberLoss: lN, logLoss: mN, meanSquaredError: dN, sigmoidCrossEntropy: fN, softmaxCrossEntropy: hN };
var lj = { sparseFillEmptyRows: gN, sparseReshape: xN, sparseSegmentMean: yN, sparseSegmentSum: bN };
var mj = { stringNGrams: CN, stringSplit: wN, stringToHashBucketFast: SN, staticRegexReplace: IN };
var vN = {};
He(vN, { Serializable: () => $l, SerializationMap: () => Na, registerClass: () => Lw });
var $l = class {
getClassName() {
return this.constructor.className;
}
static fromConfig(e, t10) {
return new e(t10);
}
};
var Na = class {
constructor() {
this.classNameMap = {};
}
static getMap() {
return Na.instance == null && (Na.instance = new Na()), Na.instance;
}
static register(e) {
Na.getMap().classNameMap[e.className] = [e, e.fromConfig];
}
};
function Lw(r) {
$(r.className != null, () => "Class being registered does not have the static className property defined."), $(typeof r.className == "string", () => "className is required to be a string, but got type " + typeof r.className), $(r.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Na.register(r);
}
var kr = class extends $l {
minimize(e, t10 = false, o) {
let { value: n, grads: s } = this.computeGradients(e, o);
if (o != null) {
let a = o.map((i) => ({ name: i.name, tensor: s[i.name] }));
this.applyGradients(a);
} else
this.applyGradients(s);
return Ot(s), t10 ? n : (n.dispose(), null);
}
get iterations() {
return this.iterations_ == null && (this.iterations_ = 0), this.iterations_;
}
incrementIterations() {
this.iterations_ = this.iterations + 1;
}
computeGradients(e, t10) {
return vw(e, t10);
}
dispose() {
this.iterations_ != null && Ot(this.iterations_);
}
async saveIterations() {
return this.iterations_ == null && (this.iterations_ = 0), { name: "iter", tensor: ke(this.iterations_, "int32") };
}
async getWeights() {
throw new Error("getWeights() is not implemented for this optimizer yet.");
}
async setWeights(e) {
throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
}
async extractIterations(e) {
return this.iterations_ = (await e[0].tensor.data())[0], e.slice(1);
}
};
Object.defineProperty(kr, Symbol.hasInstance, { value: (r) => r.minimize != null && r.computeGradients != null && r.applyGradients != null });
var Yu = class extends kr {
static get className() {
return "Adadelta";
}
constructor(e, t10, o = null) {
super(), this.learningRate = e, this.rho = t10, this.epsilon = o, this.accumulatedGrads = [], this.accumulatedUpdates = [], o == null && (this.epsilon = T.backend.epsilon());
}
applyGradients(e) {
(Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
let s = T.registeredVariables[o], a = false;
this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accum_grad`, variable: De(() => Ht(s).variable(a)) }), this.accumulatedUpdates[n] == null && (this.accumulatedUpdates[n] = { originalName: `${o}/accum_var`, variable: De(() => Ht(s).variable(a)) });
let i = Array.isArray(e) ? e[n].tensor : e[o];
if (i == null)
return;
let p = this.accumulatedGrads[n].variable, u = this.accumulatedUpdates[n].variable;
De(() => {
let c = be(se(p, this.rho), se(Jt(i), 1 - this.rho)), l = se(Ke(Rr(be(u, this.epsilon)), Rr(be(p, this.epsilon))), i), m = be(se(u, this.rho), se(Jt(l), 1 - this.rho));
p.assign(c), u.assign(m);
let d = be(se(l, -this.learningRate), s);
s.assign(d);
});
}), this.incrementIterations();
}
dispose() {
this.accumulatedUpdates != null && (Ot(this.accumulatedGrads.map((e) => e.variable)), Ot(this.accumulatedUpdates.map((e) => e.variable)));
}
async getWeights() {
let e = [...this.accumulatedGrads, ...this.accumulatedUpdates];
return [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable })));
}
async setWeights(e) {
e = await this.extractIterations(e);
let t10 = e.length / 2, o = false;
this.accumulatedGrads = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedUpdates = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) }));
}
getConfig() {
return { learningRate: this.learningRate, rho: this.rho, epsilon: this.epsilon };
}
static fromConfig(e, t10) {
return new e(t10.learningRate, t10.rho, t10.epsilon);
}
};
var Qu = class extends kr {
static get className() {
return "Adagrad";
}
constructor(e, t10 = 0.1) {
super(), this.learningRate = e, this.initialAccumulatorValue = t10, this.accumulatedGrads = [];
}
applyGradients(e) {
(Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
let s = T.registeredVariables[o];
this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accumulator`, variable: De(() => Sa(s.shape, this.initialAccumulatorValue).variable(false)) });
let a = Array.isArray(e) ? e[n].tensor : e[o];
if (a == null)
return;
let i = this.accumulatedGrads[n].variable;
De(() => {
let p = be(i, Jt(a));
i.assign(p);
let u = be(se(Ke(a, Rr(be(p, T.backend.epsilon()))), -this.learningRate), s);
s.assign(u);
});
}), this.incrementIterations();
}
dispose() {
this.accumulatedGrads != null && Ot(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 t10 = false;
this.accumulatedGrads = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t10) }));
}
getConfig() {
return { learningRate: this.learningRate, initialAccumulatorValue: this.initialAccumulatorValue };
}
static fromConfig(e, t10) {
return new e(t10.learningRate, t10.initialAccumulatorValue);
}
};
var Zu = class extends kr {
static get className() {
return "Adam";
}
constructor(e, t10, o, n = null) {
super(), this.learningRate = e, this.beta1 = t10, this.beta2 = o, this.epsilon = n, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], De(() => {
this.accBeta1 = ke(t10).variable(), this.accBeta2 = ke(o).variable();
}), n == null && (this.epsilon = T.backend.epsilon());
}
applyGradients(e) {
let t10 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e);
De(() => {
let o = Te(1, this.accBeta1), n = Te(1, this.accBeta2);
t10.forEach((s, a) => {
let i = T.registeredVariables[s], p = false;
this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: De(() => Ht(i).variable(p)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${s}/v`, variable: De(() => Ht(i).variable(p)) });
let u = Array.isArray(e) ? e[a].tensor : e[s];
if (u == null)
return;
let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedSecondMoment[a].variable, m = be(se(c, this.beta1), se(u, 1 - this.beta1)), d = be(se(l, this.beta2), se(Jt(u), 1 - this.beta2)), f = Ke(m, o), h = Ke(d, n);
c.assign(m), l.assign(d);
let g = be(se(Ke(f, be(Rr(h), this.epsilon)), -this.learningRate), i);
i.assign(g);
}), this.accBeta1.assign(se(this.accBeta1, this.beta1)), this.accBeta2.assign(se(this.accBeta2, this.beta2));
}), this.incrementIterations();
}
dispose() {
this.accBeta1.dispose(), this.accBeta2.dispose(), this.accumulatedFirstMoment != null && Ot(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedSecondMoment != null && Ot(this.accumulatedSecondMoment.map((e) => e.variable));
}
async getWeights() {
let e = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
return [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable })));
}
async setWeights(e) {
e = await this.extractIterations(e), De(() => {
this.accBeta1.assign(ri(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(ri(this.beta2, this.iterations_ + 1));
});
let t10 = e.length / 2, o = false;
this.accumulatedFirstMoment = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedSecondMoment = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) }));
}
getConfig() {
return { learningRate: this.learningRate, beta1: this.beta1, beta2: this.beta2, epsilon: this.epsilon };
}
static fromConfig(e, t10) {
return new e(t10.learningRate, t10.beta1, t10.beta2, t10.epsilon);
}
};
var Ju = class extends kr {
static get className() {
return "Adamax";
}
constructor(e, t10, o, n = null, s = 0) {
super(), this.learningRate = e, this.beta1 = t10, this.beta2 = o, this.epsilon = n, this.decay = s, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], De(() => {
this.iteration = ke(0).variable(), this.accBeta1 = ke(t10).variable();
}), n == null && (this.epsilon = T.backend.epsilon());
}
applyGradients(e) {
let t10 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e);
De(() => {
let o = Te(1, this.accBeta1), n = Ke(-this.learningRate, be(se(this.iteration, this.decay), 1));
t10.forEach((s, a) => {
let i = T.registeredVariables[s], p = false;
this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: Ht(i).variable(p) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${s}/v`, variable: Ht(i).variable(p) });
let u = Array.isArray(e) ? e[a].tensor : e[s];
if (u == null)
return;
let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedWeightedInfNorm[a].variable, m = be(se(c, this.beta1), se(u, 1 - this.beta1)), d = se(l, this.beta2), f = Zt(u), h = Ed(d, f);
c.assign(m), l.assign(h);
let g = be(se(Ke(n, o), Ke(m, be(h, this.epsilon))), i);
i.assign(g);
}), this.iteration.assign(be(this.iteration, 1)), this.accBeta1.assign(se(this.accBeta1, this.beta1));
}), this.incrementIterations();
}
dispose() {
this.accBeta1.dispose(), this.iteration.dispose(), this.accumulatedFirstMoment != null && Ot(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedWeightedInfNorm != null && Ot(this.accumulatedWeightedInfNorm.map((e) => e.variable));
}
async getWeights() {
throw new Error("getWeights() is not implemented for Adamax yet.");
}
async setWeights(e) {
throw new Error("setWeights() is not implemented for Adamax yet.");
}
getConfig() {
return { learningRate: this.learningRate, beta1: this.beta1, beta2: this.beta2, epsilon: this.epsilon, decay: this.decay };
}
static fromConfig(e, t10) {
return new e(t10.learningRate, t10.beta1, t10.beta2, t10.epsilon, t10.decay);
}
};
var ii = class extends kr {
static get className() {
return "SGD";
}
constructor(e) {
super(), this.learningRate = e, this.setLearningRate(e);
}
applyGradients(e) {
(Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
let s = Array.isArray(e) ? e[n].tensor : e[o];
if (s == null)
return;
let a = T.registeredVariables[o];
De(() => {
let i = be(se(this.c, s), a);
a.assign(i);
});
}), this.incrementIterations();
}
setLearningRate(e) {
this.learningRate = e, this.c != null && this.c.dispose(), this.c = Er(ke(-e));
}
dispose() {
this.c.dispose();
}
async getWeights() {
return [await this.saveIterations()];
}
async setWeights(e) {
if (e = await this.extractIterations(e), e.length !== 0)
throw new Error("SGD optimizer does not have settable weights.");
}
getConfig() {
return { learningRate: this.learningRate };
}
static fromConfig(e, t10) {
return new e(t10.learningRate);
}
};
var ep = class extends ii {
static get className() {
return "Momentum";
}
constructor(e, t10, o = false) {
super(e), this.learningRate = e, this.momentum = t10, this.useNesterov = o, this.accumulations = [], this.m = ke(this.momentum);
}
applyGradients(e) {
(Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
let s = T.registeredVariables[o];
this.accumulations[n] == null && (this.accumulations[n] = { originalName: `${o}/momentum`, variable: De(() => Ht(s).variable(false)) });
let a = this.accumulations[n].variable, i = Array.isArray(e) ? e[n].tensor : e[o];
i != null && De(() => {
let p, u = be(se(this.m, a), i);
this.useNesterov ? p = be(se(this.c, be(i, se(u, this.m))), s) : p = be(se(this.c, u), s), a.assign(u), s.assign(p);
});
}), this.incrementIterations();
}
dispose() {
this.m.dispose(), this.accumulations != null && Ot(this.accumulations.map((e) => e.variable));
}
setMomentum(e) {
this.momentum = e;
}
async getWeights() {
return [await this.saveIterations()].concat(this.accumulations.map((e) => ({ name: e.originalName, tensor: e.variable })));
}
async setWeights(e) {
e = await this.extractIterations(e);
let t10 = false;
this.accumulations = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t10) }));
}
getConfig() {
return { learningRate: this.learningRate, momentum: this.momentum, useNesterov: this.useNesterov };
}
static fromConfig(e, t10) {
return new e(t10.learningRate, t10.momentum, t10.useNesterov);
}
};
var tp = class extends kr {
static get className() {
return "RMSProp";
}
constructor(e, t10 = 0.9, o = 0, n = null, s = false) {
if (super(), this.learningRate = e, this.decay = t10, this.momentum = o, this.epsilon = n, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = s, n == null && (this.epsilon = T.backend.epsilon()), e == null)
throw new Error("learningRate for RMSPropOptimizer must be defined.");
}
applyGradients(e) {
(Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
let s = T.registeredVariables[o], a = false;
this.accumulatedMeanSquares[n] == null && (this.accumulatedMeanSquares[n] = { originalName: `${o}/rms`, variable: De(() => Ht(s).variable(a)) }), this.accumulatedMoments[n] == null && (this.accumulatedMoments[n] = { originalName: `${o}/momentum`, variable: De(() => Ht(s).variable(a)) }), this.accumulatedMeanGrads[n] == null && this.centered && (this.accumulatedMeanGrads[n] = { originalName: `${o}/mg`, variable: De(() => Ht(s).variable(a)) });
let i = Array.isArray(e) ? e[n].tensor : e[o];
if (i == null)
return;
let p = this.accumulatedMeanSquares[n].variable, u = this.accumulatedMoments[n].variable;
De(() => {
let c = be(se(p, this.decay), se(Jt(i), 1 - this.decay));
if (this.centered) {
let l = this.accumulatedMeanGrads[n].variable, m = be(se(l, this.decay), se(i, 1 - this.decay)), d = Ke(se(i, this.learningRate), Rr(Te(c, be(Jt(m), this.epsilon)))), f = be(se(u, this.momentum), d);
p.assign(c), l.assign(m), u.assign(f);
let h = Te(s, f);
s.assign(h);
} else {
let l = be(se(p, this.decay), se(Jt(i), 1 - this.decay)), m = be(se(u, this.momentum), Ke(se(i, this.learningRate), Rr(be(l, this.epsilon))));
p.assign(l), u.assign(m);
let d = Te(s, m);
s.assign(d);
}
});
}), this.incrementIterations();
}
dispose() {
this.accumulatedMeanSquares != null && Ot(this.accumulatedMeanSquares.map((e) => e.variable)), this.accumulatedMeanGrads != null && this.centered && Ot(this.accumulatedMeanGrads.map((e) => e.variable)), this.accumulatedMoments != null && Ot(this.accumulatedMoments.map((e) => e.variable));
}
async getWeights() {
let e = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
return this.centered && e.push(...this.accumulatedMeanGrads), [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable })));
}
async setWeights(e) {
e = await this.extractIterations(e);
let t10 = this.centered ? e.length / 3 : e.length / 2, o = false;
this.accumulatedMeanSquares = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedMoments = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.centered && (this.accumulatedMeanGrads = e.slice(t10 * 2, t10 * 3).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })));
}
getConfig() {
return { learningRate: this.learningRate, decay: this.decay, momentum: this.momentum, epsilon: this.epsilon, centered: this.centered };
}
static fromConfig(e, t10) {
return new e(t10.learningRate, t10.decay, t10.momentum, t10.epsilon, t10.centered);
}
};
var dj = [Yu, Qu, Zu, Ju, ep, tp, ii];
function kN() {
for (let r of dj)
Lw(r);
}
var pi = {};
He(pi, { browserFiles: () => TN, browserHTTPRequest: () => EN, concatenateArrayBuffers: () => tc, copyModel: () => ok, decodeWeights: () => rd, encodeWeights: () => V0, fromMemory: () => RN, fromMemorySync: () => Gw, getLoadHandlers: () => q0, getModelArtifactsForJSON: () => rc, getModelArtifactsForJSONSync: () => mw, getModelArtifactsInfoForJSON: () => ga, getSaveHandlers: () => K0, getWeightSpecs: () => nd, http: () => tf, isHTTPScheme: () => ef, listModels: () => tk, loadWeights: () => _N, moveModel: () => nk, registerLoadRouter: () => H0, registerSaveRouter: () => G0, removeModel: () => rk, weightsLoaderFactory: () => Ww, withSaveHandler: () => DN, withSaveHandlerSync: () => AN });
var fj = "model";
var hj = ".json";
var gj = ".weights.bin";
function NN(r) {
return new Promise((e) => setTimeout(e)).then(r);
}
var ui = class {
constructor(e) {
if (!P().getBool("IS_BROWSER"))
throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
e.startsWith(ui.URL_SCHEME) && (e = e.slice(ui.URL_SCHEME.length)), (e == null || e.length === 0) && (e = fj), this.modelJsonFileName = e + hj, this.weightDataFileName = e + gj;
}
async save(e) {
if (typeof document == "undefined")
throw new Error("Browser downloads are not supported in this environment since `document` is not present");
let t10 = window.URL.createObjectURL(new Blob([e.weightData], { type: "application/octet-stream" }));
if (e.modelTopology instanceof ArrayBuffer)
throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
{
let o = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], n = od(e, o), s = window.URL.createObjectURL(new Blob([JSON.stringify(n)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
if (a.download = this.modelJsonFileName, a.href = s, await NN(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) {
let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
i.download = this.weightDataFileName, i.href = t10, await NN(() => i.dispatchEvent(new MouseEvent("click")));
}
return { modelArtifactsInfo: ga(e) };
}
}
};
ui.URL_SCHEME = "downloads://";
var Bw = class {
constructor(e) {
if (e == null || e.length < 1)
throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);
this.jsonFile = e[0], this.weightsFiles = e.slice(1);
}
async load() {
return new Promise((e, t10) => {
let o = new FileReader();
o.onload = (n) => {
let s = JSON.parse(n.target.result), a = s.modelTopology;
if (a == null) {
t10(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));
return;
}
if (s.weightsManifest == null) {
t10(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));
return;
}
if (this.weightsFiles.length === 0) {
e({ modelTopology: a });
return;
}
let p = rc(s, (u) => this.loadWeights(u));
e(p);
}, o.onerror = (n) => t10(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), o.readAsText(this.jsonFile);
});
}
loadWeights(e) {
let t10 = [], o = [];
for (let a of e)
t10.push(...a.weights), o.push(...a.paths);
let n = this.checkManifestAndWeightFiles(e), s = o.map((a) => this.loadWeightsFile(a, n[a]));
return Promise.all(s).then((a) => [t10, tc(a)]);
}
loadWeightsFile(e, t10) {
return new Promise((o, n) => {
let s = new FileReader();
s.onload = (a) => {
let i = a.target.result;
o(i);
}, s.onerror = (a) => n(`Failed to weights data from file of path '${e}'.`), s.readAsArrayBuffer(t10);
});
}
checkManifestAndWeightFiles(e) {
let t10 = [], o = this.weightsFiles.map((s) => lw(s.name)), n = {};
for (let s of e)
s.paths.forEach((a) => {
let i = lw(a);
if (t10.indexOf(i) !== -1)
throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);
if (t10.push(i), o.indexOf(i) === -1)
throw new Error(`Weight file with basename '${i}' is not provided.`);
n[a] = this.weightsFiles[o.indexOf(i)];
});
if (t10.length !== this.weightsFiles.length)
throw new Error(`Mismatch in the number of files in weights manifest (${t10.length}) and the number of weight files provided (${this.weightsFiles.length}).`);
return n;
}
};
var xj = (r) => P().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(ui.URL_SCHEME) ? yj(r.slice(ui.URL_SCHEME.length)) : null;
ft.registerSaveRouter(xj);
function yj(r = "model") {
return new ui(r);
}
function TN(r) {
return new Bw(r);
}
var Jd = class {
constructor(e) {
if (this.shards = [], this.previousShardIndex = 0, e instanceof Array || (e = [e]), e = e.map((o) => Pt(o) ? o.buffer : o), e.length === 0)
return;
this.bufferUniformSize = e[0].byteLength;
let t10 = 0;
for (let o = 0; o < e.length; o++) {
let n = e[o];
o !== e.length - 1 && n.byteLength !== this.bufferUniformSize && (this.bufferUniformSize = void 0);
let s = t10 + n.byteLength;
this.shards.push({ buffer: n, start: t10, end: s }), t10 = s;
}
this.shards.length === 0 && (this.byteLength = 0), this.byteLength = this.shards[this.shards.length - 1].end;
}
slice(e = 0, t10 = this.byteLength) {
if (e = isNaN(Number(e)) ? 0 : e, t10 = isNaN(Number(t10)) ? 0 : t10, e = Math.max(0, e), t10 = Math.min(this.byteLength, t10), t10 <= e)
return new ArrayBuffer(0);
let o = this.findShardForByte(e);
if (o === -1)
throw new Error(`Could not find start shard for byte ${e}`);
let n = t10 - e, s = new ArrayBuffer(n), a = new Uint8Array(s), i = 0;
for (let p = o; p < this.shards.length; p++) {
let u = this.shards[p], l = e + i - u.start, m = i, f = Math.min(t10, u.end) - u.start, h = new Uint8Array(u.buffer.slice(l, f));
if (a.set(h, m), i += h.length, t10 < u.end)
break;
}
return s;
}
findShardForByte(e) {
if (this.shards.length === 0 || e < 0 || e >= this.byteLength)
return -1;
if (this.bufferUniformSize != null)
return this.previousShardIndex = Math.floor(e / this.bufferUniformSize), this.previousShardIndex;
function t10(n) {
return e < n.start ? -1 : e >= n.end ? 1 : 0;
}
if (t10(this.shards[this.previousShardIndex]) === 0)
return this.previousShardIndex;
let o = bj(this.shards, t10);
return o === -1 ? -1 : (this.previousShardIndex = o, this.previousShardIndex);
}
};
function bj(r, e) {
let t10 = 0, o = r.length;
for (; t10 <= o; ) {
let n = Math.floor((o - t10) / 2) + t10, s = e(r[n]);
if (s === 0)
return n;
s < 0 ? o = n : t10 = n + 1;
}
return -1;
}
function zw(r, e, t10, o) {
a(r), t10 = t10 == null ? 0 : t10, o = o == null ? 1 : o, i(t10, o);
let n = 0, s = (p) => (p.then((u) => {
let c = t10 + ++n / r.length * (o - t10);
return e(c), u;
}), p);
function a(p) {
$(p != null && Array.isArray(p) && p.length > 0, () => "promises must be a none empty array");
}
function i(p, u) {
$(p >= 0 && p <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${p}`), $(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${u}`), $(u >= p, () => `startFraction must be no more than endFraction, but got startFraction ${p} and endFraction ${u}`);
}
return Promise.all(r.map(s));
}
async function Vw(r, e) {
e == null && (e = {});
let t10 = e.fetchFunc == null ? P().platform.fetch : e.fetchFunc, o = r.map((l) => t10(l, e.requestInit, { isBinary: true })), n = 0, s = 0.5, i = (e.onProgress == null ? await Promise.all(o) : await zw(o, e.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1;
return e.onProgress == null ? await Promise.all(i) : await zw(i, e.onProgress, p, u);
}
async function _N(r, e = "", t10, o) {
return Ww((a) => Vw(a, { requestInit: o }))(r, e, t10);
}
function Ww(r) {
return async (e, t10 = "", o) => {
let n = e.map(() => false), s = {}, a = o != null ? o.map(() => false) : [], i = [];
if (e.forEach((d, f) => {
let h = 0;
d.weights.forEach((g) => {
let x = "quantization" in g ? g.quantization.dtype : g.dtype, b = Cl[x] * Ue(g.shape), w = () => {
n[f] = true, s[f] == null && (s[f] = []), s[f].push({ manifestEntry: g, groupOffset: h, sizeBytes: b });
};
o != null ? o.forEach((S, k) => {
S === g.name && (w(), a[k] = true);
}) : w(), i.push(g.name), h += b;
});
}), !a.every((d) => d)) {
let d = o.filter((f, h) => !a[h]);
throw new Error(`Could not find weights in manifest with names: ${d.join(", ")}.
Manifest JSON has weights with names: ${i.join(", ")}.`);
}
let p = n.reduce((d, f, h) => (f && d.push(h), d), []), u = [];
p.forEach((d) => {
e[d].paths.forEach((f) => {
let h = t10 + (t10.endsWith("/") ? "" : "/") + f;
u.push(h);
});
});
let c = await r(u), l = {}, m = 0;
return p.forEach((d) => {
let f = e[d].paths.length, h = new Jd(c.slice(m, m + f));
s[d].forEach((x) => {
let b = h.slice(x.groupOffset, x.groupOffset + x.sizeBytes), w = rd(b, [x.manifestEntry]);
for (let S in w)
l[S] = w[S];
}), m += f;
}), l;
};
}
var Cj = "application/octet-stream";
var wj = "application/json";
var El = class {
constructor(e, t10) {
if (this.DEFAULT_METHOD = "POST", t10 == null && (t10 = {}), this.weightPathPrefix = t10.weightPathPrefix, this.onProgress = t10.onProgress, this.weightUrlConverter = t10.weightUrlConverter, t10.fetchFunc != null ? ($(typeof t10.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t10.fetchFunc) : this.fetch = P().platform.fetch, $(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && $(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t10.requestInit != null && t10.requestInit.body != null)
throw new Error("requestInit is expected to have no pre-existing body, but has one.");
this.requestInit = t10.requestInit || {};
}
async save(e) {
if (e.modelTopology instanceof ArrayBuffer)
throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
let t10 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
t10.body = new FormData();
let o = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], n = od(e, o);
t10.body.append("model.json", new Blob([JSON.stringify(n)], { type: wj }), "model.json"), e.weightData != null && t10.body.append("model.weights.bin", new Blob([e.weightData], { type: Cj }), "model.weights.bin");
let s = await this.fetch(this.path, t10);
if (s.ok)
return { modelArtifactsInfo: ga(e), responses: [s] };
throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${s.status}.`);
}
async load() {
let e = await this.fetch(this.path, this.requestInit);
if (!e.ok)
throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);
let t10;
try {
t10 = await e.json();
} catch (s) {
let a = `Failed to parse model JSON of response from ${this.path}.`;
throw this.path.endsWith(".pb") ? a += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository." : a += " Please make sure the server is serving valid JSON for this request.", new Error(a);
}
let o = t10.modelTopology, n = t10.weightsManifest;
if (o == null && n == null)
throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
return rc(t10, (s) => this.loadWeights(s));
}
async loadWeights(e) {
let t10 = Array.isArray(this.path) ? this.path[1] : this.path, [o, n] = Sj(t10), s = this.weightPathPrefix || o, a = nd(e), i = [], p = [];
for (let c of e)
for (let l of c.paths)
this.weightUrlConverter != null ? p.push(this.weightUrlConverter(l)) : i.push(s + l + n);
this.weightUrlConverter && i.push(...await Promise.all(p));
let u = await Vw(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
return [a, tc(u)];
}
};
El.URL_SCHEME_REGEX = /^https?:\/\//;
function Sj(r) {
let e = r.lastIndexOf("/"), t10 = r.lastIndexOf("?"), o = r.substring(0, e), n = t10 > e ? r.substring(t10) : "";
return [o + "/", n];
}
function ef(r) {
return r.match(El.URL_SCHEME_REGEX) != null;
}
var $N = (r, e) => {
if (typeof fetch == "undefined" && (e == null || e.fetchFunc == null))
return null;
{
let t10 = true;
if (Array.isArray(r) ? t10 = r.every((o) => ef(o)) : t10 = ef(r), t10)
return tf(r, e);
}
return null;
};
ft.registerSaveRouter($N);
ft.registerLoadRouter($N);
function tf(r, e) {
return new El(r, e);
}
function EN(r, e) {
return tf(r, e);
}
var Rl = class {
constructor(e) {
this.modelArtifacts = e;
}
load() {
return this.modelArtifacts;
}
};
var rf = class {
constructor(e) {
this.saveHandler = e;
}
save(e) {
return this.saveHandler(e);
}
};
var Uw = class {
constructor(e) {
e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t10) => Promise.resolve(e.save(t10)));
}
};
function RN(r, e, t10, o) {
let n = arguments;
return new Uw(Gw(...n));
}
function Gw(r, e, t10, o) {
return arguments.length === 1 ? r.modelTopology != null || r.weightSpecs != null ? new Rl(r) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Rl({ modelTopology: r })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Rl({ modelTopology: r, weightSpecs: e, weightData: t10, trainingConfig: o }));
}
function DN(r) {
return new rf(r);
}
function AN(r) {
return new rf(r);
}
var PN = {};
He(PN, { confusionMatrix: () => FN });
function Ij(r, e, t10) {
let o = v(r, "labels", "confusionMatrix"), n = v(e, "predictions", "confusionMatrix");
$(t10 == null || t10 > 0 && Number.isInteger(t10), () => `If provided, numClasses must be a positive integer, but got ${t10}`), $(o.rank === 1, () => `Expected the rank of labels to be 1, but got ${o.rank}`), $(n.rank === 1, () => `Expected the rank of predictions to be 1, but got ${n.rank}`), $(o.shape[0] === n.shape[0], () => `Mismatch in the number of examples: ${o.shape[0]} vs. ${n.shape[0]}. Labels and predictions should have the same number of elements.`), $(t10 > 0 && Number.isInteger(t10), () => `numClasses is required to be a positive integer, but got ${t10}`);
let s = Tl(Ye(o, "int32"), t10), a = Tl(Ye(n, "int32"), t10), i = dc(s), p = Qe(i, a);
return Ye(p, "int32");
}
var FN = N({ confusionMatrix_: Ij });
var MN = {};
He(MN, { fromPixels: () => Ej, fromPixelsAsync: () => _j, toPixels: () => $j });
var rp;
function ON(r, e = 3) {
if (e > 4)
throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
if (r == null)
throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
let t10 = false, o = false, n = false, s = false, a = false, i = false;
if (r.data instanceof Uint8Array)
t10 = true;
else if (typeof ImageData != "undefined" && r instanceof ImageData)
o = true;
else if (typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement)
n = true;
else if (typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement)
s = true;
else if (r.getContext != null)
a = true;
else if (typeof ImageBitmap != "undefined" && r instanceof ImageBitmap)
i = true;
else
throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${r.constructor.name}`);
if (fl($u, T.backendName) != null) {
let f = { pixels: r }, h = { numChannels: e };
return T.runKernel($u, f, h);
}
let [u, c] = n ? [r.videoWidth, r.videoHeight] : [r.width, r.height], l;
if (a)
l = r.getContext("2d").getImageData(0, 0, u, c).data;
else if (o || t10)
l = r.data;
else if (s || n || i) {
if (rp == null)
if (typeof document == "undefined")
if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined")
rp = new OffscreenCanvas(1, 1).getContext("2d");
else
throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
else
rp = document.createElement("canvas").getContext("2d", { willReadFrequently: true });
rp.canvas.width = u, rp.canvas.height = c, rp.drawImage(r, 0, 0, u, c), l = rp.getImageData(0, 0, u, c).data;
}
let m;
if (e === 4)
m = new Int32Array(l);
else {
let f = u * c;
m = new Int32Array(f * e);
for (let h = 0; h < f; h++)
for (let g = 0; g < e; ++g)
m[h * e + g] = l[h * 4 + g];
}
return Hd(m, [c, u, e], "int32");
}
function vj(r) {
return r != null && r.data instanceof Uint8Array;
}
function kj() {
return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap");
}
function Nj(r) {
return r != null && r.width !== 0 && r.height !== 0;
}
function Tj(r) {
return kj() && !(r instanceof ImageBitmap) && Nj(r) && !vj(r);
}
async function _j(r, e = 3) {
let t10 = null;
if (P().getBool("WRAP_TO_IMAGEBITMAP") && Tj(r)) {
let o;
try {
o = await createImageBitmap(r, { premultiplyAlpha: "none" });
} catch (n) {
o = null;
}
o != null && o.width === r.width && o.height === r.height ? t10 = o : t10 = r;
} else
t10 = r;
return ON(t10, e);
}
async function $j(r, e) {
let t10 = v(r, "img", "toPixels");
if (!(r instanceof pt)) {
let u = t10;
t10 = Ye(u, "int32"), u.dispose();
}
if (t10.rank !== 2 && t10.rank !== 3)
throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${t10.rank}.`);
let [o, n] = t10.shape.slice(0, 2), s = t10.rank === 2 ? 1 : t10.shape[2];
if (s > 4 || s === 2)
throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);
if (t10.dtype !== "float32" && t10.dtype !== "int32")
throw new Error(`Unsupported type for toPixels: ${t10.dtype}. Please use float32 or int32 tensors.`);
let a = await t10.data(), i = t10.dtype === "float32" ? 255 : 1, p = new Uint8ClampedArray(n * o * 4);
for (let u = 0; u < o * n; ++u) {
let c = [0, 0, 0, 255];
for (let m = 0; m < s; m++) {
let d = a[u * s + m];
if (t10.dtype === "float32") {
if (d < 0 || d > 1)
throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${d}.`);
} else if (t10.dtype === "int32" && (d < 0 || d > 255))
throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${d}.`);
s === 1 ? (c[0] = d * i, c[1] = d * i, c[2] = d * i) : c[m] = d * i;
}
let l = u * 4;
p[l + 0] = Math.round(c[0]), p[l + 1] = Math.round(c[1]), p[l + 2] = Math.round(c[2]), p[l + 3] = Math.round(c[3]);
}
if (e != null) {
e.width = n, e.height = o;
let u = e.getContext("2d"), c = new ImageData(p, n, o);
u.putImageData(c, 0, 0);
}
return t10 !== r && t10.dispose(), p;
}
var Ej = N({ fromPixels_: ON });
var of = {};
He(of, { prepareAndValidate: () => LN });
function LN(r, e) {
let t10 = r.shape.length, o = e.shape.length;
if (t10 < 1)
throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${t10}.`);
if (o < 1)
throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${o}.`);
if (e.dtype !== "int32")
throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);
if (e.shape[o - 1] > t10)
throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${e.shape[o - 1]} vs. ${t10}`);
if (Ue(r.shape) === 0)
throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${r.shape}.`);
let n = e.shape, s = n[n.length - 1], a = 1;
for (let l = 0; l < n.length - 1; ++l)
a *= n[l];
let i = r.shape, p = n.slice();
p.pop();
let u = 1;
for (let l = s; l < t10; ++l)
u *= i[l], p.push(i[l]);
let c = [...Us(r.shape).map((l) => l / u), 1].slice(0, s);
return [p, a, u, c];
}
var ct = {};
He(ct, { assertParamsValid: () => Dj, computeFlatOffset: () => Mj, computeOutShape: () => Fj, getNormalizedAxes: () => Pj, isSliceContinous: () => Oj, maskToAxes: () => Aj, parseSliceParams: () => Lj, sliceInfo: () => Bj, startForAxis: () => KN, startIndicesWithElidedDims: () => UN, stopForAxis: () => qN, stopIndicesWithElidedDims: () => GN, stridesForAxis: () => HN, stridesWithElidedDims: () => zN });
var Hw = -2;
var Rj = -1;
function Dj(r, e, t10) {
let o = r.shape.length;
$(o === e.length, () => `Error in slice${o}D: Length of begin ${e} must match the rank of the array (${o}).`), $(o === t10.length, () => `Error in slice${o}D: Length of size ${t10} must match the rank of the array (${o}).`);
for (let n = 0; n < o; ++n)
$(e[n] + t10[n] <= r.shape[n], () => `Error in slice${o}D: begin[${n}] + size[${n}] (${e[n] + t10[n]}) would overflow input.shape[${n}] (${r.shape[n]})`);
}
function Aj(r) {
let e = [], t10 = 0;
for (; r > 0; )
r & 1 && e.push(t10), r /= 2, t10++;
return e;
}
function Fj(r, e, t10) {
let o = [];
for (let n = 0; n < r.length; n++)
o[n] = Math.ceil((e[n] - r[n]) / t10[n]);
return o;
}
function zN(r, e, t10, o) {
let n = [...r];
for (let s = n.length; s < o.length; s++)
n.push(1);
for (let s = 0; s < t10; s++)
s === 0 ? n[e] = 1 : (n.splice(e, 0, 1), n.pop());
return n;
}
function VN(r, e, t10) {
return t10 <= r ? t10 : t10 - (e - 1);
}
function WN(r, e) {
let t10 = [];
for (let o = 0; o < r; o++)
t10.push(e + o);
return t10;
}
function Pj(r, e, t10, o, n, s, a, i, p) {
let u = r.length, c = new Array(u), l = new Array(u), m = new Array(u);
if (e.length && t10 > 0) {
let d = e[0], f = t10 + 1;
c = UN(a, d, f, o, r), l = GN(i, d, f, n, r), m = zN(s, d, f, r);
} else
for (let d = 0; d < u; d++)
c[d] = KN(a, o, s, r, d, p), l[d] = qN(i, n, s, r, d, p), m[d] = HN(s, d, p);
return { begin: c, end: l, strides: m };
}
function UN(r, e, t10, o, n) {
let s = [...n], a = WN(t10, e);
for (let i = 0; i < s.length; i++)
if (a.indexOf(i) > -1)
s[i] = 0;
else {
let p = VN(e, t10, i), u = o[p];
r & 1 << p && (u = 0), s[i] = u;
}
return s;
}
function GN(r, e, t10, o, n) {
let s = [...n], a = WN(t10, e);
for (let i = 0; i < s.length; i++)
if (a.indexOf(i) > -1)
s[i] = Number.MAX_SAFE_INTEGER;
else {
let p = VN(e, t10, i), u = o[p];
r & 1 << p && (u = Number.MAX_SAFE_INTEGER), s[i] = u;
}
for (let i = 0; i < s.length; i++) {
let p = n[i];
s[i] < 0 && (s[i] += p), s[i] = zp(0, s[i], n[i]);
}
return s;
}
function HN(r, e, t10) {
let o = r[e];
return (t10 & 1 << e || o == null) && (o = 1), o;
}
function KN(r, e, t10, o, n, s) {
let a = e[n], i = t10[n] || 1;
(r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MIN_SAFE_INTEGER : a = Number.MAX_SAFE_INTEGER);
let p = o[n];
return a < 0 && (a += p), a = zp(0, a, p - 1), a;
}
function qN(r, e, t10, o, n, s) {
let a = e[n], i = t10[n] || 1;
(r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MAX_SAFE_INTEGER : a = Number.MIN_SAFE_INTEGER);
let p = o[n];
return a < 0 && (a += p), i > 0 ? a = zp(0, a, p) : a = zp(-1, a, p - 1), a;
}
function Oj(r, e, t10) {
let o = t10.length;
for (let n = 0; n < t10.length; n++)
if (t10[n] > 1) {
o = n;
break;
}
for (let n = o + 1; n < t10.length; n++)
if (e[n] > 0 || t10[n] !== r[n])
return false;
return true;
}
function Mj(r, e) {
let t10 = r.length > 0 ? r[r.length - 1] : 1;
for (let o = 0; o < r.length - 1; o++)
t10 += r[o] * e[o];
return t10;
}
function Lj(r, e, t10) {
let o, n = r.shape.length;
typeof e == "number" ? o = [e, ...new Array(n - 1).fill(0)] : e.length < n ? o = e.concat(new Array(n - e.length).fill(0)) : o = e.slice(), o.forEach((a) => {
$(a !== -1, () => "slice() does not support negative begin indexing.");
});
let s;
return t10 == null ? s = new Array(n).fill(-1) : typeof t10 == "number" ? s = [t10, ...new Array(n - 1).fill(-1)] : t10.length < n ? s = t10.concat(new Array(n - t10.length).fill(-1)) : s = t10, s = s.map((a, i) => a >= 0 ? a : ($(a === -1, () => `Negative size values should be exactly -1 but got ${a} for the slice() size at index ${i}.`), r.shape[i] - o[i])), [o, s];
}
function Bj(r, e, t10, o, n, s, a, i, p) {
let u;
if (o == null ? (u = new Array(e.length), u.fill(1)) : u = o, a != null && a & a - 1)
throw new Error("Multiple ellipses in slice is not allowed.");
let c = false, l = { dims: u.length, numAddAxisAfterEllipsis: 0, begin: e.slice(), end: t10.slice(), strides: u.slice(), beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p };
for (let w = 0; w < l.dims; w++)
c && 1 << w & i && l.numAddAxisAfterEllipsis++, 1 << w & a && (c = true);
c || (l.ellipsisMask |= 1 << l.dims, l.dims++);
let m = { dims: r.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false };
zj(l, m);
let d = true, f = true, h = true, g = [], x = [];
for (let w = 0; w < r.length; ++w) {
if (m.strides[w] === 0)
throw Error(`strides[${w}] must be non-zero`);
let S = !!(m.shrinkAxisMask & 1 << w), k = r[w];
if (k === -1) {
g.push(S ? 1 : -1);
continue;
}
let _ = [m.beginMask & 1 << w, m.endMask & 1 << w], E = [m.strides[w] > 0 ? 0 : -1, m.strides[w] > 0 ? k : k - 1];
if (S && m.strides[w] <= 0)
throw Error("only stride 1 allowed on non-range indexing.");
h = h && m.strides[w] === 1;
let R = !!(m.beginMask & 1 << w && m.endMask & 1 << w);
if (m.beginValid && m.endValid) {
if (S) {
let M = m.begin[w] < 0 ? k + m.begin[w] : m.begin[w];
if (m.begin[w] = M, m.end[w] = m.begin[w] + 1, M < 0 || M >= k)
throw Error(`slice index ${m.begin[w]} of dimension ${w} out of bounds.`);
} else
m.begin[w] = BN(m.begin[w], 0, m.strides[w], k, _, E), m.end[w] = BN(m.end[w], 1, m.strides[w], k, _, E);
let O = m.strides[w] === 1 && m.begin[w] === 0 && m.end[w] === k;
d = d && O, f = f && (w === 0 && m.strides[w] === 1 || O);
} else
d = d && m.strides[w] === 1 && R, f = f && (w === 0 && m.strides[w] === 1 || R);
let D, F = false;
if (m.beginValid && m.endValid ? (D = m.end[w] - m.begin[w], F = true) : S ? (D = 1, F = true) : R && k >= 0 && (m.strides[w] < 0 ? D = -k : D = k, F = true), F) {
let O;
D === 0 || D < 0 != m.strides[w] < 0 ? O = 0 : O = Math.trunc(D / m.strides[w]) + (D % m.strides[w] !== 0 ? 1 : 0), g.push(O);
} else
g.push(-1);
}
for (let w = 0; w < m.finalShapeGatherIndices.length; ++w) {
let S = m.finalShapeGatherIndices[w];
S >= 0 ? x.push(g[S]) : S === Hw && x.push(1);
}
return { finalShapeSparse: x.filter((w, S) => m.finalShapeGatherIndices[S] !== Hw), finalShape: x, isIdentity: d, sliceDim0: f, isSimpleSlice: h, begin: m.begin, end: m.end, strides: m.strides };
}
function zj(r, e) {
e.beginMask = 0, e.endMask = 0, e.shrinkAxisMask = 0;
let t10 = 0;
e.beginValid = r.begin != null, e.endValid = r.end != null, e.begin = new Array(e.dims), e.end = new Array(e.dims), e.strides = new Array(e.dims), e.finalShapeGatherIndices = [], e.finalShapeGatherIndicesSparse = [], e.inputShapeGatherIndicesSparse = new Array(e.dims);
for (let o = 0; o < r.dims; o++)
if (1 << o & r.ellipsisMask) {
let n = Math.min(e.dims - (r.dims - o) + 1 + r.numAddAxisAfterEllipsis, e.dims);
for (; t10 < n; t10++)
e.begin[t10] = 0, e.end[t10] = 0, e.strides[t10] = 1, e.beginMask |= 1 << t10, e.endMask |= 1 << t10, e.finalShapeGatherIndices.push(t10), e.finalShapeGatherIndicesSparse.push(-1), e.inputShapeGatherIndicesSparse[t10] = o;
} else if (1 << o & r.newAxisMask)
e.finalShapeGatherIndices.push(Hw), e.finalShapeGatherIndicesSparse.push(-1);
else {
if (t10 === e.begin.length)
throw Error(`Index out of range using input dim ${t10}; input has only ${e.dims} dims, ${e.begin.length}.`);
r.begin != null && (e.begin[t10] = r.begin[o]), r.end != null && (e.end[t10] = r.end[o]), e.strides[t10] = r.strides[o], r.beginMask & 1 << o && (e.beginMask |= 1 << t10), r.endMask & 1 << o && (e.endMask |= 1 << t10), r.shrinkAxisMask & 1 << o ? (e.finalShapeGatherIndices.push(Rj), e.finalShapeGatherIndicesSparse.push(-1), e.shrinkAxisMask |= 1 << t10) : (e.finalShapeGatherIndices.push(t10), e.finalShapeGatherIndicesSparse.push(o)), e.inputShapeGatherIndicesSparse[t10] = o, t10++;
}
}
function BN(r, e, t10, o, n, s) {
if (n[e])
return t10 > 0 ? s[e] : s[e + 1 & 1];
{
let a = r < 0 ? o + r : r;
return a < s[0] ? s[0] : a > s[1] ? s[1] : a;
}
}
var Vj = "4.5.0";
var Dl = class {
static sgd(e) {
return new ii(e);
}
static momentum(e, t10, o = false) {
return new ep(e, t10, o);
}
static rmsprop(e, t10 = 0.9, o = 0, n = null, s = false) {
return new tp(e, t10, o, n, s);
}
static adam(e = 1e-3, t10 = 0.9, o = 0.999, n = null) {
return new Zu(e, t10, o, n);
}
static adadelta(e = 1e-3, t10 = 0.95, o = null) {
return new Yu(e, t10, o);
}
static adamax(e = 2e-3, t10 = 0.9, o = 0.999, n = null, s = 0) {
return new Ju(e, t10, o, n, s);
}
static adagrad(e, t10 = 0.1) {
return new Qu(e, t10);
}
};
var CUe = Dl;
var Wj = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (r) => r())();
function Kw() {
return new Promise((r) => Wj(() => r()));
}
var C = {};
He(C, { ERF_A1: () => sX, ERF_A2: () => aX, ERF_A3: () => iX, ERF_A4: () => uX, ERF_A5: () => pX, ERF_P: () => nX, PARALLELIZE_THRESHOLD: () => nf, RowPartitionType: () => Ta, SELU_SCALE: () => oX, SELU_SCALEALPHA: () => rX, applyActivation: () => ju, assertAndGetBroadcastShape: () => rt, assertAxesAreInnerMostDims: () => yH, assertParamsConsistent: () => Uj, assignToTypedArray: () => hX, axesAreInnerMostDims: () => Iw, calculateShapes: () => v1, checkEinsumDimSizes: () => wX, checkPadOnDimRoundingMode: () => Lt, combineLocations: () => e2, combineRaggedTensorToTensorShapes: () => Hj, complexWithEvenIndex: () => mX, complexWithOddIndex: () => dX, computeConv2DInfo: () => Mu, computeConv3DInfo: () => bk, computeDefaultPad: () => Sw, computeDilation2DInfo: () => g4, computeOptimalWindowSize: () => Xj, computeOutAndReduceShapes: () => xH, computeOutShape: () => Gj, computePool2DInfo: () => ww, computePool3DInfo: () => x4, convertConv2DDataFormat: () => Ck, decodeEinsumEquation: () => bX, eitherStridesOrDilationsAreOne: () => gr, expandShapeToKeepDim: () => ti, exponent: () => xX, exponents: () => gX, fromStringArrayToUint8: () => WX, fromUint8ToStringArray: () => VX, getAxesPermutation: () => bH, getBroadcastDims: () => jk, getComplexWithIndex: () => fX, getEinsumComputePath: () => SX, getEinsumPermutation: () => CX, getFusedBiasGradient: () => qu, getFusedDyActivation: () => Ku, getImageCenter: () => Yj, getInnerMostAxes: () => wH, getPermuted: () => Zj, getRaggedRank: () => qj, getReductionAxes: () => fd, getReshaped: () => Qj, getReshapedPermuted: () => Jj, getRowPartitionTypesHelper: () => Kj, getSliceBeginCoords: () => eX, getSliceSize: () => tX, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => NX, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => TX, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => _X, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => RX, getSparseReshapeInputOutputMismatchErrorMessage: () => AX, getSparseReshapeInputOutputMultipleErrorMessage: () => DX, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => $X, getSparseReshapeNegativeOutputDimErrorMessage: () => EX, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => MX, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => FX, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => PX, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => OX, getUndoAxesPermutation: () => CH, isIdentityPermutation: () => IX, log: () => mG, mergeRealAndImagArrays: () => cX, prepareAndValidate: () => LN, prepareSplitSize: () => kX, segment_util: () => jw, shouldFuse: () => Xu, slice_util: () => ct, splitRealAndImagArrays: () => lX, stridesOrDilationsArePositive: () => ba, tupleValuesAreOne: () => Ou, upcastType: () => dt, validateDefaultValueShape: () => jj, validateInput: () => mc, validateUpdateShape: () => Fw, warn: () => ha });
function Uj(r, e) {
let t10 = r[0].length;
r.forEach((n, s) => {
$(n.length === t10, () => `Error in concat${t10}D: rank of tensors[${s}] must be the same as the rank of the rest (${t10})`);
}), $(e >= 0 && e < t10, () => `Error in concat${t10}D: axis must be between 0 and ${t10 - 1}.`);
let o = r[0];
r.forEach((n, s) => {
for (let a = 0; a < t10; a++)
$(a === e || n[a] === o[a], () => `Error in concat${t10}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`);
});
}
function Gj(r, e) {
let t10 = r[0].slice();
for (let o = 1; o < r.length; o++)
t10[e] += r[o][e];
return t10;
}
var Ta;
(function(r) {
r[r.FIRST_DIM_SIZE = 0] = "FIRST_DIM_SIZE", r[r.VALUE_ROWIDS = 1] = "VALUE_ROWIDS", r[r.ROW_LENGTHS = 2] = "ROW_LENGTHS", r[r.ROW_SPLITS = 3] = "ROW_SPLITS", r[r.ROW_LIMITS = 4] = "ROW_LIMITS", r[r.ROW_STARTS = 5] = "ROW_STARTS";
})(Ta || (Ta = {}));
function Hj(r, e, t10) {
let o = new Array();
if (t10 == null && e == null)
return o;
if (e == null)
for (; o.length < r + t10.length; )
o.push(-1);
else
o = e.slice();
if (t10 == null)
return o;
if (r + t10.length !== o.length)
throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.rank = ${r + t10.length}, but shape.rank = ${o.length}`);
for (let n = 1; n < t10.length; ++n) {
let s = t10[n], a = o[o.length - t10.length + n], i = o[a];
if (s >= 0)
if (i >= 0) {
if (i !== s)
throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.shape[${n + r}] = ${s} but shape[${n + r}] = ${i}`);
} else
o[a] = s;
}
return o;
}
function Kj(r) {
let e = { FIRST_DIM_SIZE: Ta.FIRST_DIM_SIZE, VALUE_ROWIDS: Ta.VALUE_ROWIDS, ROW_LENGTHS: Ta.ROW_LENGTHS, ROW_SPLITS: Ta.ROW_SPLITS, ROW_LIMITS: Ta.ROW_LIMITS, ROW_STARTS: Ta.ROW_STARTS }, t10 = [];
for (let o of r)
if (o in e)
t10.push(e[o]);
else
break;
return t10;
}
function qj(r) {
return r.length === 0 ? 0 : r[0] === Ta.FIRST_DIM_SIZE ? r.length - 1 : r.length;
}
function jj(r, e) {
if (r == null || e == null)
return;
let t10 = r.length, o = e.length;
if (t10 >= o)
throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t10} must be less than ragged tensor input flatValues.rank = ${o})`);
for (let n = 0; n < Math.min(t10, o - 1); ++n) {
let s = r[n], a = e[n + 1];
if (s >= 0 && a >= 0 && s !== 1 && s !== a)
throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${e} are incompatible: defaultValue.shape[${n - r.length}] = ${s} but ragged tensor input.flatValues.shape[${n - r.length}] = ${a}`);
}
}
var nf = 30;
function Xj(r) {
return r <= nf ? r : Wp(r, Math.floor(Math.sqrt(r)));
}
function Yj(r, e, t10) {
let o = t10 * (typeof r == "number" ? r : r[0]), n = e * (typeof r == "number" ? r : r[1]);
return [o, n];
}
function Qj(r, e, t10, o = true) {
let n = [];
if (o)
n = n.concat(e.slice(0)), n.push(r[0] / t10), n = n.concat(r.slice(1));
else {
n = n.concat(r[0]);
let s = e.length;
for (let a = 0; a < s; ++a)
n = n.concat([r[a + 1] / e[a], e[a]]);
n = n.concat(r.slice(s + 1));
}
return n;
}
function Zj(r, e, t10 = true) {
let o = [];
if (t10) {
o.push(e);
for (let n = e + 1; n < r; ++n)
n <= 2 * e ? (o.push(n), o.push(n - (e + 1))) : o.push(n);
} else {
let n = [], s = [];
for (let a = 1; a < r; ++a)
a >= e * 2 + 1 || a % 2 === 1 ? s.push(a) : n.push(a);
o.push(...n), o.push(0), o.push(...s);
}
return o;
}
function Jj(r, e, t10, o = true) {
let n = [];
o ? n.push(r[0] / t10) : n.push(r[0] * t10);
for (let s = 1; s < r.length; ++s)
s <= e.length ? o ? n.push(e[s - 1] * r[s]) : n.push(r[s] / e[s - 1]) : n.push(r[s]);
return n;
}
function eX(r, e) {
let t10 = [0];
for (let o = 0; o < e; ++o)
t10.push(r[o][0]);
return t10;
}
function tX(r, e, t10) {
let o = r.slice(0, 1);
for (let n = 0; n < t10; ++n)
o.push(r[n + 1] - e[n][0] - e[n][1]);
return o;
}
var rX = 1.7580993408473768;
var oX = 1.0507009873554805;
var nX = 0.3275911;
var sX = 0.254829592;
var aX = -0.284496736;
var iX = 1.421413741;
var uX = -1.453152027;
var pX = 1.061405429;
function cX(r, e) {
if (r.length !== e.length)
throw new Error(`Cannot merge real and imag arrays of different lengths. real:${r.length}, imag: ${e.length}.`);
let t10 = new Float32Array(r.length * 2);
for (let o = 0; o < t10.length; o += 2)
t10[o] = r[o / 2], t10[o + 1] = e[o / 2];
return t10;
}
function lX(r) {
let e = new Float32Array(r.length / 2), t10 = new Float32Array(r.length / 2);
for (let o = 0; o < r.length; o += 2)
e[o / 2] = r[o], t10[o / 2] = r[o + 1];
return { real: e, imag: t10 };
}
function mX(r) {
let e = Math.ceil(r.length / 4), t10 = new Float32Array(e), o = new Float32Array(e);
for (let n = 0; n < r.length; n += 4)
t10[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1];
return { real: t10, imag: o };
}
function dX(r) {
let e = Math.floor(r.length / 4), t10 = new Float32Array(e), o = new Float32Array(e);
for (let n = 2; n < r.length; n += 4)
t10[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1];
return { real: t10, imag: o };
}
function fX(r, e) {
let t10 = r[e * 2], o = r[e * 2 + 1];
return { real: t10, imag: o };
}
function hX(r, e, t10, o) {
r[o * 2] = e, r[o * 2 + 1] = t10;
}
function gX(r, e) {
let t10 = new Float32Array(r / 2), o = new Float32Array(r / 2);
for (let n = 0; n < Math.ceil(r / 2); n++) {
let s = (e ? 2 : -2) * Math.PI * (n / r);
t10[n] = Math.cos(s), o[n] = Math.sin(s);
}
return { real: t10, imag: o };
}
function xX(r, e, t10) {
let o = (t10 ? 2 : -2) * Math.PI * (r / e), n = Math.cos(o), s = Math.sin(o);
return { real: n, imag: s };
}
var qw = "->";
var yX = /->/g;
var jN = ",";
var XN = "...";
function bX(r, e) {
r = r.replace(/\s/g, "");
let t10 = (r.length - r.replace(yX, "").length) / qw.length;
if (t10 < 1)
throw new Error("Equations without an arrow are not supported.");
if (t10 > 1)
throw new Error(`Equation must contain exactly one arrow ("${qw}").`);
let [o, n] = r.split(qw);
$(o.indexOf(XN) === -1, () => `The ellipsis notation ("${XN}") is not supported yet.`);
let s = o.split(jN), a = s.length;
if (e !== a)
throw new Error(`Expected ${a} input tensors, received ${e}`);
if (a > 2)
throw new Error("Support for more than 2 input tensors is not implemented yet.");
let i = [];
for (let m = 0; m < n.length; ++m) {
let d = n[m];
if (!s.some((f) => f.indexOf(d) !== -1))
throw new Error(`Output subscripts contain the label ${d} not present in the input subscripts.`);
i.indexOf(d) === -1 && i.push(d);
}
for (let m = 0; m < o.length; ++m) {
let d = o[m];
i.indexOf(d) === -1 && d !== jN && i.push(d);
}
let p = new Array(s.length);
for (let m = 0; m < a; ++m) {
if (new Set(s[m].split("")).size !== s[m].length)
throw new Error(`Found duplicate axes in input component ${s[m]}. Support for duplicate axes in input is not implemented yet.`);
p[m] = [];
for (let d = 0; d < s[m].length; ++d)
p[m].push(i.indexOf(s[m][d]));
}
let u = i.length, c = n.length, l = [];
for (let m = c; m < u; ++m)
l.push(m);
return { allDims: i, summedDims: l, idDims: p };
}
function CX(r, e) {
let t10 = new Array(r);
t10.fill(-1);
for (let n = 0; n < e.length; ++n)
t10[e[n]] = n;
let o = [];
for (let n = 0; n < r; ++n)
t10[n] === -1 && o.push(n);
return t10 = t10.filter((n) => n !== -1), { permutationIndices: t10, expandDims: o };
}
function wX(r, e, t10) {
let o = new Array(r);
for (let n = 0; n < t10.length; ++n) {
let s = t10[n].shape;
for (let a = 0; a < e[n].length; ++a)
o[e[n][a]] === void 0 ? o[e[n][a]] = s[a] : $(o[e[n][a]] === s[a], () => `Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`);
}
}
function SX(r, e) {
let t10 = r, o = [], n = 0;
r.length === 0 && t10.push(-1), n = r.length + 1;
for (let a = 0; a < n; ++a)
o.push([]);
let s = [];
for (let a = 0; a < t10.length; ++a) {
let i = t10[a], p = vX(e, i);
for (let u of p)
s.indexOf(u) === -1 && (o[a].push(u), s.push(u));
}
return { path: t10, steps: o };
}
function IX(r) {
return r.every((e, t10) => e === t10);
}
function vX(r, e) {
let t10 = [];
for (let o = 0; o < r.length; ++o)
(r[o].length === 0 || r[o].indexOf(e) !== -1 || e === -1) && t10.push(o);
return t10;
}
function kX(r, e, t10 = 0) {
let o = [];
if (typeof e == "number")
$(r.shape[t10] % e === 0, () => "Number of splits must evenly divide the axis."), o = new Array(e).fill(r.shape[t10] / e);
else {
let n = e.reduce((a, i) => (i === -1 && (a += 1), a), 0);
$(n <= 1, () => "There should be only one negative value in split array.");
let s = e.indexOf(-1);
if (s !== -1) {
let a = e.reduce((i, p) => p > 0 ? i + p : i);
e[s] = r.shape[t10] - a;
}
$(r.shape[t10] === e.reduce((a, i) => a + i), () => "The sum of sizes must match the size of the axis dimension."), o = e;
}
return o;
}
function NX(r) {
return `Received SparseTensor with denseShape[0] = 0 but
indices.shape[0] = ${r}`;
}
function TX(r, e) {
return `indices(${r}, 0) is invalid: ${e} < 0`;
}
function _X(r, e, t10) {
return `indices(${r}, 0) is invalid: ${e} >= ${t10}`;
}
function $X(r, e) {
return `only one output dimension may be -1, not both ${r} and ${e}`;
}
function EX(r, e) {
return `size ${r} must be non-negative, not ${e}`;
}
function RX() {
return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
}
function DX(r, e) {
let t10 = Ue(r), o = Ue(e);
return `Input to reshape is a SparseTensor with ${t10}
dense values, but the requested shape requires a multiple of ${o}. inputShape=${r} outputShape= ${e}`;
}
function AX(r, e) {
let t10 = Ue(r), o = Ue(e);
return `Input to reshape is a tensor with ${t10} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`;
}
function FX() {
return "segment ids must be >= 0";
}
function PX() {
return "segment ids are not increasing";
}
function OX(r, e) {
return `Segment id ${r} out of range [0, ${e}), possibly because segmentIds input is not sorted.`;
}
function MX(r, e, t10) {
return `Bad: indices[${r}] == ${e} out of range [0, ${t10})`;
}
var jw = {};
He(jw, { collectGatherOpShapeInfo: () => zX, computeOutShape: () => BX, segOpComputeOptimalWindowSize: () => LX });
function LX(r, e) {
let t10 = false, o;
for (r <= nf ? (o = r, t10 = true) : o = Wp(r, Math.floor(Math.sqrt(r))); !t10; )
o > e || o === r ? t10 = true : o = Wp(r, o + 1);
return o;
}
function BX(r, e, t10) {
let o = [], n = r.length;
for (let s = 0; s < n; s++)
s !== e ? o.push(r[s]) : o.push(t10);
return o;
}
function zX(r, e, t10, o) {
let n = e.shape.length, s = r.shape.length;
if (o !== 0 && (o < -n || o > n))
throw new Error(`Expect batchDims in the range of [-${n}, ${n}], but got ${o}`);
if (o < 0 && (o += n), o > s)
throw new Error(`batchDims (${o}) must be less than rank(x) (
${s}).`);
if (t10 < o)
throw new Error(`batchDims (${o}) must be less than or equal to axis (${t10}).`);
for (let l = 0; l < o; ++l)
if (r.shape[l] !== e.shape[l])
throw new Error(`x.shape[${l}]: ${r.shape[l]} should be equal to indices.shape[${l}]: ${e.shape[l]}.`);
let a = r.shape[t10], i = [], p = 1, u = 1, c = 1;
for (let l = 0; l < o; ++l)
i.push(r.shape[l]), p *= r.shape[l];
for (let l = o; l < t10; l++)
i.push(r.shape[l]), u *= r.shape[l];
for (let l = o; l < n; l++)
i.push(e.shape[l]);
for (let l = t10 + 1; l < s; l++)
i.push(r.shape[l]), c *= r.shape[l];
return { batchSize: p, sliceSize: c, outerSize: u, dimSize: a, outputShape: i };
}
function VX(r) {
try {
return r.map((e) => Jp(e));
} catch (e) {
throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`);
}
}
function WX(r) {
return r.map((e) => Yi(e));
}
var Wt = {};
He(Wt, { nonMaxSuppressionV3Impl: () => Yd, nonMaxSuppressionV4Impl: () => Qd, nonMaxSuppressionV5Impl: () => Zd, whereImpl: () => Kd });
kN();
var UX = P();
UX.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (r) => {
r && console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.");
});
var co;
(function(r) {
r[r.DT_INVALID = 0] = "DT_INVALID", r[r.DT_FLOAT = 1] = "DT_FLOAT", r[r.DT_DOUBLE = 2] = "DT_DOUBLE", r[r.DT_INT32 = 3] = "DT_INT32", r[r.DT_UINT8 = 4] = "DT_UINT8", r[r.DT_INT16 = 5] = "DT_INT16", r[r.DT_INT8 = 6] = "DT_INT8", r[r.DT_STRING = 7] = "DT_STRING", r[r.DT_COMPLEX64 = 8] = "DT_COMPLEX64", r[r.DT_INT64 = 9] = "DT_INT64", r[r.DT_BOOL = 10] = "DT_BOOL", r[r.DT_QINT8 = 11] = "DT_QINT8", r[r.DT_QUINT8 = 12] = "DT_QUINT8", r[r.DT_QINT32 = 13] = "DT_QINT32", r[r.DT_BFLOAT16 = 14] = "DT_BFLOAT16", r[r.DT_QINT16 = 15] = "DT_QINT16", r[r.DT_QUINT16 = 16] = "DT_QUINT16", r[r.DT_UINT16 = 17] = "DT_UINT16", r[r.DT_COMPLEX128 = 18] = "DT_COMPLEX128", r[r.DT_HALF = 19] = "DT_HALF", r[r.DT_RESOURCE = 20] = "DT_RESOURCE", r[r.DT_VARIANT = 21] = "DT_VARIANT", r[r.DT_UINT32 = 22] = "DT_UINT32", r[r.DT_UINT64 = 23] = "DT_UINT64", r[r.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", r[r.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", r[r.DT_INT32_REF = 103] = "DT_INT32_REF", r[r.DT_UINT8_REF = 104] = "DT_UINT8_REF", r[r.DT_INT16_REF = 105] = "DT_INT16_REF", r[r.DT_INT8_REF = 106] = "DT_INT8_REF", r[r.DT_STRING_REF = 107] = "DT_STRING_REF", r[r.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", r[r.DT_INT64_REF = 109] = "DT_INT64_REF", r[r.DT_BOOL_REF = 110] = "DT_BOOL_REF", r[r.DT_QINT8_REF = 111] = "DT_QINT8_REF", r[r.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", r[r.DT_QINT32_REF = 113] = "DT_QINT32_REF", r[r.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", r[r.DT_QINT16_REF = 115] = "DT_QINT16_REF", r[r.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", r[r.DT_UINT16_REF = 117] = "DT_UINT16_REF", r[r.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", r[r.DT_HALF_REF = 119] = "DT_HALF_REF", r[r.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", r[r.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", r[r.DT_UINT32_REF = 122] = "DT_UINT32_REF", r[r.DT_UINT64_REF = 123] = "DT_UINT64_REF";
})(co || (co = {}));
var YN;
(function(r) {
let e;
(function(t10) {
t10[t10.LEGACY = 0] = "LEGACY", t10[t10.V1 = 1] = "V1", t10[t10.V2 = 2] = "V2";
})(e = r.CheckpointFormatVersion || (r.CheckpointFormatVersion = {}));
})(YN || (YN = {}));
var Yw = {};
function HX(r, e) {
let t10 = { tfOpName: r, category: "custom", inputs: [], attrs: [], customExecutor: e };
Yw[r] = t10;
}
function sf(r) {
return Yw[r];
}
function KX(r) {
delete Yw[r];
}
function I(r, e, t10, o, n) {
let s = e.inputParams[r];
if (s && s.inputIndexStart !== void 0) {
let i = s.inputIndexStart, p = s.inputIndexEnd === 0 ? void 0 : s.inputIndexEnd === void 0 ? i + 1 : s.inputIndexEnd, u = i < 0 ? e.inputNames.length + i : i;
if (s.type === "tensor")
return Bt(e.inputNames[u], t10, o, n);
if (s.type === "tensors") {
let m = e.inputs.slice(i, p);
return e.inputNames.slice(i, p).filter((f, h) => {
var g;
return ((g = m[h]) === null || g === void 0 ? void 0 : g.op) !== "NoOp";
}).map((f) => Bt(f, t10, o, n));
}
let c = Bt(e.inputNames[u], t10, o, n), l = c.dataSync();
return s.type === "number" ? l[0] : y.toNestedArray(c.shape, l);
}
let a = e.attrParams[r];
return a && a.value;
}
function Bt(r, e, t10, o) {
let [n, s] = Nr(r, t10);
if (o != null) {
let i = o.getHashTableHandleByName(n);
if (i != null)
return i;
}
let a = t10.currentContextIds.find((i) => !!e[af(n, i)]);
return a !== void 0 ? e[af(n, a)][s] : void 0;
}
function Qw(r, e, t10) {
return e[af(r, t10.currentContextId)];
}
function Ds(r, e) {
let [t10, o, n] = Nr(r, e);
return [af(t10, e && e.currentContextId), o, n];
}
function af(r, e) {
return e ? `${r}-${e}` : r;
}
function Nr(r, e) {
if (r === "")
return ["", 0, void 0];
let t10 = e != null && e.parseNodeNameCache != null;
if (t10) {
let s = e.parseNodeNameCache.get(r);
if (s != null)
return s;
}
let o = r.split(":"), n;
if (o.length === 1)
n = [r, 0, void 0];
else {
let s = o[0], a = o.length === 3 ? o[1] : void 0, i = Number(o[o.length - 1]);
n = [s, i, a];
}
return t10 && e.parseNodeNameCache.set(r, n), n;
}
function Al(r, e, t10) {
let o = I("pad", r, e, t10);
if (o === "explicit") {
o = I("explicitPaddings", r, e, t10);
let n = [[0, 0], [0, 0], [0, 0], [0, 0]];
for (let s = 0; s < 4; s++)
n[s][0] = o[s * 2], n[s][1] = o[s * 2 + 1];
return n;
}
return o;
}
function As(r) {
return r.kept ? r : Vr(r);
}
var Zw = {};
He(Zw, { json: () => qX });
var qX = [{ tfOpName: "Add", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddV2", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddN", category: "arithmetic", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "BiasAdd", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "Sub", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "RealDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Div", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "DivNoNan", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mul", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Maximum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Minimum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Pow", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SquaredDifference", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorMod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
var Jw = {};
He(Jw, { json: () => jX });
var jX = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan2", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ClipByValue", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "clipValueMin", type: "number" }, { start: 2, name: "clipValueMax", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Complex", category: "basic_math", inputs: [{ start: 0, name: "real", type: "tensor" }, { start: 1, name: "imag", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Prelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "alpha", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log1p", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Reciprocal", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Softplus", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsFinite", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsInf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
var eS = {};
He(eS, { json: () => XX });
var XX = [{ tfOpName: "EmptyTensorList", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "maxNumElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "LoopCond", category: "control", inputs: [{ start: 0, name: "pred", type: "tensor" }] }, { tfOpName: "Switch", category: "control", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "pred", type: "tensor" }] }, { tfOpName: "Merge", category: "control", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "Enter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "frame_name", name: "frameName", type: "string" }, { tfName: "is_constant", name: "isConstant", type: "bool" }] }, { tfOpName: "Exit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayV3", category: "control", inputs: [{ start: 0, name: "size", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "dynamic_size", name: "dynamicSize", type: "bool" }, { tfName: "clear_after_read", name: "clearAfterRead", type: "bool" }, { tfName: "identical_element_shapes", name: "identicalElementShapes", type: "bool" }, { tfName: "tensor_array_name", name: "name", type: "string" }] }, { tfOpName: "TensorArrayWriteV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayReadV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayGatherV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }] }, { tfOpName: "TensorArrayScatterV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArrayConcatV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape_except0", name: "elementShapeExcept0", type: "shape", notSupported: true }] }, { tfOpName: "TensorArraySplitV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }, { start: 2, name: "lengths", type: "number[]" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArraySizeV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }] }, { tfOpName: "TensorArrayCloseV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }] }, { tfOpName: "StatelessIf", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "If", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "StatelessWhile", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "While", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "TensorListScatter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListScatterV2", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }, { start: 3, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGather", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListSetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListReserve", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListFromTensor", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListStack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }, { tfName: "num_elements", name: "numElements", type: "dtype" }] }, { tfOpName: "TensorListSplit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }, { start: 2, name: "lengths", type: "number[]" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcat", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcatV2", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPopBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPushBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListLength", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }] }, { tfOpName: "TensorListResize", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "size", type: "number" }] }];
var tS = {};
He(tS, { json: () => YX });
var YX = [{ tfOpName: "AvgPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [], notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPoolWithArgmax", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "include_batch_in_index", name: "includeBatchInIndex", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AvgPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Conv1D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "stride", name: "stride", type: "number" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NWC" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "dilation", name: "dilation", type: "number", defaultValue: 1 }] }, { tfOpName: "Conv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "useCudnnOnGpu", name: "useCudnnOnGpu", type: "bool" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "_FusedConv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "use_cudnn_on_gpu", name: "useCudnnOnGpu", type: "bool", defaultValue: true }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }] }, { tfOpName: "Conv2DBackpropInput", category: "convolution", inputs: [{ start: 2, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 0, name: "outputShape", type: "number[]" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]", notSupported: true }] }, { tfOpName: "DepthwiseConv2d", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "DepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "FusedDepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }] }, { tfOpName: "Conv3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "Dilation2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "rates", name: "dilations", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }] }];
var rS = {};
He(rS, { json: () => QX });
var QX = [{ tfOpName: "Fill", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }, { start: 1, name: "value", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "LinSpace", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "num", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "OneHot", category: "creation", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "depth", type: "number" }, { start: 2, name: "onValue", type: "number", defaultValue: 1 }, { start: 3, name: "offValue", type: "number", defaultValue: 0 }], attrs: [{ tfName: "axis", name: "axis", type: "number", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "Ones", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "OnesLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "RandomStandardNormal", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "RandomUniform", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "minval", name: "minval", type: "number", defaultValue: 0 }, { tfName: "maxval", name: "maxval", type: "number", defaultValue: 1 }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "RandomUniformInt", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "minval", name: "minval", type: "number" }, { tfName: "maxval", name: "maxval", type: "number" }, { tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }] }, { tfOpName: "Range", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "step", type: "number", defaultValue: 0 }], attrs: [{ tfName: "Tidx", name: "dtype", type: "dtype" }] }, { tfOpName: "TruncatedNormal", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "means", name: "mean", type: "number", defaultValue: 0 }, { tfName: "stddev", name: "stdDev", type: "number", defaultValue: 1 }, { tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Zeros", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "ZerosLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "Multinomial", category: "creation", inputs: [{ start: 0, name: "logits", type: "tensor" }, { start: 1, name: "numSamples", type: "number" }], attrs: [{ tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number" }, { tfName: "T", name: "dtype", type: "dtype" }, { tfName: "output_dtype", name: "output_dtype", type: "dtype" }] }];
var oS = {};
He(oS, { json: () => ZX });
var ZX = [{ tfOpName: "NonMaxSuppressionV2", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV3", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV4", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "T_threshold", name: "threshold", type: "dtype", notSupported: true }, { tfName: "pad_to_max_output_size", name: "padToMaxOutputSize", type: "bool" }] }, { tfOpName: "NonMaxSuppressionV5", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }, { start: 5, name: "softNmsSigma", type: "number" }] }, { tfOpName: "Where", category: "dynamic", inputs: [{ start: 0, name: "condition", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ListDiff", category: "dynamic", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
var nS = {};
He(nS, { json: () => JX });
var JX = [{ tfOpName: "LowerBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "TopKV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "k", type: "number" }], attrs: [{ tfName: "sorted", name: "sorted", type: "bool" }] }, { tfOpName: "UpperBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { tfOpName: "Unique", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "UniqueV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }];
var sS = {};
He(sS, { json: () => e5 });
var e5 = [{ tfOpName: "PlaceholderWithDefault", category: "graph", inputs: [{ start: 0, name: "default", type: "tensor" }], attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Placeholder", category: "graph", attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Const", category: "graph" }, { tfOpName: "Identity", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IdentityN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Snapshot", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Rank", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Size", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Shape", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "ShapeN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Print", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "data", type: "tensors" }], attrs: [{ tfName: "message", name: "message", type: "string" }, { tfName: "first_n", name: "firstN", type: "number", notSupported: true }, { tfName: "summarize", name: "summarize", type: "number", defaultValue: 3 }] }, { tfOpName: "NoOp", category: "graph", inputs: [] }, { tfOpName: "StopGradient", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "FakeQuantWithMinMaxVars", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "min", name: "min", type: "number" }, { tfName: "max", name: "max", type: "number" }] }];
var aS = {};
He(aS, { json: () => t5 });
var t5 = [{ tfOpName: "HashTable", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "HashTableV2", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "LookupTableImport", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableImportV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFind", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFindV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableSize", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "LookupTableSizeV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "InitializeTable", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }] }, { tfOpName: "InitializeTableV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }] }];
var iS = {};
He(iS, { json: () => r5 });
var r5 = [{ tfOpName: "ResizeBilinear", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ResizeNearestNeighbor", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "CropAndResize", category: "image", inputs: [{ start: 0, name: "image", type: "tensor" }, { start: 1, name: "boxes", type: "tensor" }, { start: 2, name: "boxInd", type: "tensor" }, { start: 3, name: "cropSize", type: "number[]" }], attrs: [{ tfName: "method", name: "method", type: "string" }, { tfName: "extrapolation_value", name: "extrapolationValue", type: "number" }] }, { tfOpName: "ImageProjectiveTransformV3", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "transforms", type: "tensor" }, { start: 2, name: "outputShape", type: "number[]" }, { start: 3, name: "fillValue", type: "number" }], attrs: [{ tfName: "interpolation", name: "interpolation", type: "string" }, { tfName: "fill_mode", name: "fillMode", type: "string" }] }];
var uS = {};
He(uS, { json: () => o5 });
var o5 = [{ tfOpName: "Equal", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NotEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Greater", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "GreaterEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Less", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LessEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalAnd", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalNot", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalOr", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Select", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SelectV2", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BitwiseAnd", category: "logical", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }] }];
var pS = {};
He(pS, { json: () => n5 });
var n5 = [{ tfOpName: "_FusedMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMulV2", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Transpose", category: "matrices", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "perm", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Einsum", category: "matrices", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "equation", name: "equation", type: "string" }, { tfName: "N", name: "n", type: "number", defaultValue: 2 }, { tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "MatrixBandPart", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "numLower", type: "tensor" }, { start: 1, name: "numUpper", type: "tensor" }] }];
var cS = {};
He(cS, { json: () => s5 });
var s5 = [{ tfOpName: "EuclideanNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", defaultValue: false }] }, { tfOpName: "FusedBatchNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV2", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV3", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "LRN", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "depth_radius", name: "radius", type: "number", defaultValue: 5 }, { tfName: "bias", name: "bias", type: "number", defaultValue: 1 }, { tfName: "alpha", name: "alpha", type: "number", defaultValue: 1 }, { tfName: "beta", name: "beta", type: "number", defaultValue: 0.5 }] }, { tfOpName: "Softmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "LogSoftmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }];
var lS = {};
He(lS, { json: () => a5 });
var a5 = [{ tfOpName: "Bincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }] }, { tfOpName: "DenseBincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }], attrs: [{ tfName: "binary_output", name: "binaryOutput", type: "bool" }] }, { tfOpName: "Max", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Mean", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Min", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Sum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "All", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Any", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "ArgMax", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "ArgMin", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "Prod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cumprod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }, { tfOpName: "Cumsum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }];
var mS = {};
He(mS, { json: () => i5 });
var i5 = [{ tfOpName: "ConcatV2", category: "slice_join", inputs: [{ start: 0, end: -1, name: "tensors", type: "tensors" }, { start: -1, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "Concat", category: "slice_join", inputs: [{ start: 1, end: 0, name: "tensors", type: "tensors" }, { start: 0, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "GatherV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }], attrs: [{ tfName: "batch_dims", name: "batchDims", type: "number", defaultValue: 0 }] }, { tfOpName: "Gather", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", notSupported: true }] }, { tfOpName: "Reverse", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "dims", type: "bool[]" }] }, { tfOpName: "ReverseV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }] }, { tfOpName: "Slice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "size", type: "number[]" }] }, { tfOpName: "StridedSlice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "end", type: "number[]" }, { start: 3, name: "strides", type: "number[]" }], attrs: [{ tfName: "begin_mask", name: "beginMask", type: "number", defaultValue: 0 }, { tfName: "end_mask", name: "endMask", type: "number", defaultValue: 0 }, { tfName: "new_axis_mask", name: "newAxisMask", type: "number", defaultValue: 0 }, { tfName: "ellipsis_mask", name: "ellipsisMask", type: "number", defaultValue: 0 }, { tfName: "shrink_axis_mask", name: "shrinkAxisMask", type: "number", defaultValue: 0 }] }, { tfOpName: "Pack", category: "slice_join", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "Unpack", category: "slice_join", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }, { tfName: "num", name: "num", type: "number", defaultValue: 0, notSupported: true }] }, { tfOpName: "Tile", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "reps", type: "number[]" }] }, { tfOpName: "Split", category: "slice_join", inputs: [{ start: 0, name: "axis", type: "number", defaultValue: 0 }, { start: 1, name: "x", type: "tensor" }], attrs: [{ tfName: "num_split", name: "numOrSizeSplits", type: "number", defaultValue: 1 }] }, { tfOpName: "SplitV", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "numOrSizeSplits", type: "number[]" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "ScatterNd", category: "slice_join", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "shape", type: "number[]" }] }, { tfOpName: "GatherNd", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "slice_join", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: false, notSupported: true }] }, { tfOpName: "TensorScatterUpdate", category: "slice_join", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "values", type: "tensor" }] }];
var dS = {};
He(dS, { json: () => u5 });
var u5 = [{ tfOpName: "SparseFillEmptyRows", category: "sparse", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "denseShape", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }] }, { tfOpName: "SparseReshape", category: "sparse", inputs: [{ start: 0, name: "inputIndices", type: "tensor" }, { start: 1, name: "inputShape", type: "tensor" }, { start: 2, name: "newShape", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SparseSegmentMean", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }, { tfOpName: "SparseSegmentSum", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }];
var fS = {};
He(fS, { json: () => p5 });
var p5 = [{ tfOpName: "FFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "RFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }, { tfOpName: "IRFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }];
var hS = {};
He(hS, { json: () => c5 });
var c5 = [{ tfOpName: "StaticRegexReplace", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "pattern", name: "pattern", type: "string" }, { tfName: "rewrite", name: "rewrite", type: "string" }, { tfName: "replace_global", name: "replaceGlobal", type: "bool" }] }, { tfOpName: "StringNGrams", category: "string", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "dataSplits", type: "tensor" }], attrs: [{ tfName: "separator", name: "separator", type: "string" }, { tfName: "ngram_widths", name: "nGramWidths", type: "number[]" }, { tfName: "left_pad", name: "leftPad", type: "string" }, { tfName: "right_pad", name: "rightPad", type: "string" }, { tfName: "pad_width", name: "padWidth", type: "number" }, { tfName: "preserve_short_sequences", name: "preserveShortSequences", type: "bool" }], outputs: ["ngrams", "ngrams_splits"] }, { tfOpName: "StringSplit", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "delimiter", type: "tensor" }], attrs: [{ tfName: "skip_empty", name: "skipEmpty", type: "bool" }], outputs: ["indices", "values", "shape"] }, { tfOpName: "StringToHashBucketFast", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "num_buckets", name: "numBuckets", type: "number" }] }];
var gS = {};
He(gS, { json: () => l5 });
var l5 = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { tfName: "DstT", name: "dtype", type: "dtype" }] }, { tfOpName: "ExpandDims", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "MirrorPad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "mode", name: "mode", type: "string" }] }, { tfOpName: "Pad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "constant_value", name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "PadV2", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }, { start: 2, name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "Reshape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "EnsureShape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "Squeeze", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "axis", tfDeprecatedName: "squeeze_dims", name: "axis", type: "number[]" }] }, { tfOpName: "SpaceToBatchND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "paddings", type: "number[]" }] }, { tfOpName: "BatchToSpaceND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "crops", type: "number[]" }] }, { tfOpName: "DepthToSpace", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "block_size", name: "blockSize", type: "number" }, { tfName: "data_format", name: "dataFormat", type: "string" }] }, { tfOpName: "BroadcastTo", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }], attrs: [] }, { tfOpName: "BroadcastArgs", category: "transformation", inputs: [{ start: 0, name: "s0", type: "tensor" }, { start: 1, name: "s1", type: "tensor" }], attrs: [] }];
var Fl = class {
static get Instance() {
return this._instance || (this._instance = new this());
}
constructor() {
let e = [Zw, Jw, eS, tS, rS, oS, nS, sS, aS, iS, uS, pS, cS, lS, mS, dS, fS, hS, gS], t10 = [].concat(...e.map((o) => o.json));
this.opMappers = t10.reduce((o, n) => (o[n.tfOpName] = n, o), {});
}
transformGraph(e, t10 = {}) {
let o = e.node, n = [], s = [], a = [], i = o.reduce((h, g) => (h[g.name] = this.mapNode(g), g.op.startsWith("Placeholder") ? n.push(h[g.name]) : g.op === "Const" ? s.push(h[g.name]) : (g.input == null || g.input.length === 0) && a.push(h[g.name]), h), {}), p = [], u = [], c = {}, l = {};
t10 != null && (c = this.mapSignatureEntries(t10.inputs), l = this.mapSignatureEntries(t10.outputs));
let m = Object.keys(i);
m.forEach((h) => {
let g = i[h];
g.inputNames.forEach((x, b) => {
let [w, , S] = Ds(x), k = i[w];
if (k.outputs != null) {
let _ = k.outputs.indexOf(S);
if (_ !== -1) {
let E = `${w}:${_}`;
g.inputNames[b] = E;
}
}
g.inputs.push(k), k.children.push(g);
});
}), Object.keys(l).length === 0 ? m.forEach((h) => {
let g = i[h];
g.children.length === 0 && u.push(g);
}) : Object.keys(l).forEach((h) => {
let [g] = Ds(h), x = i[g];
x != null && (x.signatureKey = l[h], u.push(x));
}), Object.keys(c).length > 0 ? Object.keys(c).forEach((h) => {
let [g] = Ds(h), x = i[g];
x && (x.signatureKey = c[h], p.push(x));
}) : p = n;
let d = {};
e.library != null && e.library.function != null && (d = e.library.function.reduce((h, g) => (h[g.signature.name] = this.mapFunction(g), h), {}));
let f = { nodes: i, inputs: p, outputs: u, weights: s, placeholders: n, signature: t10, functions: d };
return a.length > 0 && (f.initNodes = a), f;
}
mapSignatureEntries(e) {
return Object.keys(e || {}).reduce((t10, o) => (t10[e[o].name] = o, t10), {});
}
mapNode(e) {
let t10 = sf(e.op) || this.opMappers[e.op] || {};
e.attr == null && (e.attr = {});
let o = { name: e.name, op: e.op, category: t10.category, inputNames: (e.input || []).map((n) => n.startsWith("^") ? n.slice(1) : n), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t10.outputs };
return t10.inputs != null && (o.inputParams = t10.inputs.reduce((n, s) => (n[s.name] = { type: s.type, inputIndexStart: s.start, inputIndexEnd: s.end }, n), {})), t10.attrs != null && (o.attrParams = t10.attrs.reduce((n, s) => {
let a = s.type, i;
switch (s.type) {
case "string":
i = uf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = uf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "string[]":
i = hf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = hf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "number":
i = cf(e.attr, s.tfName, s.defaultValue || 0), i === void 0 && s.tfDeprecatedName && (i = cf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "number[]":
i = ff(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = ff(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "bool":
i = pf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = pf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "bool[]":
i = xf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = xf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "shape":
i = df(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = df(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "shape[]":
i = gf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = gf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "dtype":
i = lf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = lf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "dtype[]":
i = mf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = mf(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "func":
i = QN(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = QN(e.attr, s.tfDeprecatedName, s.defaultValue));
break;
case "tensor":
case "tensors":
break;
default:
throw new Error(`Unsupported param type: ${s.type} for op: ${e.op}`);
}
return n[s.name] = { value: i, type: a }, n;
}, {})), o;
}
mapFunction(e) {
let t10 = e.nodeDef, o = [], n = [], s = {};
t10 != null && (s = t10.reduce((l, m) => (l[m.name] = this.mapNode(m), m.op === "Const" && n.push(l[m.name]), l), {}));
let a = [], i = [];
e.signature.inputArg.forEach((l) => {
let [m] = Ds(l.name), d = { name: m, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: xS(l.type), type: "dtype" } }, children: [] };
d.signatureKey = l.name, a.push(d), s[m] = d;
}), Object.keys(s).forEach((l) => {
let m = s[l];
m.inputNames.forEach((d, f) => {
let [h, , g] = Ds(d), x = s[h];
if (x.outputs != null) {
let b = x.outputs.indexOf(g);
if (b !== -1) {
let w = `${h}:${b}`;
m.inputNames[f] = w;
}
}
m.inputs.push(x), x.children.push(m);
});
});
let u = e.ret;
e.signature.outputArg.forEach((l) => {
let [m, d] = Ds(u[l.name]), f = s[m];
f != null && (f.defaultOutput = d, i.push(f));
});
let c = this.mapArgsToSignature(e);
return { nodes: s, inputs: a, outputs: i, weights: n, placeholders: o, signature: c };
}
mapArgsToSignature(e) {
return { methodName: e.signature.name, inputs: e.signature.inputArg.reduce((t10, o) => (t10[o.name] = this.mapArgToTensorInfo(o), t10), {}), outputs: e.signature.outputArg.reduce((t10, o) => (t10[o.name] = this.mapArgToTensorInfo(o, e.ret), t10), {}) };
}
mapArgToTensorInfo(e, t10) {
let o = e.name;
return t10 != null && (o = t10[o]), { name: o, dtype: e.type };
}
};
function m5(r) {
let e = P().global;
if (typeof e.atob != "undefined")
return e.atob(r);
if (typeof Buffer != "undefined")
return new Buffer(r, "base64").toString();
throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
}
function ZN(r, e) {
let t10 = Array.isArray(r) ? String.fromCharCode.apply(null, r) : m5(r);
return e ? t10 : t10.toLowerCase();
}
function uf(r, e, t10, o = false) {
let n = r[e];
return n != null ? ZN(n.s, o) : t10;
}
function pf(r, e, t10) {
let o = r[e];
return o ? o.b : t10;
}
function cf(r, e, t10) {
let o = r[e] || {}, n = o.i != null ? o.i : o.f != null ? o.f : t10;
return typeof n == "number" ? n : parseInt(n, 10);
}
function xS(r) {
switch (typeof r == "string" && (r = co[r]), r) {
case co.DT_FLOAT:
case co.DT_HALF:
return "float32";
case co.DT_INT32:
case co.DT_INT64:
case co.DT_INT8:
case co.DT_UINT8:
return "int32";
case co.DT_BOOL:
return "bool";
case co.DT_DOUBLE:
return "float32";
case co.DT_STRING:
return "string";
default:
return null;
}
}
function QN(r, e, t10) {
let o = r[e];
return o && o.func ? o.func.name : t10;
}
function lf(r, e, t10) {
let o = r[e];
return o && o.type ? xS(o.type) : t10;
}
function mf(r, e, t10) {
let o = r[e];
return o && o.list && o.list.type ? o.list.type.map((n) => xS(n)) : t10;
}
function JN(r) {
if (!r.unknownRank)
return r.dim != null ? r.dim.map((e) => typeof e.size == "number" ? e.size : parseInt(e.size, 10)) : [];
}
function df(r, e, t10) {
let o = r[e];
return o && o.shape ? JN(o.shape) : t10;
}
function ff(r, e, t10) {
let o = r[e];
return o ? ((o.list.f && o.list.f.length ? o.list.f : o.list.i) || []).map((n) => typeof n == "number" ? n : parseInt(n, 10)) : t10;
}
function hf(r, e, t10, o = false) {
let n = r[e];
return n && n.list && n.list.s ? n.list.s.map((s) => ZN(s, o)) : t10;
}
function gf(r, e, t10) {
let o = r[e];
return o && o.list && o.list.shape ? o.list.shape.map((n) => JN(n)) : t10;
}
function xf(r, e, t10) {
let o = r[e];
return o && o.list && o.list.b ? o.list.b : t10;
}
var yf = class {
constructor(e, t10, o) {
this.node = e, this.tensorMap = t10, this.context = o, this.inputs = [], this.attrs = {}, this.inputs = e.inputNames.map((n) => this.getInput(n)), e.rawAttrs != null && (this.attrs = Object.keys(e.rawAttrs).reduce((n, s) => (n[s] = this.getAttr(s), n), {}));
}
getInput(e) {
return Bt(e, this.tensorMap, this.context);
}
getAttr(e, t10) {
let o = this.node.rawAttrs[e];
if (o.tensor != null)
return Bt(e, this.tensorMap, this.context);
if (o.i != null || o.f != null)
return cf(this.node.rawAttrs, e, t10);
if (o.s != null)
return uf(this.node.rawAttrs, e, t10);
if (o.b != null)
return pf(this.node.rawAttrs, e, t10);
if (o.shape != null)
return df(this.node.rawAttrs, e, t10);
if (o.type != null)
return lf(this.node.rawAttrs, e, t10);
if (o.list != null) {
if (o.list.i != null || o.list.f != null)
return ff(this.node.rawAttrs, e, t10);
if (o.list.s != null)
return hf(this.node.rawAttrs, e, t10);
if (o.list.shape != null)
return gf(this.node.rawAttrs, e, t10);
if (o.list.b != null)
return xf(this.node.rawAttrs, e, t10);
if (o.list.type != null)
return mf(this.node.rawAttrs, e, t10);
}
return t10;
}
};
var Ze = {};
He(Ze, { OP_SCOPE_SUFFIX: () => pw, abs: () => Zt, acos: () => ik, acosh: () => uk, add: () => be, addN: () => pk, all: () => ck, any: () => lk, argMax: () => mk, argMin: () => dk, asin: () => fk, asinh: () => hk, atan: () => gk, atan2: () => xk, atanh: () => yk, avgPool: () => cd, avgPool3d: () => wk, basicLSTMCell: () => Sk, batchNorm: () => tu, batchNorm2d: () => vk, batchNorm3d: () => kk, batchNorm4d: () => Nk, batchToSpaceND: () => ld, bincount: () => md, bitwiseAnd: () => Tk, booleanMaskAsync: () => qq, broadcastArgs: () => _k, broadcastTo: () => ru, buffer: () => me, cast: () => Ye, ceil: () => $k, clipByValue: () => Ek, clone: () => Vr, complex: () => $r, concat: () => yt, concat1d: () => Rk, concat2d: () => Dk, concat3d: () => Ak, concat4d: () => Fk, conv1d: () => Pk, conv2d: () => ou, conv2dTranspose: () => Ok, conv3d: () => Mk, conv3dTranspose: () => Bk, cos: () => zk, cosh: () => Vk, cosineWindow: () => _l, cumprod: () => Wk, cumsum: () => Uk, denseBincount: () => Gk, depthToSpace: () => Hk, depthwiseConv2d: () => ac, diag: () => Kk, dilation2d: () => qk, div: () => Ke, divNoNan: () => Xk, dot: () => Yk, dropout: () => s6, einsum: () => Qk, elu: () => gd, enclosingPowerOfTwo: () => Pw, ensureShape: () => Zk, equal: () => hd, erf: () => Jk, euclideanNorm: () => r2, exp: () => ko, expandDims: () => oi, expm1: () => o2, eye: () => xd, fft: () => pc, fill: () => Sa, floor: () => yd, floorDiv: () => pd, fused: () => Ow, gather: () => bd, gatherND: () => o6, greater: () => Bu, greaterEqual: () => Cd, ifft: () => Hu, imag: () => su, image: () => uj, inTopKAsync: () => i6, irfft: () => Wd, isFinite: () => n2, isInf: () => s2, isNaN: () => a2, leakyRelu: () => wd, less: () => kl, lessEqual: () => ic, linalg: () => pj, linspace: () => i2, localResponseNormalization: () => u2, log: () => ni, log1p: () => Sd, logSigmoid: () => p2, logSoftmax: () => c2, logSumExp: () => kd, logicalAnd: () => zu, logicalNot: () => Nd, logicalOr: () => Td, logicalXor: () => l2, losses: () => cj, lowerBound: () => m2, matMul: () => Qe, max: () => Ia, maxPool: () => $d, maxPool3d: () => d2, maxPoolWithArgmax: () => f2, maximum: () => Ed, mean: () => Vu, meshgrid: () => h2, min: () => vl, minimum: () => Wu, mirrorPad: () => g2, mod: () => x2, moments: () => y2, movingAverage: () => Yq, mul: () => se, multiRNNCell: () => b2, multinomial: () => C2, neg: () => pr, norm: () => Lu, notEqual: () => Rd, oneHot: () => Tl, ones: () => va, onesLike: () => w2, op: () => N, outerProduct: () => S2, pad: () => ka, pad1d: () => I2, pad2d: () => v2, pad3d: () => k2, pad4d: () => N2, pool: () => T2, pow: () => ri, prelu: () => Ad, print: () => ud, prod: () => _2, raggedGather: () => $2, raggedRange: () => E2, raggedTensorToTensor: () => R2, rand: () => D2, randomGamma: () => J2, randomNormal: () => Bd, randomStandardNormal: () => e1, randomUniform: () => uc, randomUniformInt: () => t1, range: () => au, real: () => si, reciprocal: () => r1, relu: () => iu, relu6: () => zd, reshape: () => W, reverse: () => uo, reverse1d: () => o1, reverse2d: () => n1, reverse3d: () => s1, reverse4d: () => a1, rfft: () => cc, round: () => Vd, rsqrt: () => i1, scalar: () => ke, scatterND: () => Zq, searchSorted: () => Nl, selu: () => u1, separableConv2d: () => p1, setdiff1dAsync: () => c1, sigmoid: () => wa, sign: () => l1, signal: () => ij, sin: () => m1, sinh: () => d1, slice: () => qe, slice1d: () => f1, slice2d: () => h1, slice3d: () => g1, slice4d: () => x1, softmax: () => y1, softplus: () => vd, spaceToBatchND: () => Dd, sparse: () => lj, sparseToDense: () => t6, spectral: () => aj, split: () => ai, sqrt: () => Rr, square: () => Jt, squaredDifference: () => Ud, squeeze: () => lc, stack: () => vr, step: () => Gd, stridedSlice: () => b1, string: () => mj, sub: () => Te, sum: () => ot, tan: () => C1, tanh: () => Il, tensor: () => ir, tensor1d: () => xr, tensor2d: () => uu, tensor3d: () => Hd, tensor4d: () => w1, tensor5d: () => S1, tensor6d: () => I1, tensorScatterUpdate: () => k1, tile: () => nu, topk: () => N1, transpose: () => dc, truncatedNormal: () => T1, unique: () => _1, unsortedSegmentSum: () => $1, unstack: () => po, upperBound: () => E1, variable: () => R1, where: () => io, whereAsync: () => qd, zeros: () => Wr, zerosLike: () => Ht });
var eT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "BiasAdd":
case "AddV2":
case "Add":
return [o.add(I("a", r, e, t10), I("b", r, e, t10))];
case "AddN":
return [o.addN(I("tensors", r, e, t10))];
case "FloorMod":
case "Mod":
return [o.mod(I("a", r, e, t10), I("b", r, e, t10))];
case "Mul":
return [o.mul(I("a", r, e, t10), I("b", r, e, t10))];
case "RealDiv":
case "Div":
return [o.div(I("a", r, e, t10), I("b", r, e, t10))];
case "DivNoNan":
return [o.divNoNan(I("a", r, e, t10), I("b", r, e, t10))];
case "FloorDiv":
return [o.floorDiv(I("a", r, e, t10), I("b", r, e, t10))];
case "Sub":
return [o.sub(I("a", r, e, t10), I("b", r, e, t10))];
case "Minimum":
return [o.minimum(I("a", r, e, t10), I("b", r, e, t10))];
case "Maximum":
return [o.maximum(I("a", r, e, t10), I("b", r, e, t10))];
case "Pow":
return [o.pow(I("a", r, e, t10), I("b", r, e, t10))];
case "SquaredDifference":
return [o.squaredDifference(I("a", r, e, t10), I("b", r, e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var tT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "Abs":
case "ComplexAbs":
return [o.abs(I("x", r, e, t10))];
case "Acos":
return [o.acos(I("x", r, e, t10))];
case "Acosh":
return [o.acosh(I("x", r, e, t10))];
case "Asin":
return [o.asin(I("x", r, e, t10))];
case "Asinh":
return [o.asinh(I("x", r, e, t10))];
case "Atan":
return [o.atan(I("x", r, e, t10))];
case "Atan2":
return [o.atan2(I("x", r, e, t10), I("y", r, e, t10))];
case "Atanh":
return [o.atanh(I("x", r, e, t10))];
case "Ceil":
return [o.ceil(I("x", r, e, t10))];
case "Complex":
return [o.complex(I("real", r, e, t10), I("imag", r, e, t10))];
case "Cos":
return [o.cos(I("x", r, e, t10))];
case "Cosh":
return [o.cosh(I("x", r, e, t10))];
case "Elu":
return [o.elu(I("x", r, e, t10))];
case "Erf":
return [o.erf(I("x", r, e, t10))];
case "Exp":
return [o.exp(I("x", r, e, t10))];
case "Expm1":
return [o.expm1(I("x", r, e, t10))];
case "Floor":
return [o.floor(I("x", r, e, t10))];
case "Log":
return [o.log(I("x", r, e, t10))];
case "Log1p":
return [o.log1p(I("x", r, e, t10))];
case "Imag":
return [o.imag(I("x", r, e, t10))];
case "Neg":
return [o.neg(I("x", r, e, t10))];
case "Reciprocal":
return [o.reciprocal(I("x", r, e, t10))];
case "Real":
return [o.real(I("x", r, e, t10))];
case "Relu":
return [o.relu(I("x", r, e, t10))];
case "Round":
return [o.round(I("x", r, e, t10))];
case "Selu":
return [o.selu(I("x", r, e, t10))];
case "Sigmoid":
return [o.sigmoid(I("x", r, e, t10))];
case "Sin":
return [o.sin(I("x", r, e, t10))];
case "Sign":
return [o.sign(I("x", r, e, t10))];
case "Sinh":
return [o.sinh(I("x", r, e, t10))];
case "Softplus":
return [o.softplus(I("x", r, e, t10))];
case "Sqrt":
return [o.sqrt(I("x", r, e, t10))];
case "Square":
return [o.square(I("x", r, e, t10))];
case "Tanh":
return [o.tanh(I("x", r, e, t10))];
case "Tan":
return [o.tan(I("x", r, e, t10))];
case "ClipByValue":
return [o.clipByValue(I("x", r, e, t10), I("clipValueMin", r, e, t10), I("clipValueMax", r, e, t10))];
case "Relu6":
return [o.relu6(I("x", r, e, t10))];
case "Rsqrt":
return [o.rsqrt(Bt(r.inputNames[0], e, t10))];
case "LeakyRelu":
return [o.leakyRelu(I("x", r, e, t10), I("alpha", r, e, t10))];
case "Prelu":
return [o.prelu(I("x", r, e, t10), I("alpha", r, e, t10))];
case "IsNan":
return [o.isNaN(Bt(r.inputNames[0], e, t10))];
case "IsInf":
return [o.isInf(Bt(r.inputNames[0], e, t10))];
case "IsFinite":
return [o.isFinite(Bt(r.inputNames[0], e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
function Ur(r, e, t10 = "") {
if (!(typeof r == "number" || typeof e == "number")) {
y.assert(r.length === e.length, () => t10 + ` Shapes ${r} and ${e} must match`);
for (let o = 0; o < r.length; o++) {
let n = r[o], s = e[o];
y.assert(n < 0 || s < 0 || n === s, () => t10 + ` Shapes ${r} and ${e} must match`);
}
}
}
function rT(r) {
return !(typeof r == "number" || r.some((e) => e < 0));
}
function fc(r, e, t10) {
let o = bf(r, t10), n = !rT(o);
if (n && e.length === 0)
throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`);
if (n && e.forEach((s) => {
o = bf(s.shape, o);
}), !rT(o))
throw new Error(`Non-fully-defined elementShape: ${o}`);
return o;
}
function bf(r, e) {
if (typeof r == "number")
return e;
if (typeof e == "number")
return r;
if (r.length !== e.length)
throw new Error(`Incompatible ranks during merge: ${r} vs. ${e}`);
let t10 = [];
for (let o = 0; o < r.length; ++o) {
let n = r[o], s = e[o];
if (n >= 0 && s >= 0 && n !== s)
throw new Error(`Incompatible shape during merge: ${r} vs. ${e}`);
t10[o] = n >= 0 ? n : s;
}
return t10;
}
var Cf = class {
constructor(e, t10, o, n, s, a, i) {
this.name = e, this.dtype = t10, this.maxSize = o, this.elementShape = n, this.identicalElementShapes = s, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = ke(0), Er(this.idTensor);
}
get id() {
return this.idTensor.id;
}
get closed() {
return this.closed_;
}
clearAndClose(e) {
this.tensors.forEach((t10) => {
(e == null || !e.has(t10.tensor.id)) && t10.tensor.dispose();
}), this.tensors = [], this.closed_ = true, this.idTensor.dispose();
}
size() {
return this.tensors.length;
}
read(e) {
if (this.closed_)
throw new Error(`TensorArray ${this.name} has already been closed.`);
if (e < 0 || e >= this.size())
throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);
let t10 = this.tensors[e];
if (t10.cleared)
throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);
return this.clearAfterRead && (t10.cleared = true), t10.read = true, t10.tensor;
}
readMany(e) {
return e.map((t10) => this.read(t10));
}
write(e, t10) {
if (this.closed_)
throw new Error(`TensorArray ${this.name} has already been closed.`);
if (e < 0 || !this.dynamicSize && e >= this.maxSize)
throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);
let o = this.tensors[e] || {};
if (t10.dtype !== this.dtype)
throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
because the value dtype is ${t10.dtype}, but TensorArray dtype is ${this.dtype}.`);
if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t10.shape), Ur(this.elementShape, t10.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), o.read)
throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);
if (o.written)
throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);
o.tensor = t10, Er(t10), o.written = true, this.tensors[e] = o;
}
writeMany(e, t10) {
if (e.length !== t10.length)
throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t10.length}.`);
e.forEach((o, n) => this.write(o, t10[n]));
}
gather(e, t10) {
if (t10 && t10 !== this.dtype)
throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t10}`);
if (e)
e = e.slice(0, this.size());
else {
e = [];
for (let n = 0; n < this.size(); n++)
e.push(n);
}
if (e.length === 0)
return ir([], [0].concat(this.elementShape));
let o = this.readMany(e);
return Ur(this.elementShape, o[0].shape, "TensorArray shape mismatch: "), vr(o, 0);
}
concat(e) {
if (e && e !== this.dtype)
throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);
if (this.size() === 0)
return ir([], [0].concat(this.elementShape));
let t10 = [];
for (let n = 0; n < this.size(); n++)
t10.push(n);
let o = this.readMany(t10);
return Ur(this.elementShape, o[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`), yt(o, 0);
}
scatter(e, t10) {
if (t10.dtype !== this.dtype)
throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t10.dtype}`);
if (e.length !== t10.shape[0])
throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t10.shape[0]}`);
let o = Math.max(...e);
if (!this.dynamicSize && o >= this.maxSize)
throw new Error(`Max index must be < array size (${o} vs. ${this.maxSize})`);
this.writeMany(e, po(t10, 0));
}
split(e, t10) {
if (t10.dtype !== this.dtype)
throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t10.dtype}`);
let o = 0, n = e.map((p) => (o += p, o));
if (o !== t10.shape[0])
throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${o}, and tensor's shape is: ${t10.shape}`);
if (!this.dynamicSize && e.length !== this.maxSize)
throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);
let s = o === 0 ? 0 : t10.size / o, a = [];
De(() => {
t10 = W(t10, [1, o, s]);
for (let p = 0; p < e.length; ++p) {
let c = [0, p === 0 ? 0 : n[p - 1], 0], l = [1, e[p], s];
a[p] = W(qe(t10, c, l), this.elementShape);
}
return a;
});
let i = [];
for (let p = 0; p < e.length; p++)
i[p] = p;
this.writeMany(i, a);
}
};
var ci = class {
get id() {
return this.idTensor.id;
}
constructor(e, t10, o, n = -1) {
this.tensors = e, this.elementShape = t10, this.elementDtype = o, e != null && e.forEach((s) => {
if (o !== s.dtype)
throw new Error(`Invalid data types; op elements ${o}, but list elements ${s.dtype}`);
Ur(t10, s.shape, "TensorList shape mismatch: "), Er(s);
}), this.idTensor = ke(0), this.maxNumElements = n, Er(this.idTensor);
}
copy() {
return new ci([...this.tensors], this.elementShape, this.elementDtype);
}
clearAndClose(e) {
this.tensors.forEach((t10) => {
(e == null || !e.has(t10.id)) && t10.dispose();
}), this.tensors.length = 0, this.idTensor.dispose();
}
size() {
return this.tensors.length;
}
stack(e, t10, o = -1) {
if (t10 !== this.elementDtype)
throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`);
if (o !== -1 && this.tensors.length !== o)
throw new Error(`Operation expected a list with ${o} elements but got a list with ${this.tensors.length} elements.`);
Ur(e, this.elementShape, "TensorList shape mismatch: ");
let n = fc(this.elementShape, this.tensors, e);
return De(() => {
let s = this.tensors.map((a) => W(a, n));
return vr(s, 0);
});
}
popBack(e, t10) {
if (t10 !== this.elementDtype)
throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`);
if (this.size() === 0)
throw new Error("Trying to pop from an empty list.");
let o = fc(this.elementShape, this.tensors, e), n = this.tensors.pop();
return n.kept = false, Ur(n.shape, e, "TensorList shape mismatch: "), W(n, o);
}
pushBack(e) {
if (e.dtype !== this.elementDtype)
throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);
if (Ur(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size())
throw new Error("Trying to push element into a full list.");
Er(e), this.tensors.push(e);
}
resize(e) {
if (e < 0)
throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);
if (this.maxNumElements !== -1 && e > this.maxNumElements)
throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);
let t10 = new ci([], this.elementShape, this.elementDtype, this.maxNumElements);
t10.tensors.length = e;
for (let o = 0; o < Math.min(this.tensors.length, e); ++o)
t10.tensors[o] = this.tensors[o];
return t10;
}
getItem(e, t10, o) {
if (o !== this.elementDtype)
throw new Error(`Invalid data types; op elements ${o}, but list elements ${this.elementDtype}`);
if (e < 0 || e > this.tensors.length)
throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);
if (this.tensors[e] == null)
throw new Error(`element at index ${e} is null.`);
Ur(this.tensors[e].shape, t10, "TensorList shape mismatch: ");
let n = fc(this.elementShape, this.tensors, t10);
return W(this.tensors[e], n);
}
setItem(e, t10) {
if (t10.dtype !== this.elementDtype)
throw new Error(`Invalid data types; op elements ${t10.dtype}, but list elements ${this.elementDtype}`);
if (e < 0 || this.maxNumElements !== -1 && e >= this.maxNumElements)
throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);
Ur(this.elementShape, t10.shape, "TensorList shape mismatch: "), Er(t10), this.tensors[e] != null && (this.tensors[e].kept = false), this.tensors[e] = t10;
}
gather(e, t10, o) {
if (t10 !== this.elementDtype)
throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`);
Ur(this.elementShape, o, "TensorList shape mismatch: "), e = e.slice(0, this.size());
let n = fc(this.elementShape, this.tensors, o);
return e.length === 0 ? ir([], [0].concat(n)) : De(() => {
let s = e.map((a) => W(this.tensors[a], n));
return vr(s, 0);
});
}
concat(e, t10) {
if (e && e !== this.elementDtype)
throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);
Ur(this.elementShape, t10, "TensorList shape mismatch: ");
let o = fc(this.elementShape, this.tensors, t10);
return this.size() === 0 ? ir([], [0].concat(o)) : De(() => {
let n = this.tensors.map((s) => W(s, o));
return yt(n, 0);
});
}
};
function oT(r, e, t10) {
let o = r.dtype;
if (r.shape.length < 1)
throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`);
if (r.dtype !== t10)
throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t10}`);
let n = r.shape.slice(1);
Ur(n, e, "TensorList shape mismatch: ");
let s = po(r);
return new ci(s, e, o);
}
function nT(r, e, t10, o) {
return new ci([], r, e, o);
}
function sT(r, e, t10, o) {
if (e.length !== r.shape[0])
throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`);
let n = Math.max(...e);
if (o != null && o !== -1 && n >= o)
throw new Error(`Max index must be < array size (${n} vs. ${o})`);
let s = new ci([], t10, r.dtype, o), a = po(r, 0);
return e.forEach((i, p) => {
s.setItem(i, a[p]);
}), s;
}
function aT(r, e, t10) {
let o = 0, n = e.map((c) => (o += c, o));
if (o !== r.shape[0])
throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${o}, and tensor's shape is: ${r.shape}`);
let s = r.shape.slice(1), a = bf(s, t10), i = o === 0 ? 0 : r.size / o, p = De(() => {
let c = [];
r = W(r, [1, o, i]);
for (let l = 0; l < e.length; ++l) {
let d = [0, l === 0 ? 0 : n[l - 1], 0], f = [1, e[l], i];
c[l] = W(qe(r, d, f), a);
}
return r.dispose(), c;
}), u = new ci([], t10, r.dtype, e.length);
for (let c = 0; c < p.length; c++)
u.setItem(c, p[c]);
return u;
}
var iT = async (r, e, t10) => {
switch (r.op) {
case "If":
case "StatelessIf": {
let o = I("thenBranch", r, e, t10), n = I("elseBranch", r, e, t10), s = I("cond", r, e, t10), a = I("args", r, e, t10);
return (await s.data())[0] ? t10.functionMap[o].executeFunctionAsync(a, t10.tensorArrayMap, t10.tensorListMap) : t10.functionMap[n].executeFunctionAsync(a, t10.tensorArrayMap, t10.tensorListMap);
}
case "While":
case "StatelessWhile": {
let o = I("body", r, e, t10), n = I("cond", r, e, t10), s = I("args", r, e, t10), a = await t10.functionMap[n].executeFunctionAsync(s, t10.tensorArrayMap, t10.tensorListMap), i = s.map((c) => c.id), p = await a[0].data();
a.forEach((c) => {
!c.kept && i.indexOf(c.id) === -1 && c.dispose();
});
let u = s;
for (; p[0]; ) {
let c = u;
u = await t10.functionMap[o].executeFunctionAsync(u, t10.tensorArrayMap, t10.tensorListMap);
let l = u.map((d) => d.id);
c.forEach((d) => {
!d.kept && i.indexOf(d.id) === -1 && l.indexOf(d.id) === -1 && d.dispose();
});
let m = await t10.functionMap[n].executeFunctionAsync(u, t10.tensorArrayMap, t10.tensorListMap);
p = await m[0].data(), m.forEach((d) => {
!d.kept && i.indexOf(d.id) === -1 && l.indexOf(d.id) === -1 && d.dispose();
});
}
return u;
}
case "LoopCond": {
let o = I("pred", r, e, t10);
return [As(o)];
}
case "Switch": {
let o = I("pred", r, e, t10), n = I("data", r, e, t10);
return n.kept || (n = As(n)), (await o.data())[0] ? [void 0, n] : [n, void 0];
}
case "Merge": {
let o = r.inputNames.find((n) => Bt(n, e, t10) !== void 0);
if (o) {
let n = Bt(o, e, t10);
return [As(n)];
}
return;
}
case "Enter": {
let o = I("frameName", r, e, t10), n = I("tensor", r, e, t10);
return t10.enterFrame(o), [As(n)];
}
case "Exit": {
let o = I("tensor", r, e, t10);
return t10.exitFrame(), [As(o)];
}
case "NextIteration": {
let o = I("tensor", r, e, t10);
return t10.nextIteration(), [As(o)];
}
case "TensorArrayV3": {
let o = I("size", r, e, t10), n = I("dtype", r, e, t10), s = I("elementShape", r, e, t10), a = I("dynamicSize", r, e, t10), i = I("clearAfterRead", r, e, t10), p = I("identicalElementShapes", r, e, t10), u = I("name", r, e, t10), c = new Cf(u, n, o, s, p, a, i);
return t10.addTensorArray(c), [c.idTensor, ke(1)];
}
case "TensorArrayWriteV3": {
let o = I("tensorArrayId", r, e, t10), n = I("index", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorArray(o.id);
return a.write(n, s), [a.idTensor];
}
case "TensorArrayReadV3": {
let o = I("tensorArrayId", r, e, t10), n = I("index", r, e, t10);
return [t10.getTensorArray(o.id).read(n)];
}
case "TensorArrayGatherV3": {
let o = I("tensorArrayId", r, e, t10), n = I("indices", r, e, t10), s = I("dtype", r, e, t10);
return [t10.getTensorArray(o.id).gather(n, s)];
}
case "TensorArrayScatterV3": {
let o = I("tensorArrayId", r, e, t10), n = I("indices", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorArray(o.id);
return a.scatter(n, s), [a.idTensor];
}
case "TensorArrayConcatV3": {
let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id), s = I("dtype", r, e, t10);
return [n.concat(s)];
}
case "TensorArraySplitV3": {
let o = I("tensorArrayId", r, e, t10), n = I("tensor", r, e, t10), s = I("lengths", r, e, t10), a = t10.getTensorArray(o.id);
return a.split(s, n), [a.idTensor];
}
case "TensorArraySizeV3": {
let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id);
return [ke(n.size(), "int32")];
}
case "TensorArrayCloseV3": {
let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id);
return n.clearAndClose(), [n.idTensor];
}
case "TensorListSetItem": {
let o = I("tensorListId", r, e, t10), n = I("index", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorList(o.id);
return a.setItem(n, s), [a.idTensor];
}
case "TensorListGetItem": {
let o = I("tensorListId", r, e, t10), n = I("index", r, e, t10), s = I("elementShape", r, e, t10), a = I("elementDType", r, e, t10);
return [t10.getTensorList(o.id).getItem(n, s, a)];
}
case "TensorListScatterV2":
case "TensorListScatter": {
let o = I("indices", r, e, t10), n = I("tensor", r, e, t10), s = I("elementShape", r, e, t10), a = I("numElements", r, e, t10), i = sT(n, o, s, a);
return t10.addTensorList(i), [i.idTensor];
}
case "TensorListReserve":
case "EmptyTensorList": {
let o = I("elementShape", r, e, t10), n = I("elementDType", r, e, t10), s;
r.op === "TensorListReserve" ? s = "numElements" : s = "maxNumElements";
let a = I(s, r, e, t10), i = r.op === "TensorListReserve" ? -1 : a, p = nT(o, n, a, i);
return t10.addTensorList(p), [p.idTensor];
}
case "TensorListGather": {
let o = I("tensorListId", r, e, t10), n = I("indices", r, e, t10), s = I("elementShape", r, e, t10), a = I("elementDType", r, e, t10);
return [t10.getTensorList(o.id).gather(n, a, s)];
}
case "TensorListStack": {
let o = I("tensorListId", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10), a = I("numElements", r, e, t10);
return [t10.getTensorList(o.id).stack(n, s, a)];
}
case "TensorListFromTensor": {
let o = I("tensor", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10), a = oT(o, n, s);
return t10.addTensorList(a), [a.idTensor];
}
case "TensorListConcat":
case "TensorListConcatV2": {
let o = I("tensorListId", r, e, t10), n = t10.getTensorList(o.id), s = I("dtype", r, e, t10), a = I("elementShape", r, e, t10);
return [n.concat(s, a)];
}
case "TensorListPushBack": {
let o = I("tensorListId", r, e, t10), n = I("tensor", r, e, t10), s = t10.getTensorList(o.id);
return s.pushBack(n), [s.idTensor];
}
case "TensorListPopBack": {
let o = I("tensorListId", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10);
return [t10.getTensorList(o.id).popBack(n, s)];
}
case "TensorListSplit": {
let o = I("tensor", r, e, t10), n = I("elementShape", r, e, t10), s = I("lengths", r, e, t10), a = aT(o, s, n);
return t10.addTensorList(a), [a.idTensor];
}
case "TensorListLength": {
let o = I("tensorListId", r, e, t10), n = t10.getTensorList(o.id);
return [ke(n.size(), "int32")];
}
case "TensorListResize": {
let o = I("tensorListId", r, e, t10), n = I("size", r, e, t10), a = t10.getTensorList(o.id).resize(n);
return t10.addTensorList(a), [a.idTensor];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
function uT(r, e, t10) {
let [o, n] = I("fusedOps", r, e, t10), s = o === "biasadd", a = !s, i = n === "prelu", p = o === "fusedbatchnorm", u = I("numArgs", r, e, t10);
if (s) {
if (i && u !== 2)
throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
if (!i && s && u !== 1)
throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.");
}
if (p)
throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");
let c = I("strides", r, e, t10), l = Al(r, e, t10), m = I("dataFormat", r, e, t10).toUpperCase(), d = I("dilations", r, e, t10), [f, h] = I("args", r, e, t10);
a && (h = f, f = void 0);
let g = I("leakyreluAlpha", r, e, t10);
return { stride: c, pad: l, dataFormat: m, dilations: d, biasArg: f, preluArg: h, activationFunc: n, leakyreluAlpha: g };
}
var pT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "Conv1D": {
let n = I("stride", r, e, t10), s = I("pad", r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilation", r, e, t10);
return [o.conv1d(I("x", r, e, t10), I("filter", r, e, t10), n, s, a, i)];
}
case "Conv2D": {
let n = I("strides", r, e, t10), s = Al(r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilations", r, e, t10);
return [o.conv2d(I("x", r, e, t10), I("filter", r, e, t10), [n[1], n[2]], s, a, [i[1], i[2]])];
}
case "_FusedConv2D": {
let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = uT(r, e, t10);
return [o.fused.conv2d({ x: I("x", r, e, t10), filter: I("filter", r, e, t10), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })];
}
case "FusedDepthwiseConv2dNative": {
let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = uT(r, e, t10);
return [o.fused.depthwiseConv2d({ x: I("x", r, e, t10), filter: I("filter", r, e, t10), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })];
}
case "Conv2DBackpropInput":
case "Conv2dTranspose": {
let n = I("outputShape", r, e, t10), s = I("strides", r, e, t10), a = Al(r, e, t10);
return [o.conv2dTranspose(I("x", r, e, t10), I("filter", r, e, t10), n, [s[1], s[2]], a)];
}
case "DepthwiseConv2dNative":
case "DepthwiseConv2d": {
let n = I("strides", r, e, t10), s = Al(r, e, t10), a = I("dilations", r, e, t10), i = I("dataFormat", r, e, t10).toUpperCase();
return [o.depthwiseConv2d(I("input", r, e, t10), I("filter", r, e, t10), [n[1], n[2]], s, i, [a[1], a[2]])];
}
case "Conv3D": {
let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilations", r, e, t10);
return [o.conv3d(I("x", r, e, t10), I("filter", r, e, t10), [n[1], n[2], n[3]], s, a, [i[1], i[2], i[3]])];
}
case "AvgPool": {
let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10);
return [o.avgPool(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s)];
}
case "MaxPool": {
let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10);
return [o.maxPool(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s)];
}
case "MaxPoolWithArgmax": {
let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10), i = I("includeBatchInIndex", r, e, t10), { result: p, indexes: u } = o.maxPoolWithArgmax(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s, i);
return [p, u];
}
case "AvgPool3D": {
let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10);
return [o.avgPool3d(I("x", r, e, t10), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)];
}
case "MaxPool3D": {
let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10);
return [o.maxPool3d(I("x", r, e, t10), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)];
}
case "Dilation2D": {
let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("dilations", r, e, t10), i = n[1], p = n[2], u = a[1], c = a[2];
return [o.dilation2d(I("x", r, e, t10), I("filter", r, e, t10), [i, p], s, [u, c], "NHWC")];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var cT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "Fill": {
let n = I("shape", r, e, t10), s = I("dtype", r, e, t10), a = I("value", r, e, t10);
return [o.fill(n, a, s)];
}
case "LinSpace": {
let n = I("start", r, e, t10), s = I("stop", r, e, t10), a = I("num", r, e, t10);
return [o.linspace(n, s, a)];
}
case "Multinomial": {
let n = I("logits", r, e, t10), s = I("numSamples", r, e, t10), a = I("seed", r, e, t10);
return [o.multinomial(n, s, a)];
}
case "OneHot": {
let n = I("indices", r, e, t10), s = I("depth", r, e, t10), a = I("onValue", r, e, t10), i = I("offValue", r, e, t10), p = I("dtype", r, e, t10);
return [o.oneHot(n, s, a, i, p)];
}
case "Ones":
return [o.ones(I("shape", r, e, t10), I("dtype", r, e, t10))];
case "OnesLike":
return [o.onesLike(I("x", r, e, t10))];
case "RandomStandardNormal":
return [o.randomStandardNormal(I("shape", r, e, t10), I("dtype", r, e, t10), I("seed", r, e, t10))];
case "RandomUniform":
return [o.randomUniform(I("shape", r, e, t10), I("minval", r, e, t10), I("maxval", r, e, t10), I("dtype", r, e, t10))];
case "RandomUniformInt":
return [o.randomUniformInt(I("shape", r, e, t10), I("minval", r, e, t10), I("maxval", r, e, t10), I("seed", r, e, t10))];
case "Range": {
let n = I("start", r, e, t10), s = I("stop", r, e, t10), a = I("step", r, e, t10);
return [o.range(n, s, a, I("dtype", r, e, t10))];
}
case "TruncatedNormal": {
let n = I("shape", r, e, t10), s = I("mean", r, e, t10), a = I("stdDev", r, e, t10), i = I("seed", r, e, t10);
return [o.truncatedNormal(n, s, a, I("dtype", r, e, t10), i)];
}
case "Zeros":
return [o.zeros(I("shape", r, e, t10), I("dtype", r, e, t10))];
case "ZerosLike":
return [o.zerosLike(I("x", r, e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
function yS(r, e, t10) {
let o = I("boxes", r, e, t10), n = I("scores", r, e, t10), s = I("maxOutputSize", r, e, t10), a = I("iouThreshold", r, e, t10), i = I("scoreThreshold", r, e, t10), p = I("softNmsSigma", r, e, t10);
return { boxes: o, scores: n, maxOutputSize: s, iouThreshold: a, scoreThreshold: i, softNmsSigma: p };
}
var lT = async (r, e, t10, o, n = Ze) => {
switch (r.op) {
case "NonMaxSuppressionV5": {
let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u, softNmsSigma: c } = yS(r, e, t10), l = await n.image.nonMaxSuppressionWithScoreAsync(s, a, i, p, u, c);
return [l.selectedIndices, l.selectedScores];
}
case "NonMaxSuppressionV4": {
let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = yS(r, e, t10), c = I("padToMaxOutputSize", r, e, t10), l = await n.image.nonMaxSuppressionPaddedAsync(s, a, i, p, u, c);
return [l.selectedIndices, l.validOutputs];
}
case "NonMaxSuppressionV3":
case "NonMaxSuppressionV2": {
let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = yS(r, e, t10);
return [await n.image.nonMaxSuppressionAsync(s, a, i, p, u)];
}
case "Where": {
let s = n.cast(I("condition", r, e, t10), "bool"), a = [await n.whereAsync(s)];
return s.dispose(), a;
}
case "ListDiff":
return n.setdiff1dAsync(I("x", r, e, t10), I("y", r, e, t10));
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var mT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "LowerBound": {
let n = I("sortedSequence", r, e, t10), s = I("values", r, e, t10);
return [o.lowerBound(n, s)];
}
case "TopKV2": {
let n = I("x", r, e, t10), s = I("k", r, e, t10), a = I("sorted", r, e, t10), i = o.topk(n, s, a);
return [i.values, i.indices];
}
case "UpperBound": {
let n = I("sortedSequence", r, e, t10), s = I("values", r, e, t10);
return [o.upperBound(n, s)];
}
case "Unique": {
let n = I("x", r, e, t10), s = o.unique(n);
return [s.values, s.indices];
}
case "UniqueV2": {
let n = I("x", r, e, t10), s = I("axis", r, e, t10), a = o.unique(n, s);
return [a.values, a.indices];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var dT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "Const":
return e[r.name];
case "PlaceholderWithDefault":
let n = I("default", r, e, t10);
return [Bt(r.name, e, t10) || n];
case "Placeholder":
return [Bt(r.name, e, t10)];
case "Identity":
case "StopGradient":
case "FakeQuantWithMinMaxVars": {
let c = I("x", r, e, t10);
return [As(c)];
}
case "IdentityN":
return I("x", r, e, t10).map((c) => As(c));
case "Snapshot":
let s = I("x", r, e, t10);
return [As(s)];
case "Shape":
return [o.tensor1d(I("x", r, e, t10).shape, "int32")];
case "ShapeN":
return I("x", r, e, t10).map((c) => o.tensor1d(c.shape));
case "Size":
return [o.scalar(I("x", r, e, t10).size, "int32")];
case "Rank":
return [o.scalar(I("x", r, e, t10).rank, "int32")];
case "NoOp":
return [o.scalar(1)];
case "Print":
let a = I("x", r, e, t10), i = I("data", r, e, t10), p = I("message", r, e, t10), u = I("summarize", r, e, t10);
console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."), console.log(p);
for (let c = 0; c < i.length; c++)
console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0, u));
return [a];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var wf = class {
get id() {
return this.handle.id;
}
constructor(e, t10) {
this.keyDType = e, this.valueDType = t10, this.handle = ke(0), this.tensorMap = /* @__PURE__ */ new Map(), Er(this.handle);
}
clearAndClose() {
this.tensorMap.forEach((e) => e.dispose()), this.tensorMap.clear(), this.handle.dispose();
}
size() {
return this.tensorMap.size;
}
tensorSize() {
return ke(this.size(), "int32");
}
async import(e, t10) {
this.checkKeyAndValueTensor(e, t10);
let o = await e.data();
return this.tensorMap.forEach((n) => n.dispose()), this.tensorMap.clear(), De(() => {
let n = po(t10), s = o.length, a = n.length;
y.assert(s === a, () => `The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);
for (let i = 0; i < s; i++) {
let p = o[i], u = n[i];
Er(u), this.tensorMap.set(p, u);
}
return this.handle;
});
}
async find(e, t10) {
this.checkKeyAndValueTensor(e, t10);
let o = await e.data();
return De(() => {
let n = [];
for (let s = 0; s < o.length; s++) {
let a = o[s], i = this.findWithDefault(a, t10);
n.push(i);
}
return vr(n);
});
}
findWithDefault(e, t10) {
let o = this.tensorMap.get(e);
return o != null ? o : t10;
}
checkKeyAndValueTensor(e, t10) {
if (e.dtype !== this.keyDType)
throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);
if (t10.dtype !== this.valueDType)
throw new Error(`Expect value dtype ${this.valueDType}, but got ${t10.dtype}`);
}
};
var fT = async (r, e, t10, o) => {
switch (r.op) {
case "HashTable":
case "HashTableV2": {
let n = o.getHashTableHandleByName(r.name);
if (n != null)
return [n];
{
let s = I("keyDType", r, e, t10), a = I("valueDType", r, e, t10), i = new wf(s, a);
return o.addHashTable(r.name, i), [i.handle];
}
}
case "InitializeTable":
case "InitializeTableV2":
case "LookupTableImport":
case "LookupTableImportV2": {
let n = I("tableHandle", r, e, t10, o), s = I("keys", r, e, t10), a = I("values", r, e, t10);
return [await o.getHashTableById(n.id).import(s, a)];
}
case "LookupTableFind":
case "LookupTableFindV2": {
let n = I("tableHandle", r, e, t10, o), s = I("keys", r, e, t10), a = I("defaultValue", r, e, t10);
return [await o.getHashTableById(n.id).find(s, a)];
}
case "LookupTableSize":
case "LookupTableSizeV2": {
let n = I("tableHandle", r, e, t10, o);
return [o.getHashTableById(n.id).tensorSize()];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var hT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "ResizeBilinear": {
let n = I("images", r, e, t10), s = I("size", r, e, t10), a = I("alignCorners", r, e, t10), i = I("halfPixelCenters", r, e, t10);
return [o.image.resizeBilinear(n, [s[0], s[1]], a, i)];
}
case "ResizeNearestNeighbor": {
let n = I("images", r, e, t10), s = I("size", r, e, t10), a = I("alignCorners", r, e, t10), i = I("halfPixelCenters", r, e, t10);
return [o.image.resizeNearestNeighbor(n, [s[0], s[1]], a, i)];
}
case "CropAndResize": {
let n = I("image", r, e, t10), s = I("boxes", r, e, t10), a = I("boxInd", r, e, t10), i = I("cropSize", r, e, t10), p = I("method", r, e, t10), u = I("extrapolationValue", r, e, t10);
return [o.image.cropAndResize(n, s, a, i, p, u)];
}
case "ImageProjectiveTransformV3": {
let n = I("images", r, e, t10), s = I("transforms", r, e, t10), a = I("outputShape", r, e, t10), i = I("fillValue", r, e, t10), p = I("interpolation", r, e, t10), u = I("fillMode", r, e, t10);
return [o.image.transform(n, s, p.toLowerCase(), u.toLowerCase(), i, a)];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var gT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "Equal":
return [o.equal(I("a", r, e, t10), I("b", r, e, t10))];
case "NotEqual":
return [o.notEqual(I("a", r, e, t10), I("b", r, e, t10))];
case "Greater":
return [o.greater(I("a", r, e, t10), I("b", r, e, t10))];
case "GreaterEqual":
return [o.greaterEqual(I("a", r, e, t10), I("b", r, e, t10))];
case "Less":
return [o.less(I("a", r, e, t10), I("b", r, e, t10))];
case "LessEqual":
return [o.lessEqual(I("a", r, e, t10), I("b", r, e, t10))];
case "LogicalAnd":
return [o.logicalAnd(I("a", r, e, t10), I("b", r, e, t10))];
case "LogicalNot":
return [o.logicalNot(I("a", r, e, t10))];
case "LogicalOr":
return [o.logicalOr(I("a", r, e, t10), I("b", r, e, t10))];
case "Select":
case "SelectV2":
return [o.where(I("condition", r, e, t10), I("a", r, e, t10), I("b", r, e, t10))];
case "BitwiseAnd":
return [o.bitwiseAnd(I("a", r, e, t10), I("b", r, e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var xT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "BatchMatMul":
case "BatchMatMulV2":
case "MatMul":
return [o.matMul(I("a", r, e, t10), I("b", r, e, t10), I("transposeA", r, e, t10), I("transposeB", r, e, t10))];
case "Einsum":
return [o.einsum(I("equation", r, e, t10), ...I("tensors", r, e, t10))];
case "Transpose":
return [o.transpose(I("x", r, e, t10), I("perm", r, e, t10))];
case "_FusedMatMul":
let [n, s] = I("fusedOps", r, e, t10), a = n === "biasadd", i = s === "prelu", p = I("numArgs", r, e, t10), u = I("leakyreluAlpha", r, e, t10);
if (a) {
if (i && p !== 2)
throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
if (!i && p !== 1)
throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.");
}
let [c, l] = I("args", r, e, t10);
return [o.fused.matMul({ a: I("a", r, e, t10), b: I("b", r, e, t10), transposeA: I("transposeA", r, e, t10), transposeB: I("transposeB", r, e, t10), bias: c, activation: s, preluActivationWeights: l, leakyreluAlpha: u })];
case "MatrixBandPart":
return [o.linalg.bandPart(I("a", r, e, t10), I("numLower", r, e, t10), I("numUpper", r, e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var yT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "EuclideanNorm":
return [o.euclideanNorm(I("x", r, e, t10), I("axis", r, e, t10), I("keepDims", r, e, t10))];
case "FusedBatchNorm":
case "FusedBatchNormV2":
return [o.batchNorm(I("x", r, e, t10), I("mean", r, e, t10), I("variance", r, e, t10), I("offset", r, e, t10), I("scale", r, e, t10), I("epsilon", r, e, t10))];
case "FusedBatchNormV3":
return [o.batchNorm(I("x", r, e, t10), I("mean", r, e, t10), I("variance", r, e, t10), I("offset", r, e, t10), I("scale", r, e, t10), I("epsilon", r, e, t10))];
case "LRN":
return [o.localResponseNormalization(I("x", r, e, t10), I("radius", r, e, t10), I("bias", r, e, t10), I("alpha", r, e, t10), I("beta", r, e, t10))];
case "Softmax":
return [o.softmax(I("x", r, e, t10))];
case "LogSoftmax":
return [o.logSoftmax(I("x", r, e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var bT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "RaggedGather": {
let { outputNestedSplits: n, outputDenseValues: s } = o.raggedGather(I("paramsNestedSplits", r, e, t10), I("paramsDenseValues", r, e, t10), I("indices", r, e, t10), I("outputRaggedRank", r, e, t10));
return n.concat(s);
}
case "RaggedRange": {
let { rtNestedSplits: n, rtDenseValues: s } = o.raggedRange(I("starts", r, e, t10), I("limits", r, e, t10), I("splits", r, e, t10));
return [n, s];
}
case "RaggedTensorToTensor":
return [o.raggedTensorToTensor(I("shape", r, e, t10), I("values", r, e, t10), I("defaultValue", r, e, t10), I("rowPartitionTensors", r, e, t10), I("rowPartitionTypes", r, e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var CT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "Max": {
let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
return [o.max(I("x", r, e, t10), i, p)];
}
case "Mean": {
let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
return [o.mean(I("x", r, e, t10), i, p)];
}
case "Min": {
let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
return [o.min(I("x", r, e, t10), i, p)];
}
case "Sum": {
let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
return [o.sum(I("x", r, e, t10), i, p)];
}
case "All": {
let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
return [o.all(I("x", r, e, t10), i, p)];
}
case "Any": {
let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
return [o.any(I("x", r, e, t10), i, p)];
}
case "ArgMax": {
let i = I("axis", r, e, t10);
return [o.argMax(I("x", r, e, t10), i)];
}
case "ArgMin": {
let i = I("axis", r, e, t10);
return [o.argMin(I("x", r, e, t10), i)];
}
case "Prod": {
let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
return [o.prod(I("x", r, e, t10), i, p)];
}
case "Cumprod": {
let i = I("axis", r, e, t10), p = I("exclusive", r, e, t10), u = I("reverse", r, e, t10);
return [o.cumprod(I("x", r, e, t10), i, p, u)];
}
case "Cumsum": {
let i = I("axis", r, e, t10), p = I("exclusive", r, e, t10), u = I("reverse", r, e, t10);
return [o.cumsum(I("x", r, e, t10), i, p, u)];
}
case "Bincount":
let n = I("x", r, e, t10), s = I("weights", r, e, t10), a = I("size", r, e, t10);
return [o.bincount(n, s, a)];
case "DenseBincount": {
let i = I("x", r, e, t10), p = I("weights", r, e, t10), u = I("size", r, e, t10), c = I("binaryOutput", r, e, t10);
return [o.denseBincount(i, p, u, c)];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var wT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "ConcatV2":
case "Concat": {
let n = I("n", r, e, t10), s = I("axis", r, e, t10), a = I("tensors", r, e, t10);
return a = a.slice(0, n), [o.concat(a, s)];
}
case "Gather": {
let n = I("x", r, e, t10), s = I("indices", r, e, t10);
return [o.gather(n, o.cast(s, "int32"), 0)];
}
case "GatherV2": {
let n = I("axis", r, e, t10), s = I("batchDims", r, e, t10), a = I("x", r, e, t10), i = I("indices", r, e, t10);
return [o.gather(a, o.cast(i, "int32"), n, s)];
}
case "Reverse": {
let n = I("dims", r, e, t10), s = [];
for (let i = 0; i < n.length; i++)
n[i] && s.push(i);
let a = I("x", r, e, t10);
return [o.reverse(a, s)];
}
case "ReverseV2": {
let n = I("axis", r, e, t10), s = I("x", r, e, t10);
return [o.reverse(s, n)];
}
case "Slice": {
let n = I("begin", r, e, t10), s = I("size", r, e, t10);
return [o.slice(I("x", r, e, t10), n, s)];
}
case "StridedSlice": {
let n = I("begin", r, e, t10), s = I("end", r, e, t10), a = I("strides", r, e, t10), i = I("beginMask", r, e, t10), p = I("endMask", r, e, t10), u = I("ellipsisMask", r, e, t10), c = I("newAxisMask", r, e, t10), l = I("shrinkAxisMask", r, e, t10), m = I("x", r, e, t10);
return [o.stridedSlice(m, n, s, a, i, p, u, c, l)];
}
case "Pack":
return De(() => {
let n = I("axis", r, e, t10), s = I("tensors", r, e, t10), a = s[0].shape, i = o.squeeze(s[0]).shape, p = s.map((u) => {
let c = y.arraysEqual(u.shape, a);
if (!c && !y.arraysEqual(o.squeeze(u).shape, i))
throw new Error("the input tensors shape does not match");
return c ? u : o.reshape(u, a);
});
return [o.stack(p, n)];
});
case "Unpack": {
let n = I("axis", r, e, t10), s = I("tensor", r, e, t10);
return o.unstack(s, n);
}
case "Tile": {
let n = I("reps", r, e, t10);
return [o.tile(I("x", r, e, t10), n)];
}
case "Split":
case "SplitV": {
let n = I("axis", r, e, t10), s = I("numOrSizeSplits", r, e, t10), a = I("x", r, e, t10);
return o.split(a, s, n);
}
case "ScatterNd": {
let n = I("indices", r, e, t10), s = I("values", r, e, t10), a = I("shape", r, e, t10);
return [o.scatterND(n, s, a)];
}
case "GatherNd": {
let n = I("x", r, e, t10), s = I("indices", r, e, t10);
return [o.gatherND(n, s)];
}
case "SparseToDense": {
let n = I("sparseIndices", r, e, t10), s = I("outputShape", r, e, t10), a = I("sparseValues", r, e, t10), i = I("defaultValue", r, e, t10);
return [o.sparseToDense(n, a, s, a.dtype === i.dtype ? i : o.cast(i, a.dtype))];
}
case "TensorScatterUpdate": {
let n = I("indices", r, e, t10), s = I("values", r, e, t10), a = I("tensor", r, e, t10);
return [o.tensorScatterUpdate(a, n, s)];
}
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var ST = (r, e, t10, o = Ze) => {
switch (r.op) {
case "SparseFillEmptyRows": {
let { outputIndices: n, outputValues: s, emptyRowIndicator: a, reverseIndexMap: i } = o.sparse.sparseFillEmptyRows(I("indices", r, e, t10), I("values", r, e, t10), I("denseShape", r, e, t10), I("defaultValue", r, e, t10));
return [n, s, a, i];
}
case "SparseReshape": {
let { outputIndices: n, outputShape: s } = o.sparse.sparseReshape(I("inputIndices", r, e, t10), I("inputShape", r, e, t10), I("newShape", r, e, t10));
return [n, s];
}
case "SparseSegmentMean":
return [o.sparse.sparseSegmentMean(I("data", r, e, t10), I("indices", r, e, t10), I("segmentIds", r, e, t10))];
case "SparseSegmentSum":
return [o.sparse.sparseSegmentSum(I("data", r, e, t10), I("indices", r, e, t10), I("segmentIds", r, e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var IT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "FFT":
return [o.fft(I("x", r, e, t10))];
case "IFFT":
return [o.ifft(I("x", r, e, t10))];
case "RFFT":
return [o.rfft(I("x", r, e, t10))];
case "IRFFT":
return [o.irfft(I("x", r, e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var vT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "StaticRegexReplace":
return [o.string.staticRegexReplace(I("input", r, e, t10), I("pattern", r, e, t10), I("rewrite", r, e, t10), I("replaceGlobal", r, e, t10))];
case "StringNGrams": {
let { nGrams: n, nGramsSplits: s } = o.string.stringNGrams(I("data", r, e, t10), I("dataSplits", r, e, t10), I("separator", r, e, t10), I("nGramWidths", r, e, t10), I("leftPad", r, e, t10), I("rightPad", r, e, t10), I("padWidth", r, e, t10), I("preserveShortSequences", r, e, t10));
return [n, s];
}
case "StringSplit": {
let { indices: n, values: s, shape: a } = o.string.stringSplit(I("input", r, e, t10), I("delimiter", r, e, t10), I("skipEmpty", r, e, t10));
return [n, s, a];
}
case "StringToHashBucketFast":
return [o.string.stringToHashBucketFast(I("input", r, e, t10), I("numBuckets", r, e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
var kT = (r, e, t10, o = Ze) => {
switch (r.op) {
case "Cast":
return [o.cast(I("x", r, e, t10), I("dtype", r, e, t10))];
case "ExpandDims": {
let n = I("axis", r, e, t10);
return [o.expandDims(I("x", r, e, t10), n)];
}
case "Squeeze": {
let n = I("axis", r, e, t10);
return [o.squeeze(I("x", r, e, t10), n)];
}
case "Reshape":
return [o.reshape(I("x", r, e, t10), I("shape", r, e, t10))];
case "EnsureShape":
return [o.ensureShape(I("x", r, e, t10), I("shape", r, e, t10))];
case "MirrorPad":
return [o.mirrorPad(I("x", r, e, t10), I("padding", r, e, t10), I("mode", r, e, t10))];
case "PadV2":
case "Pad":
return [o.pad(I("x", r, e, t10), I("padding", r, e, t10), I("constantValue", r, e, t10))];
case "SpaceToBatchND": {
let n = I("blockShape", r, e, t10), s = I("paddings", r, e, t10);
return [o.spaceToBatchND(I("x", r, e, t10), n, s)];
}
case "BatchToSpaceND": {
let n = I("blockShape", r, e, t10), s = I("crops", r, e, t10);
return [o.batchToSpaceND(I("x", r, e, t10), n, s)];
}
case "DepthToSpace": {
let n = I("blockSize", r, e, t10), s = I("dataFormat", r, e, t10).toUpperCase();
return [o.depthToSpace(I("x", r, e, t10), n, s)];
}
case "BroadcastTo":
return [o.broadcastTo(I("x", r, e, t10), I("shape", r, e, t10))];
case "BroadcastArgs":
return [o.broadcastArgs(I("s0", r, e, t10), I("s1", r, e, t10))];
default:
throw TypeError(`Node type ${r.op} is not implemented`);
}
};
function bS(r, e, t10, o, n = De) {
let s = ((a, i, p) => {
switch (a.category) {
case "arithmetic":
return n(() => eT(a, i, p));
case "basic_math":
return n(() => tT(a, i, p));
case "control":
return iT(a, i, p);
case "convolution":
return n(() => pT(a, i, p));
case "creation":
return n(() => cT(a, i, p));
case "dynamic":
return lT(a, i, p);
case "evaluation":
return n(() => mT(a, i, p));
case "image":
return n(() => hT(a, i, p));
case "graph":
return n(() => dT(a, i, p));
case "logical":
return n(() => gT(a, i, p));
case "matrices":
return n(() => xT(a, i, p));
case "normalization":
return n(() => yT(a, i, p));
case "ragged":
return n(() => bT(a, i, p));
case "reduction":
return n(() => CT(a, i, p));
case "slice_join":
return n(() => wT(a, i, p));
case "sparse":
return n(() => ST(a, i, p));
case "spectral":
return n(() => IT(a, i, p));
case "string":
return n(() => vT(a, i, p));
case "transformation":
return n(() => kT(a, i, p));
case "hash_table":
return fT(a, i, p, o);
case "custom":
let u = sf(a.op);
if (u && u.customExecutor)
return u.customExecutor(new yf(a, i, p));
throw TypeError(`Custom op ${a.op} is not registered.`);
default:
throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);
}
})(r, e, t10);
return y.isPromise(s) ? s.then((a) => [].concat(a)) : [].concat(s);
}
var Pl = class {
constructor(e = {}, t10 = {}, o = {}, n = {}, s) {
this.weightMap = e, this.tensorArrayMap = t10, this.tensorListMap = o, this.functionMap = n, this.parseNodeNameCache = s, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds();
}
newFrame(e, t10) {
return { id: e, frameName: t10, iterationId: 0 };
}
set currentContext(e) {
this.contexts !== e && (this.contexts = e, this.generateCurrentContextIds());
}
get currentContext() {
return this.contexts;
}
get currentContextId() {
return this._currentContextIds[0];
}
get currentContextIds() {
return this._currentContextIds;
}
generateCurrentContextIds() {
let e = [];
for (let t10 = 0; t10 < this.contexts.length - 1; t10++) {
let o = this.contexts.slice(0, this.contexts.length - t10);
e.push(this.contextIdforContexts(o));
}
e.push(""), this._currentContextIds = e;
}
contextIdforContexts(e) {
return e ? e.map((t10) => t10.id === 0 && t10.iterationId === 0 ? "" : `${t10.frameName}-${t10.iterationId}`).join("/") : "";
}
enterFrame(e) {
this.contexts && (this.lastId++, this.contexts = this.contexts.slice(), this.contexts.push(this.newFrame(this.lastId, e)), this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)));
}
exitFrame() {
if (this.contexts && this.contexts.length > 1)
this.contexts = this.contexts.slice(), this.contexts.splice(-1), this.currentContextIds.shift();
else
throw new Error("Cannot exit frame, the context is empty");
}
nextIteration() {
if (this.contexts && this.contexts.length > 0) {
this.contexts = this.contexts.slice(), this.lastId++;
let e = Object.assign({}, this.contexts[this.contexts.length - 1]);
e.iterationId += 1, e.id = this.lastId, this.contexts.splice(-1, 1, e), this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));
} else
throw new Error("Cannot increase frame iteration, the context is empty");
}
getWeight(e) {
return this.weightMap[e];
}
addTensorArray(e) {
this.tensorArrayMap[e.id] = e;
}
getTensorArray(e) {
return this.tensorArrayMap[e];
}
addTensorList(e) {
this.tensorListMap[e.id] = e;
}
getTensorList(e) {
return this.tensorListMap[e];
}
dispose(e) {
for (let t10 in this.tensorArrayMap)
this.tensorArrayMap[t10].clearAndClose(e);
for (let t10 in this.tensorListMap)
this.tensorListMap[t10].clearAndClose(e);
}
};
function CS(r, e, t10, o) {
let n = /* @__PURE__ */ new Set(), s = [], a = null, i = null, p = /* @__PURE__ */ new Set(), u = new Set(Object.keys(r).map((m) => Nr(m)[0]));
o = o || [];
let c = new Set(o.map((m) => Nr(m.name)[0])), l = [...e];
for (; l.length > 0; ) {
let m = l.pop();
if ((cu(m) || L5(m) || B5(m)) && a == null && (a = m, i = a.children.map((d) => d.name).filter((d) => n.has(d))), n.add(m.name), t10[m.name] == null && !u.has(m.name) && !c.has(m.name)) {
if (m.inputs.length === 0) {
s.push(m.name);
continue;
}
m.inputs.forEach((d) => {
p.has(d.name) || (p.add(d.name), l.push(d));
});
}
}
return { inputs: r, outputs: e, usedNodes: n, missingInputs: s, dynamicNode: a, syncInputs: i };
}
function NT(r, e) {
let { usedNodes: t10, inputs: o } = e, n = Object.keys(o).map((g) => Nr(g)[0]).map((g) => r.nodes[g]), s = r.initNodes || [], a = (g) => t10.has(typeof g == "string" ? g : g.name);
function i(g) {
return [...new Map(g.map((x) => [x.name, x])).values()];
}
let p = i([...n, ...r.weights, ...s]).filter(a), u = i([...p, ...Object.values(r.nodes)]).filter(a), c = new Map(u.map((g) => [g.name, g])), l = {};
for (let g of u) {
l[g.name] = l[g.name] || 0;
for (let x of g.children)
a(x) || (l[x.name] = Number.POSITIVE_INFINITY), l[x.name] = (l[x.name] || 0) + 1;
}
let m = Object.entries(l).filter(([, g]) => g === 0).map(([g]) => g), d = [...m];
for (; m.length > 0; ) {
let g = m.pop(), x = c.get(g);
for (let b of x.children.filter(a))
--l[b.name] === 0 && (d.push(b.name), m.push(b.name));
}
let f = d.map((g) => c.get(g)), h = A5(f, p);
return F5(h, p), h;
}
function A5(r, e) {
let t10 = new Map(r.map((a) => [a.name, a])), o = e.map((a) => a.name), n = new Set(o);
for (; o.length > 0; ) {
let a = o.pop(), i = t10.get(a);
for (let p of i.children)
!t10.has(p.name) || n.has(p.name) || (n.add(p.name), o.push(p.name));
}
return r.filter((a) => n.has(a.name));
}
var hc = class extends Error {
constructor(e) {
super(`NodesExecutionOrderError: ${e}`);
}
};
function F5(r, e) {
let t10 = new Map(r.map((i, p) => [i.name, p])), o = new Set(e.map((i) => i.name)), n = (i) => o.has(typeof i == "string" ? i : i.name), s = new Set(r.map((i) => i.name)), a = (i) => s.has(typeof i == "string" ? i : i.name);
for (let i of r) {
for (let p of i.children.filter(a)) {
if (!t10.has(p.name))
throw new hc(`Child ${p.name} of node ${i.name} is unreachable.`);
if (t10.get(i.name) > t10.get(p.name))
throw new hc(`Node ${i.name} is scheduled to run after its child ${p.name}.`);
}
if (!n(i))
for (let p of i.inputs) {
if (!t10.has(p.name))
throw new hc(`Input ${p.name} of node ${i.name} is unreachable.`);
if (t10.get(p.name) > t10.get(i.name))
throw new hc(`Node ${i.name} is scheduled to run before its input ${p.name}.`);
}
}
}
function TT(r) {
let e = new Map(r.map((i, p) => [i.name, p])), t10 = Number.MAX_SAFE_INTEGER, o = r.map((i, p) => cu(i) ? t10 : p), n = (i) => {
let p = o[e.get(i.name)];
return p == null ? -1 : p;
}, s = r.map((i, p) => i.children.map(n).reduce((u, c) => Math.max(u, c), o[p])), a = /* @__PURE__ */ new Map();
for (let i = 0; i < r.length; ++i) {
let p = s[i];
if (p === t10)
continue;
let u = r[i], c = r[p];
a.has(c.name) || a.set(c.name, []), a.get(c.name).push(u);
}
return a;
}
var P5 = /* @__PURE__ */ new Set(["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"]);
var O5 = /* @__PURE__ */ new Set(["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"]);
var M5 = /* @__PURE__ */ new Set(["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"]);
function cu(r) {
return P5.has(r.op);
}
function L5(r) {
return O5.has(r.op);
}
function B5(r) {
return M5.has(r.op);
}
var op = class {
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 t10 = Object.keys(e).map((o) => e[o].map((n) => n.id));
this._weightIds = [].concat(...t10), 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 t10 = e.signatureKey || e.name;
return e.defaultOutput ? `${t10}:${e.defaultOutput}` : t10;
});
}
get functions() {
return Object.keys(this._functions).reduce((e, t10) => (e[t10] = this._functions[t10].signature, e), {});
}
constructor(e, t10) {
this.graph = e, this.parent = t10, this.compiledMap = /* @__PURE__ */ new Map(), this.parseNodeNameCache = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPARATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.keepIntermediateTensors = false, this._outputs = e.outputs, this._inputs = e.inputs, this._initNodes = e.initNodes, this._signature = e.signature, this._functions = e.functions, e.functions != null && Object.keys(e.functions).forEach((o) => {
this._functionExecutorMap[o] = new op(e.functions[o], this);
});
}
getCompilationKey(e, t10) {
let o = e.map((s) => s.name).sort(), n = t10.map((s) => s.name).sort();
return o.join(this.SEPARATOR) + "--" + n.join(this.SEPARATOR);
}
compile(e, t10) {
let o = CS(e, t10, this.weightMap, this._initNodes), { missingInputs: n, dynamicNode: s, syncInputs: a } = o;
if (s != null)
throw new Error(`This execution contains the node '${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 (n.length > 0) {
let u = t10.map((l) => l.name), c = Object.keys(e);
throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`);
}
let i = NT(this.graph, o), p = TT(i);
return { orderedNodes: i, nodeLiveUntilMap: p };
}
cloneAndKeepTensor(e) {
if (e == null)
return null;
let t10 = e.clone();
return Er(t10), t10;
}
cloneTensorList(e) {
return e ? e.map((o) => this.cloneAndKeepTensor(o)) : null;
}
cloneTensorMap(e) {
return Object.fromEntries(Object.entries(e).map(([t10, o]) => [t10, this.cloneTensorList(o)]));
}
execute(e, t10) {
this.disposeIntermediateTensors(), e = this.mapInputs(e);
let o = Object.keys(e).sort();
this.checkInputs(e), this.checkInputShapeAndType(e), t10 = this.mapOutputs(t10), this.checkOutputs(t10);
let n = o.map((m) => this.graph.nodes[Nr(m)[0]]), s = t10.map((m) => Nr(m)[0]), a = new Set(s), i = s.map((m) => this.graph.nodes[m]);
i.length === 0 && (i = this._outputs);
let p = this.getCompilationKey(n, i), u = this.compiledMap.get(p);
u == null && (u = this.compile(e, i), this.compiledMap.set(p, u));
try {
this.keepIntermediateTensors = P().getBool("KEEP_INTERMEDIATE_TENSORS");
} catch (m) {
this.keepIntermediateTensors = false, console.warn(m.message);
}
let c = {}, l = {};
return De(() => {
let m = new Pl(this.weightMap, c, l, this.functionExecutorMap, this.parseNodeNameCache), d = Object.assign({}, this.weightMap);
this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap)), Object.keys(e).forEach((x) => {
let [b, w] = Nr(x, m), S = [];
S[w] = e[x], d[b] = S, this.keepIntermediateTensors && (this.clonedTensorsMap[b] = this.cloneTensorList(S));
});
let f = this.getFrozenTensorIds(d), { orderedNodes: h, nodeLiveUntilMap: g } = u;
for (let x of h) {
if (d[x.name])
continue;
let b = bS(x, d, m, this._resourceManager);
if (y.isPromise(b))
throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);
d[x.name] = b, this.keepIntermediateTensors && (this.clonedTensorsMap[x.name] = this.cloneTensorList(b)), this.checkTensorForDisposalWithNodeLiveUntilInfo(x, d, m, f, a, g.get(x.name));
}
return this.parent == null && m.dispose(f), t10.map((x) => Bt(x, d, m));
});
}
getFrozenTensorIds(e) {
let t10 = [].concat.apply([], Object.keys(e).map((o) => e[o]).map((o) => o.map((n) => n.id)));
return new Set(t10);
}
checkTensorForDisposal(e, t10, o, n, s, a, i) {
if (!(cu(t10) || a.has(e))) {
for (let p of o[e])
p != null && (i[p.id] = (i[p.id] || 0) + t10.children.length);
for (let p of t10.inputs) {
if (cu(p))
continue;
let u = Qw(p.name, o, n);
if (u != null)
for (let c of u) {
if (!c || c.kept || s.has(c.id))
continue;
let l = i[c.id];
l === 1 ? (c.dispose(), delete i[c.id]) : l != null && i[c.id]--;
}
}
}
}
checkTensorForDisposalWithNodeLiveUntilInfo(e, t10, o, n, s, a) {
function i(p) {
return cu(p) || s.has(p.name);
}
if (!(cu(e) || a == null))
for (let p of a) {
if (i(p))
continue;
let u = Qw(p.name, t10, o);
for (let c of u)
!c || c.kept || n.has(c.id) || c.dispose();
}
}
async executeAsync(e, t10) {
return this._executeAsync(e, t10);
}
disposeIntermediateTensors() {
this.clonedTensorsMap && (Object.values(this.clonedTensorsMap).forEach((e) => {
for (let t10 of e)
t10 && !t10.isDisposed && t10.dispose();
}), this.clonedTensorsMap = null);
}
getIntermediateTensors() {
return this.clonedTensorsMap;
}
async _executeAsync(e, t10, o = false, n = {}, s = {}) {
this.disposeIntermediateTensors(), o || (e = this.mapInputs(e), this.checkInputs(e), this.checkInputShapeAndType(e), t10 = this.mapOutputs(t10), this.checkOutputs(t10));
try {
this.keepIntermediateTensors = P().getBool("KEEP_INTERMEDIATE_TENSORS");
} catch (m) {
this.keepIntermediateTensors = false, console.warn(m.message);
}
let a = new Pl(this.weightMap, n, s, this.functionExecutorMap, this.parseNodeNameCache);
this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap));
let i = await this.executeWithControlFlow(e, a, t10, o), p = t10.map((m) => Bt(m, i, a)), u = p.map((m) => m.id), c = Object.keys(e).map((m) => e[m].id), l = /* @__PURE__ */ new Set([...u, ...c, ...this.weightIds]);
return Object.values(i).forEach((m) => {
m.forEach((d) => {
d && !d.isDisposed && !l.has(d.id) && d.dispose();
});
}), this.parent == null && a.dispose(l), p;
}
async executeFunctionAsync(e, t10, o) {
let n = e.reduce((s, a, i) => (s[this.inputs[i].name] = a, s), {});
return this._executeAsync(n, this.outputNodes, true, t10, o);
}
async executeWithControlFlow(e, t10, o, n) {
let s = Object.keys(e), a = s.map((S) => this.graph.nodes[Nr(S)[0]]), i = o.map((S) => Nr(S)[0]), p = new Set(i), u = i.map((S) => this.graph.nodes[S]);
u.length === 0 && (u = this._outputs);
let { usedNodes: c, missingInputs: l, dynamicNode: m, syncInputs: d } = CS(e, u, this.weightMap, this._initNodes), f = [...a, ...this.graph.weights, ...this._initNodes || []].map((S) => ({ node: S, contexts: t10.currentContext })), h = Object.assign({}, this.weightMap);
Object.keys(e).forEach((S) => {
let [k, _] = Nr(S), E = [];
E[_] = e[S], h[k] = E;
});
let g = {}, x = this.getFrozenTensorIds(h), b = {};
for (; f.length > 0; ) {
let S = this.processStack(a, f, t10, h, b, x, p, g, c);
await Promise.all(S);
}
m == null && !n && console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");
let w = u.filter((S) => !cu(S) && !Bt(S.name, h, t10)).map((S) => S.name);
if (w.length > 0) {
let S = "";
throw m != null && (S = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`), new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`);
}
return h;
}
processStack(e, t10, o, n, s, a, i, p, u) {
let c = [];
for (; t10.length > 0; ) {
let l = t10.pop();
o.currentContext = l.contexts;
let m = "";
if (l.node.op === "Enter" && I("isConstant", l.node, n, o) && ([m] = Ds(l.node.name, o)), n[l.node.name] == null) {
let d = bS(l.node, n, o, this._resourceManager);
m || ([m] = Ds(l.node.name, o));
let f = o.currentContext;
y.isPromise(d) ? c.push(d.then((h) => (n[m] = h, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(h)), o.currentContext = f, this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t10, o, n, s, u), h))) : (n[m] = d, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(d)), this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t10, o, n, s, u));
} else
this.processChildNodes(l.node, t10, o, n, s, u);
}
return c;
}
processChildNodes(e, t10, o, n, s, a) {
e.children.forEach((i) => {
let [p] = Ds(i.name, o);
s[p] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!Bt(u, n, o)) && (s[p] = true, t10.push({ contexts: o.currentContext, node: i })) : i.inputNames.every((u) => !!Bt(u, n, o)) && (s[p] = true, t10.push({ contexts: o.currentContext, node: i })));
});
}
dispose() {
Object.keys(this.weightMap).forEach((e) => this.weightMap[e].forEach((t10) => t10.dispose()));
}
checkInputShapeAndType(e) {
Object.keys(e).forEach((t10) => {
let o = e[t10], [n] = Nr(t10), s = this.graph.nodes[n];
if (s.attrParams.shape && s.attrParams.shape.value) {
let a = s.attrParams.shape.value, i = a.length === o.shape.length && o.shape.every((p, u) => a[u] === -1 || a[u] === p);
y.assert(i, () => `The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`);
}
s.attrParams.dtype && s.attrParams.dtype.value && y.assert(o.dtype === s.attrParams.dtype.value, () => `The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`);
});
}
mapInputs(e) {
var t10, o;
let n = {};
for (let s in e) {
let a = (o = (t10 = this._signature) === null || t10 === void 0 ? void 0 : t10.inputs) === null || o === void 0 ? void 0 : o[s];
a != null ? n[a.name] = e[s] : n[s] = e[s];
}
return n;
}
checkInputs(e) {
let t10 = Object.keys(e).filter((o) => {
let [n] = Nr(o);
return this.graph.nodes[n] == null;
});
if (t10.length > 0)
throw new Error(`The dict provided in model.execute(dict) has keys: [${t10}] that are not part of graph`);
}
mapOutputs(e) {
return e.map((t10) => {
var o, n;
let s = (n = (o = this._signature) === null || o === void 0 ? void 0 : o.outputs) === null || n === void 0 ? void 0 : n[t10];
return s != null ? s.name : t10;
}, {});
}
checkOutputs(e) {
e.forEach((t10) => {
let [o] = Nr(t10);
if (!this.graph.nodes[o])
throw new Error(`The output '${t10}' is not found in the graph`);
});
}
};
var Sf = class {
constructor(e = {}, t10 = {}) {
this.hashTableNameToHandle = e, this.hashTableMap = t10;
}
addHashTable(e, t10) {
this.hashTableNameToHandle[e] = t10.handle, this.hashTableMap[t10.id] = t10;
}
getHashTableHandleByName(e) {
return this.hashTableNameToHandle[e];
}
getHashTableById(e) {
return this.hashTableMap[e];
}
dispose() {
for (let e in this.hashTableMap)
this.hashTableMap[e].clearAndClose(), delete this.hashTableMap[e];
for (let e in this.hashTableNameToHandle)
this.hashTableNameToHandle[e].dispose(), delete this.hashTableNameToHandle[e];
}
};
var z5 = "?tfjs-format=file";
var V5 = "model.json";
var Ol = class {
get modelVersion() {
return this.version;
}
get inputNodes() {
return this.executor.inputNodes;
}
get outputNodes() {
return this.executor.outputNodes;
}
get inputs() {
return this.executor.inputs;
}
get outputs() {
return this.executor.outputs;
}
get weights() {
return this.executor.weightMap;
}
get metadata() {
return this.artifacts.userDefinedMetadata;
}
get modelSignature() {
return this.signature;
}
get modelStructuredOutputKeys() {
return this.structuredOutputKeys;
}
constructor(e, t10 = {}, o = pi) {
this.modelUrl = e, this.loadOptions = t10, this.version = "n/a", this.io = o, t10 == null && (this.loadOptions = {}), this.resourceManager = new Sf();
}
findIOHandler() {
let e = this.modelUrl;
if (e.load != null)
this.handler = e;
else if (this.loadOptions.requestInit != null)
this.handler = this.io.browserHTTPRequest(e, this.loadOptions);
else {
let t10 = this.io.getLoadHandlers(e, this.loadOptions);
if (t10.length === 0)
t10.push(this.io.browserHTTPRequest(e, this.loadOptions));
else if (t10.length > 1)
throw new Error(`Found more than one (${t10.length}) load handlers for URL '${[e]}'`);
this.handler = t10[0];
}
}
load() {
if (this.findIOHandler(), this.handler.load == null)
throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
let e = this.handler.load();
return y.isPromise(e) ? e.then((t10) => this.loadSync(t10)) : this.loadSync(e);
}
loadSync(e) {
this.artifacts = e;
let t10 = this.artifacts.modelTopology, o = this.artifacts.signature;
if (this.artifacts.userDefinedMetadata != null) {
let s = this.artifacts.userDefinedMetadata;
s.signature != null && (o = s.signature), s.structuredOutputKeys != null && (this.structuredOutputKeys = s.structuredOutputKeys);
}
this.signature = o, this.version = `${t10.versions.producer}.${t10.versions.minConsumer}`;
let n = this.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
if (this.executor = new op(Fl.Instance.transformGraph(t10, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
let s = Fl.Instance.transformGraph(e.modelInitializer);
this.initializer = new op(s), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializerSignature = e.initializerSignature;
}
return true;
}
async save(e, t10) {
if (typeof e == "string") {
let o = this.io.getSaveHandlers(e);
if (o.length === 0)
throw new Error(`Cannot find any save handlers for URL '${e}'`);
if (o.length > 1)
throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);
e = o[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);
}
addStructuredOutputNames(e) {
if (this.structuredOutputKeys) {
let t10 = e instanceof pt ? [e] : e, o = {};
return t10.forEach((n, s) => o[this.structuredOutputKeys[s]] = n), o;
}
return e;
}
predict(e, t10) {
let o = this.execute(e, this.outputNodes);
return this.addStructuredOutputNames(o);
}
async predictAsync(e, t10) {
let o = await this.executeAsync(e, this.outputNodes);
return this.addStructuredOutputNames(o);
}
normalizeInputs(e) {
var t10;
if (!(e instanceof pt) && !Array.isArray(e)) {
let s = (t10 = this.signature) === null || t10 === void 0 ? void 0 : t10.inputs;
if (s != null)
for (let a in s) {
let i = s[a];
i.resourceId != null && (e[a] = this.resourceIdToCapturedInput[i.resourceId]);
}
return e;
}
e = Array.isArray(e) ? e : [e];
let o = Object.keys(this.resourceIdToCapturedInput).length;
if (e.length + o !== this.inputNodes.length)
throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length - o} non-resource placeholders, while there are ${e.length} input tensors provided.`);
let n = 0;
return this.inputNodes.reduce((s, a) => {
var i, p, u;
let c = (u = (p = (i = this.signature) === null || i === void 0 ? void 0 : i.inputs) === null || p === void 0 ? void 0 : p[a]) === null || u === void 0 ? void 0 : u.resourceId;
return c != null ? s[a] = this.resourceIdToCapturedInput[c] : s[a] = e[n++], s;
}, {});
}
normalizeOutputs(e) {
return e = e || this.outputNodes, Array.isArray(e) ? e : [e];
}
executeInitializerGraph() {
return this.initializer == null ? [] : this.initializerSignature == null ? this.initializer.execute({}, []) : this.initializer.execute({}, Object.keys(this.initializerSignature.outputs));
}
async executeInitializerGraphAsync() {
return this.initializer == null ? [] : this.initializerSignature == null ? this.initializer.executeAsync({}, []) : this.initializer.executeAsync({}, Object.keys(this.initializerSignature.outputs));
}
setResourceIdToCapturedInput(e) {
if (this.resourceIdToCapturedInput = {}, this.initializerSignature) {
let t10 = this.initializerSignature.outputs, o = Object.keys(t10);
for (let n = 0; n < o.length; n++) {
let s = o[n], a = t10[s];
this.resourceIdToCapturedInput[a.resourceId] = e[n];
}
}
}
execute(e, t10) {
this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(this.executeInitializerGraph()), e = this.normalizeInputs(e), t10 = this.normalizeOutputs(t10);
let o = this.executor.execute(e, t10);
return o.length > 1 ? o : o[0];
}
async executeAsync(e, t10) {
this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()), e = this.normalizeInputs(e), t10 = this.normalizeOutputs(t10);
let o = await this.executor.executeAsync(e, t10);
return o.length > 1 ? o : o[0];
}
getIntermediateTensors() {
return this.executor.getIntermediateTensors();
}
disposeIntermediateTensors() {
this.executor.disposeIntermediateTensors();
}
convertTensorMapToTensorsMap(e) {
return Object.keys(e).reduce((t10, o) => (t10[o] = [e[o]], t10), {});
}
dispose() {
this.executor.dispose(), this.initializer && (this.initializer.dispose(), this.resourceIdToCapturedInput && Ot(this.resourceIdToCapturedInput)), this.resourceManager.dispose();
}
};
async function W5(r, e = {}, t10 = pi) {
if (r == null)
throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
e == null && (e = {}), e.fromTFHub && typeof r == "string" && (r = G5(r));
let o = new Ol(r, e, t10);
return await o.load(), o;
}
function U5(r) {
if (r == null)
throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");
let e;
if (r instanceof Array) {
let [o, n] = r;
if (!o)
throw new Error("modelJSON must be the first element of the array");
if (!n || !(n instanceof ArrayBuffer))
throw new Error("An ArrayBuffer of weights must be the second element of the array");
if (!("modelTopology" in o))
throw new Error("Model JSON is missing 'modelTopology'");
if (!("weightsManifest" in o))
throw new Error("Model JSON is missing 'weightsManifest'");
let s = pi.getWeightSpecs(o.weightsManifest), a = pi.getModelArtifactsForJSONSync(o, s, n);
e = pi.fromMemorySync(a);
} else if ("load" in r)
e = r;
else if ("modelTopology" in r && "weightSpecs" in r && "weightData" in r)
e = pi.fromMemorySync(r);
else
throw new Error("Unknown model format");
let t10 = new Ol(e);
return t10.load(), t10;
}
function G5(r) {
return r.endsWith("/") || (r = r + "/"), `${r}${V5}${z5}`;
}
var H5 = "4.5.0";
function Y(r, e) {
Array.isArray(r) || (r = [r]), r.forEach((t10) => {
t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the CPU backend.`);
});
}
var K5 = Wt.whereImpl;
var lu = class extends ro {
nextDataId() {
return lu.nextDataId++;
}
constructor() {
super(), this.blockSize = 48, this.firstUse = true, this.data = new Lo(this, ur());
}
write(e, t10, o) {
this.firstUse && (this.firstUse = false, P().get("IS_NODE") && C.warn(`
============================
Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details.
============================`));
let n = { id: this.nextDataId() };
return this.data.set(n, { values: e, dtype: o, refCount: 1 }), n;
}
makeTensorInfo(e, t10, o) {
let n;
if (t10 === "string" && o != null && o.length > 0 && y.isString(o[0])) {
let s = o.map((a) => y.encodeString(a));
n = this.write(s, e, t10);
} else
n = this.write(o, e, t10);
return { dataId: n, shape: e, dtype: t10 };
}
refCount(e) {
return this.data.has(e) ? this.data.get(e).refCount : 0;
}
incRef(e) {
let t10 = this.data.get(e);
t10.refCount++;
}
decRef(e) {
if (this.data.has(e)) {
let t10 = this.data.get(e);
t10.refCount--;
}
}
move(e, t10, o, n, s) {
this.data.set(e, { values: t10, dtype: n, refCount: s });
}
numDataIds() {
return this.data.numDataIds();
}
async read(e) {
return this.readSync(e);
}
readSync(e) {
let { dtype: t10, complexTensorInfos: o } = this.data.get(e);
if (t10 === "complex64") {
let n = this.readSync(o.real.dataId), s = this.readSync(o.imag.dataId);
return C.mergeRealAndImagArrays(n, s);
}
return y.convertBackendValuesAndArrayBuffer(this.data.get(e).values, t10);
}
bufferSync(e) {
let t10 = this.readSync(e.dataId);
if (e.dtype === "string")
try {
let o = t10.map((n) => y.decodeString(n));
return me(e.shape, e.dtype, o);
} catch (o) {
throw new Error("Failed to decode encoded string bytes into utf-8");
}
return me(e.shape, e.dtype, t10);
}
makeOutput(e, t10, o) {
return ur().makeTensorFromTensorInfo(this.makeTensorInfo(t10, o, e), this);
}
disposeData(e, t10 = false) {
if (this.data.has(e)) {
if (this.data.get(e).refCount--, !t10 && this.data.get(e).refCount > 0)
return false;
let { complexTensorInfos: o } = this.data.get(e);
o != null && (this.disposeData(o.real.dataId, true), this.disposeData(o.imag.dataId, true)), this.data.delete(e);
}
return true;
}
disposeIntermediateTensorInfo(e) {
this.disposeData(e.dataId);
}
async time(e) {
let t10 = y.now();
return e(), { kernelMs: y.now() - t10 };
}
memory() {
return { unreliable: true, reasons: ["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."] };
}
where(e) {
Y([e], "where");
let t10 = this.readSync(e.dataId);
return K5(e.shape, t10);
}
dispose() {
}
floatPrecision() {
return 32;
}
epsilon() {
return super.epsilon();
}
};
lu.nextDataId = 0;
var Sc = {};
He(Sc, { addImpl: () => IS, bincountImpl: () => yc, bincountReduceImpl: () => If, bitwiseAndImpl: () => vS, castImpl: () => SS, ceilImpl: () => kS, concatImpl: () => np, equalImpl: () => NS, expImpl: () => _S, expm1Impl: () => ES, floorDivImpl: () => DS, floorImpl: () => RS, gatherNdImpl: () => vf, gatherV2Impl: () => kf, greaterEqualImpl: () => FS, greaterImpl: () => AS, lessEqualImpl: () => OS, lessImpl: () => PS, linSpaceImpl: () => Nf, logImpl: () => MS, maxImpl: () => Tf, maximumImpl: () => LS, minimumImpl: () => BS, multiplyImpl: () => Ml, negImpl: () => zS, notEqualImpl: () => VS, prodImpl: () => WS, raggedGatherImpl: () => _f, raggedRangeImpl: () => $f, raggedTensorToTensorImpl: () => Ef, rangeImpl: () => ap, rsqrtImpl: () => US, scatterImpl: () => Fs, sigmoidImpl: () => n_, simpleAbsImpl: () => wS, sliceImpl: () => ip, sparseFillEmptyRowsImpl: () => Rf, sparseReshapeImpl: () => Df, sparseSegmentReductionImpl: () => wc, sqrtImpl: () => i_, squaredDifferenceImpl: () => HS, staticRegexReplaceImpl: () => KS, stridedSliceImpl: () => Af, stringNGramsImpl: () => up, stringSplitImpl: () => pp, stringToHashBucketFastImpl: () => cp, subImpl: () => jS, tileImpl: () => Ff, topKImpl: () => Pf, transposeImpl: () => bc, uniqueImpl: () => lp });
function wS(r) {
let e = new Float32Array(r.length);
for (let t10 = 0; t10 < r.length; ++t10)
e[t10] = Math.abs(r[t10]);
return e;
}
var q5 = (r) => {
let { x: e } = r.inputs, t10 = r.backend;
Y(e, "abs");
let o = new Float32Array(y.sizeFromShape(e.shape)), n = t10.data.get(e.dataId).values;
return o = wS(n), t10.makeOutput(o, e.shape, e.dtype);
};
var _T = { kernelName: Gs, backendName: "cpu", kernelFunc: q5 };
function ze(r) {
return (e, t10, o, n, s) => {
let a = C.assertAndGetBroadcastShape(e, t10), i = a.length, p = y.computeStrides(a), u = y.sizeFromShape(a), c = y.getTypedArrayFromDType(s, u), l = e.length, m = t10.length, d = y.computeStrides(e), f = y.computeStrides(t10), h = C.getBroadcastDims(e, a), g = C.getBroadcastDims(t10, a);
if (h.length + g.length === 0)
for (let x = 0; x < c.length; ++x)
c[x] = r(o[x % o.length], n[x % n.length]);
else
for (let x = 0; x < c.length; ++x) {
let b = y.indexToLoc(x, i, p), w = b.slice(-l);
h.forEach((E) => w[E] = 0);
let S = y.locToIndex(w, l, d), k = b.slice(-m);
g.forEach((E) => k[E] = 0);
let _ = y.locToIndex(k, m, f);
c[x] = r(o[S], n[_]);
}
return [c, a];
};
}
function Kt(r) {
let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, i = t10.makeTensorInfo(o.shape, "complex64"), p = t10.data.get(i.dataId);
return p.complexTensorInfos = { real: t10.makeTensorInfo(o.shape, "float32", s), imag: t10.makeTensorInfo(n.shape, "float32", a) }, i;
}
var $T = { kernelName: Ti, backendName: "cpu", kernelFunc: Kt };
function gc(r, e, t10 = "float32") {
if (t10 === "complex64") {
let n = gc(r, e, "float32"), s = gc(r, e, "float32");
return Kt({ inputs: { real: n, imag: s }, backend: r });
}
let o = y.makeZerosTypedArray(y.sizeFromShape(e), t10);
return r.makeTensorInfo(e, t10, o);
}
function lr(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
return t10.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
var ET = { kernelName: xo, backendName: "cpu", kernelFunc: lr };
function To(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.data.get(o.dataId).complexTensorInfos.real, s = t10.data.get(n.dataId).values;
return t10.makeTensorInfo(n.shape, n.dtype, s);
}
var RT = { kernelName: zi, backendName: "cpu", kernelFunc: To };
function SS(r, e, t10, o) {
if (o === "int32") {
let n = Int32Array.from(r);
return [e, "int32", n];
}
if (o === "bool") {
let n = y.toTypedArray([0], t10), [s, a] = ze((i, p) => i !== p ? 1 : 0)(e, [], r, n, "bool");
return [a, "bool", s];
}
throw new Error(`Error in Cast: failed to cast ${t10} to ${o}`);
}
function _o(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o;
if (s === "complex64") {
if (n.dtype === "complex64")
return lr({ inputs: { x: n }, backend: t10 });
let c = gc(t10, n.shape, n.dtype), l = _o({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), m = Kt({ inputs: { real: l, imag: c }, backend: t10 });
return t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), m;
}
if (n.dtype === "complex64") {
let c = To({ inputs: { input: n }, backend: t10 }), l = _o({ inputs: { x: c }, backend: t10, attrs: { dtype: s } });
return t10.disposeIntermediateTensorInfo(c), l;
}
if (!y.hasEncodingLoss(n.dtype, s)) {
let c = lr({ inputs: { x: n }, backend: t10 });
return { dataId: c.dataId, shape: c.shape, dtype: s };
}
let a = t10.data.get(n.dataId).values, [i, p, u] = SS(a, n.shape, n.dtype, s);
return t10.makeTensorInfo(i, p, u);
}
var DT = { kernelName: ho, backendName: "cpu", kernelFunc: _o };
function je(r, e, t10, o) {
return t10 == null ? ({ inputs: n, backend: s }) => {
let { a, b: i } = n, p = s;
Y([a, i], r);
let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = a.dtype === "string" ? C.fromUint8ToStringArray(u) : u, m = a.dtype === "string" ? C.fromUint8ToStringArray(c) : c, d = o || a.dtype, [f, h] = e(a.shape, i.shape, l, m, d);
return p.makeTensorInfo(h, d, f);
} : ({ inputs: n, backend: s }) => {
let { a, b: i } = n, p = s;
if (a.dtype === "complex64" || i.dtype === "complex64") {
let u = _o({ inputs: { x: a }, backend: p, attrs: { dtype: "complex64" } }), c = p.data.get(u.dataId), l = c.complexTensorInfos.real, m = c.complexTensorInfos.imag, d = p.data.get(l.dataId).values, f = p.data.get(m.dataId).values, h = _o({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), x = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, w = p.data.get(x.dataId).values, S = p.data.get(b.dataId).values, [k, _, E] = t10(a.shape, i.shape, d, f, w, S), R = p.makeTensorInfo(E, "float32", k), D = p.makeTensorInfo(E, "float32", _), F = Kt({ inputs: { real: R, imag: D }, backend: p });
return p.disposeIntermediateTensorInfo(u), p.disposeIntermediateTensorInfo(h), p.disposeIntermediateTensorInfo(R), p.disposeIntermediateTensorInfo(D), F;
} else {
let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = o || a.dtype, [m, d] = e(a.shape, i.shape, u, c, l);
return p.makeTensorInfo(d, l, m);
}
};
}
function xc(r) {
return (e, t10, o, n, s, a) => {
let i = C.assertAndGetBroadcastShape(e, t10), p = y.sizeFromShape(i), u = i.length, c = y.computeStrides(i), l = y.getTypedArrayFromDType("float32", p), m = y.getTypedArrayFromDType("float32", p), d = C.getBroadcastDims(e, i), f = C.getBroadcastDims(t10, i), h = C.mergeRealAndImagArrays(o, n), g = C.mergeRealAndImagArrays(s, a), x = e.length, b = y.computeStrides(e), w = t10.length, S = y.computeStrides(t10);
if (d.length + f.length === 0)
for (let k = 0; k < l.length; k++) {
let _ = k % h.length, E = k % g.length, R = r(h[_ * 2], h[_ * 2 + 1], g[E * 2], g[E * 2 + 1]);
l[k] = R.real, m[k] = R.imag;
}
else
for (let k = 0; k < l.length; k++) {
let _ = y.indexToLoc(k, u, c), E = _.slice(-x);
d.forEach((M) => E[M] = 0);
let R = y.locToIndex(E, x, b), D = _.slice(-w);
f.forEach((M) => D[M] = 0);
let F = y.locToIndex(D, w, S), O = r(h[R * 2], h[R * 2 + 1], g[F * 2], g[F * 2 + 1]);
l[k] = O.real, m[k] = O.imag;
}
return [l, m, i];
};
}
var IS = ze((r, e) => r + e);
var j5 = xc((r, e, t10, o) => ({ real: r + t10, imag: e + o }));
var _a = je(no, IS, j5);
var AT = { kernelName: no, backendName: "cpu", kernelFunc: _a };
function yc(r, e, t10, o, n) {
let s = y.sizeFromShape(o), a = y.makeZerosTypedArray(n, t10);
for (let i = 0; i < r.length; i++) {
let p = r[i];
if (p < 0)
throw new Error("Input x must be non-negative!");
p >= n || (s > 0 ? a[p] += e[i] : a[p] += 1);
}
return a;
}
function If(r, e, t10, o = false) {
let n = r.shape[0], s = r.shape[1], a = me([n, t10], e.dtype);
for (let i = 0; i < n; i++)
for (let p = 0; p < s; p++) {
let u = r.get(i, p);
if (u < 0)
throw new Error("Input x must be non-negative!");
u >= t10 || (o ? a.set(1, i, u) : e.size > 0 ? a.set(a.get(i, u) + e.get(i, p), i, u) : a.set(a.get(i, u) + 1, i, u));
}
return a;
}
var vS = ze((r, e) => r & e);
var X5 = je(ml, vS);
var FT = { kernelName: ml, backendName: "cpu", kernelFunc: X5 };
function jt(r) {
return (e, t10, o) => {
let n = y.getArrayFromDType(t10, e.length);
for (let s = 0; s < e.length; ++s)
n[s] = r(e[s], o);
return n;
};
}
function Ie(r, e, t10) {
let o = jt(e);
return Dr(r, o, t10);
}
function Dr(r, e, t10) {
return ({ inputs: o, attrs: n, backend: s }) => {
let { x: a } = o;
Y(a, r);
let i = s, p = i.data.get(a.dataId).values, u;
if (a.dtype === "string") {
if (!Array.isArray(p))
throw new Error("String tensor's value was not an instance of Array");
u = C.fromUint8ToStringArray(p);
} else
u = p;
let c = t10 || a.dtype, l = e(u, c, n);
return i.makeTensorInfo(a.shape, c, l);
};
}
var kS = jt((r) => Math.ceil(r));
var Y5 = Dr(Jo, kS);
var PT = { kernelName: Jo, backendName: "cpu", kernelFunc: Y5 };
function np(r, e, t10, o) {
let n = y.getArrayFromDType(t10, y.sizeFromShape(e));
if (o && t10 !== "string") {
let s = 0;
r.forEach((a) => {
let i = y.sizeFromShape(a.shape);
n.set(a.vals, s), s += i;
});
} else {
let s = 0;
r.forEach((a) => {
let i = t10 === "string" ? C.fromUint8ToStringArray(a.vals) : a.vals, p = 0;
for (let u = 0; u < a.shape[0]; ++u) {
let c = u * e[1] + s;
for (let l = 0; l < a.shape[1]; ++l)
n[c + l] = i[p++];
}
s += a.shape[1];
});
}
return n;
}
var NS = ze((r, e) => r === e ? 1 : 0);
var TS = je(hn, NS, null, "bool");
var OT = { kernelName: hn, backendName: "cpu", kernelFunc: TS };
var _S = jt((r) => Math.exp(r));
var $S = Dr(gn, _S, "float32");
var MT = { kernelName: gn, backendName: "cpu", kernelFunc: $S };
var ES = jt((r) => Math.expm1(r));
var Q5 = Dr(xn, ES);
var LT = { kernelName: xn, backendName: "cpu", kernelFunc: Q5 };
var RS = jt((r) => Math.floor(r));
var Z5 = Dr(bn, RS);
var BT = { kernelName: bn, backendName: "cpu", kernelFunc: Z5 };
var DS = ze((r, e) => Math.floor(r / e));
var J5 = je(Cn, DS, null, "int32");
var zT = { kernelName: Cn, backendName: "cpu", kernelFunc: J5 };
function vf(r, e, t10, o, n, s, a, i, p) {
let u = me([o, s], t10);
for (let c = 0; c < o; c++) {
let l = [], m = 0;
for (let d = 0; d < n; d++) {
let f = r[c * n + d];
m += f * a[d], l.push(f);
}
if (m < 0 || m >= p / s)
throw new Error(`Invalid indices: ${l} does not index into ${i}`);
for (let d = 0; d < s; d++)
u.values[c * s + d] = e.get(...e.indexToLoc(m * s + d));
}
return u;
}
function kf(r, e, t10) {
let o = me(t10, r.dtype);
for (let n = 0; n < o.size; ++n) {
let a = o.indexToLoc(n).slice(), i = a[0], p = a[2], u = e.locToIndex([i, p]);
a[2] = e.values[u];
let c = r.locToIndex(a);
0 <= c && c < r.values.length && (o.values[n] = r.values[c]);
}
return o;
}
var AS = ze((r, e) => r > e ? 1 : 0);
var e8 = je(In, AS, null, "bool");
var VT = { kernelName: In, backendName: "cpu", kernelFunc: e8 };
var FS = ze((r, e) => r >= e ? 1 : 0);
var t8 = je(vn, FS, null, "bool");
var WT = { kernelName: vn, backendName: "cpu", kernelFunc: t8 };
var PS = ze((r, e) => r < e ? 1 : 0);
var r8 = je($n, PS, null, "bool");
var UT = { kernelName: $n, backendName: "cpu", kernelFunc: r8 };
var OS = ze((r, e) => r <= e ? 1 : 0);
var o8 = je(En, OS, null, "bool");
var GT = { kernelName: En, backendName: "cpu", kernelFunc: o8 };
function Nf(r, e, t10) {
let o = (e - r) / (t10 - 1), n = y.makeZerosTypedArray(t10, "float32");
n[0] = r;
for (let s = 1; s < n.length; s++)
n[s] = n[s - 1] + o;
return n;
}
var MS = jt((r) => Math.log(r));
var n8 = Dr(Dn, MS);
var HT = { kernelName: Dn, backendName: "cpu", kernelFunc: n8 };
function Tf(r, e, t10, o) {
let n = y.getTypedArrayFromDType(o, y.sizeFromShape(t10));
for (let s = 0; s < n.length; ++s) {
let a = s * e, i = r[a];
for (let p = 0; p < e; ++p) {
let u = r[a + p];
(Number.isNaN(u) || u > i) && (i = u);
}
n[s] = i;
}
return n;
}
var LS = ze((r, e) => Math.max(r, e));
var s8 = je(Bn, LS);
var KT = { kernelName: Bn, backendName: "cpu", kernelFunc: s8 };
var BS = ze((r, e) => Math.min(r, e));
var a8 = je(Un, BS);
var qT = { kernelName: Un, backendName: "cpu", kernelFunc: a8 };
var Ml = ze((r, e) => r * e);
var i8 = xc((r, e, t10, o) => ({ real: r * t10 - e * o, imag: r * o + e * t10 }));
var sp = je(Kn, Ml, i8);
var jT = { kernelName: Kn, backendName: "cpu", kernelFunc: sp };
function zS(r, e, t10) {
let o = y.createScalarValue(-1, t10);
return Ml([], e, o, r, t10);
}
function u8(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
Y(o, "neg");
let n = t10.data.get(o.dataId).values, [s, a] = zS(n, o.shape, o.dtype);
return t10.makeTensorInfo(a, o.dtype, s);
}
var XT = { kernelName: oa, backendName: "cpu", kernelFunc: u8 };
var VS = ze((r, e) => r !== e ? 1 : 0);
var p8 = je(qn, VS, null, "bool");
var YT = { kernelName: qn, backendName: "cpu", kernelFunc: p8 };
function bc(r, e, t10, o, n) {
let s = e.length, a = y.sizeFromShape(e), i = y.computeStrides(e), p = y.computeStrides(n), u = y.getTypedArrayFromDType(t10, y.sizeFromShape(n));
for (let c = 0; c < a; ++c) {
let l = y.indexToLoc(c, s, i), m = new Array(l.length);
for (let f = 0; f < m.length; f++)
m[f] = l[o[f]];
let d = y.locToIndex(m, s, p);
u[d] = r[c];
}
return u;
}
function St(r) {
let { inputs: e, attrs: t10, backend: o } = r, { x: n } = e, { perm: s } = t10;
Y(n, "transpose");
let a = n.shape.length, i = new Array(a);
for (let l = 0; l < i.length; l++)
i[l] = n.shape[s[l]];
let p = o.data.get(n.dataId).values, u = bc(p, n.shape, n.dtype, s, i);
return { dataId: o.write(u, i, n.dtype), shape: i, dtype: n.dtype };
}
var QT = { kernelName: ao, backendName: "cpu", kernelFunc: St };
function WS(r, e, t10, o) {
let [n, s] = C.computeOutAndReduceShapes(r, o), a = dt(e, "int32"), i = y.makeZerosTypedArray(y.sizeFromShape(n), a), p = y.sizeFromShape(s);
for (let u = 0; u < i.length; ++u) {
let c = u * p, l = 1;
for (let m = 0; m < p; ++m)
l *= t10[c + m];
i[u] = l;
}
return { outVals: i, outShape: n, outDtype: a };
}
function c8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
Y(n, "prod");
let i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = C.getAxesPermutation(p, i), c = p, l = n, m = [];
u != null && (l = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), m.push(l), c = C.getInnerMostAxes(c.length, i));
let d = t10.data.get(l.dataId).values, { outVals: f, outShape: h, outDtype: g } = WS(l.shape, l.dtype, d, c), x = h;
return a && (x = C.expandShapeToKeepDim(h, p)), m.forEach((b) => t10.disposeIntermediateTensorInfo(b)), t10.makeTensorInfo(x, g, f);
}
var ZT = { kernelName: es, backendName: "cpu", kernelFunc: c8 };
function l8(r, e, t10) {
r.forEach((o, n) => {
if (o < 0 || o >= t10) {
let s = y.indexToLoc(n, e.length, y.computeStrides(e)).join(",");
throw new Error(`indices[${s}] = ${o} is not in [0, ${t10})`);
}
});
}
function m8(r, e) {
for (let t10 = 0; t10 < r.length; ++t10) {
let o = r[t10], n = t10 === r.length - 1 ? e : r[t10 + 1].length;
if (o.length === 0)
throw new Error("Ragged splits may not be empty");
if (o[0] < 0)
throw new Error("Ragged splits must be non-negative");
if (o[o.length - 1] > n)
throw new Error("Ragged splits must not point past values");
for (let s = 1; s < o.length; ++s)
if (o[s - 1] > o[s])
throw new Error("Ragged splits must be sorted in ascending order");
}
}
function d8(r, e, t10, o) {
let n = [], s = 0, a = e.length - 1 + t10.length, i = new Array(a).fill(null).map(() => [0]);
m8(t10, o);
let p = 1;
for (let u = 0; u < e.length - 1; ++u) {
p *= e[u];
let c = e[u + 1];
for (let l = 1; l < p + 1; ++l)
i[u].push(l * c);
}
for (let u = 0; u < r.length; ++u) {
let c = r[u], l = r[u] + 1;
for (let m = 0; m < t10.length; ++m) {
let d = t10[m], f = m + e.length - 1;
if (f >= 0) {
let h = i[f], g = h[h.length - 1] - d[c];
for (let x = c; x < l; ++x)
i[f].push(d[x + 1] + g);
}
c = d[c], l = d[l];
}
l !== c && (n.push([c, l]), s += l - c);
}
return { outSplits: i, valueSlices: n, numValues: s };
}
function f8(r) {
let e = [];
for (let t10 = 0; t10 < r.length; ++t10) {
let o = r[t10].length, n = y.getArrayFromDType("int32", o);
e.push(n), r[t10].forEach((s, a) => n[a] = s);
}
return e;
}
function JT(r, e) {
let t10 = r.slice(0, e);
for (; t10.length < e; )
t10.push(1);
for (let o = e; o < r.length; o++)
t10[e - 1] *= r[o];
return t10;
}
function h8(r, e, t10, o, n, s) {
let a = JT(e, 2)[1], i = JT(s, 2)[1], p = 0;
for (let u of t10)
for (let c = u[0]; c < u[1]; ++c) {
for (let l = 0; l < o; ++l)
n[p * i + l] = r[c * a + l];
++p;
}
}
function g8(r, e, t10, o, n) {
let s = e.slice();
s[0] = n;
let a = y.getArrayFromDType(t10, y.sizeFromShape(s)), i = r.length, p = i === 0 ? 0 : i / e[0];
return h8(r, e, o, p, a, s), [a, s];
}
function _f(r, e, t10, o, n, s, a, i) {
if (r.length === 0)
throw new Error("paramsNestedSplits must be non empty");
if (e[0].length === 0)
throw new Error("Split tensors must not be scalars");
let p = e[0][0] - 1;
if (l8(s, a, p), o.length === 0)
throw new Error("params.rank must be nonzero");
let u = o[0], { outSplits: c, valueSlices: l, numValues: m } = d8(s, a, r, u), d = f8(c), f = g8(t10, o, n, l, m);
return [d, f[0], f[1]];
}
var e_ = 2147483647;
function $f(r, e, t10, o, n, s, a) {
if (e.length > 1)
throw new Error("starts must be a scalar or vector");
if (n.length > 1)
throw new Error("limits must be a scalar or vector");
if (a.length > 1)
throw new Error("deltas must be a scalar or vector");
let i = e.length === 0, p = n.length === 0, u = a.length === 0, c = [];
i || c.push(e[0]), p || c.push(n[0]), u || c.push(a[0]);
for (let g = 1; g < c.length; ++g)
if (c[g] !== c[g - 1])
throw new Error("starts, limits, and deltas must have the same shape");
let l = c.length === 0 ? 1 : c[0], m = y.getArrayFromDType("int32", l + 1);
m[0] = 0;
for (let g = 0; g < l; ++g) {
let x = i ? r[0] : r[g], b = p ? o[0] : o[g], w = u ? s[0] : s[g];
if (w === 0)
throw new Error("Requires delta != 0");
let S;
if (w > 0 && b < x || w < 0 && b > x)
S = 0;
else if (S = Math.ceil(Math.abs((b - x) / w)), S > e_)
throw new Error(`Requires ((limit - start) / delta) <= ${e_}`);
m[g + 1] = m[g] + S;
}
let d = m[l], f = y.getArrayFromDType(t10, d), h = 0;
for (let g = 0; g < l; ++g) {
let x = m[g + 1] - m[g], b = i ? r[0] : r[g], w = u ? s[0] : s[g];
for (let S = 0; S < x; ++S)
f[h++] = b, b += w;
}
return [m, f];
}
var $o = C.RowPartitionType;
var Cc = class {
constructor(e, t10, o, n, s, a, i, p, u, c) {
this.shape = e, this.shapeShape = t10, this.values = o, this.valuesShape = n, this.valuesDType = s, this.defaultValue = a, this.defaultValueShape = i, this.rowPartitionValues = p, this.rowPartitionValuesShapes = u, this.rowPartitionTypes = C.getRowPartitionTypesHelper(c), this.raggedRank = C.getRaggedRank(this.rowPartitionTypes);
}
getRowPartitionTypeByDimension(e) {
return this.rowPartitionTypes[0] === $o.FIRST_DIM_SIZE ? this.rowPartitionTypes[e + 1] : this.rowPartitionTypes[e];
}
getRowPartitionTensor(e) {
return this.rowPartitionTypes[0] === $o.FIRST_DIM_SIZE ? this.rowPartitionValues[e + 1] : this.rowPartitionValues[e];
}
getMaxWidth(e) {
let t10 = this.getRowPartitionTensor(e - 1);
switch (this.getRowPartitionTypeByDimension(e - 1)) {
case $o.VALUE_ROWIDS:
return Cc.getMaxWidthValueRowID(t10);
case $o.ROW_SPLITS:
return Cc.getMaxWidthRowSplit(t10);
default:
throw new Error(`Cannot handle partition type ${$o[this.getRowPartitionTypeByDimension(e - 1)]}`);
}
}
static getMaxWidthRowSplit(e) {
let t10 = e.length;
if (t10 === 0 || t10 === 1)
return 0;
let o = 0;
for (let n = 0; n < t10 - 1; ++n) {
let s = e[n + 1] - e[n];
s > o && (o = s);
}
return o;
}
static getMaxWidthValueRowID(e) {
let t10 = e.length;
if (t10 === 0)
return 0;
let o = 0, n = e[0], s = 0;
for (let a = 1; a < t10; ++a) {
let i = e[a];
i !== n && (n = i, s = Math.max(a - o, s), o = a);
}
return Math.max(t10 - o, s);
}
tensorShapeFromTensor(e, t10, o = true) {
if (t10.length === 0) {
if (e[0] === -1)
return [];
throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.");
}
return r_(e, o);
}
calculateOutputSize(e) {
let t10 = this.valuesShape, o = this.defaultValueShape;
C.validateDefaultValueShape(o, t10);
let n = this.tensorShapeFromTensor(this.shape, this.shapeShape), a = C.combineRaggedTensorToTensorShapes(this.raggedRank, n, t10);
a[0] < 0 && (a[0] = e);
for (let i = 1; i <= this.raggedRank; ++i)
a[i] < 0 && (a[i] = this.getMaxWidth(i));
return a;
}
calculateFirstParentOutputIndex(e, t10, o) {
let n = Math.min(e, o), s = [], a = 0;
for (let i = 0; i < n; ++i, a += t10)
s.push(a);
for (let i = n; i < e; ++i)
s.push(-1);
return y.assert(s.length === e, () => "Final length of result must be equal to firstDimension."), s;
}
calculateOutputIndexRowSplit(e, t10, o, n) {
let s = e.length, a = [];
for (let i = 0; i < s - 1; ++i) {
let p = e[i + 1] - e[i], u = Math.min(n, p), c = t10[i];
c === -1 && (u = 0);
for (let l = 0; l < u; ++l)
a.push(c), c += o;
for (let l = 0; l < p - u; ++l)
a.push(-1);
}
if (s > 0 && a.length !== e[s - 1])
throw new Error("Invalid row split size.");
return a;
}
calculateOutputIndexValueRowID(e, t10, o, n) {
let s = e.length, a = [];
if (s === 0)
return [];
let i = 0, p = e[0];
if (p >= t10.length)
throw new Error(`Got currentValueRowId=${p}, which is not less than ${t10.length}`);
let u = t10[p];
a.push(u);
for (let c = 1; c < s; ++c) {
let l = e[c];
if (l === p)
u >= 0 && (++i, i < n ? u += o : u = -1);
else {
if (i = 0, p = l, l >= t10.length)
throw new Error(`Got nextValueRowId=${l} which is not less than ${t10.length}`);
u = t10[l];
}
a.push(u);
}
if (a.length !== e.length)
throw new Error("Invalid row ids.");
return a;
}
calculateOutputIndex(e, t10, o, n) {
let s = this.getRowPartitionTensor(e), a = this.getRowPartitionTypeByDimension(e);
switch (a) {
case $o.VALUE_ROWIDS:
return this.calculateOutputIndexValueRowID(s, t10, o, n);
case $o.ROW_SPLITS:
if (s.length - 1 > t10.length)
throw new Error(`Row partition size is greater than output size: ${s.length - 1} > ${t10.length}`);
return this.calculateOutputIndexRowSplit(s, t10, o, n);
default:
throw new Error(`Unsupported partition type: ${$o[a]}`);
}
}
getFirstDimensionSize() {
let e = this.rowPartitionValues[0];
if (this.rowPartitionTypes.length === 0)
throw new Error("No row_partition_types given.");
let t10 = this.rowPartitionTypes[0];
switch (t10) {
case $o.FIRST_DIM_SIZE:
return e[0];
case $o.VALUE_ROWIDS:
throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");
case $o.ROW_SPLITS:
return this.rowPartitionValuesShapes[0][0] - 1;
default:
throw new Error(`Cannot handle type ${$o[t10]}`);
}
}
compute() {
if (this.rowPartitionValues[0].length <= 0)
throw new Error("Invalid first partition input. Tensor requires at least one element.");
let t10 = this.getFirstDimensionSize(), o = this.calculateOutputSize(t10), n = new Array(this.raggedRank + 1);
n[n.length - 1] = 1;
for (let p = n.length - 2; p >= 0; --p)
n[p] = n[p + 1] * o[p + 1];
let s = r_(o, false), a = y.getArrayFromDType(this.valuesDType, y.sizeFromShape(s));
if (n[0] * o[0] > 0) {
let p = this.calculateFirstParentOutputIndex(t10, n[0], o[0]);
for (let u = 1; u <= this.raggedRank; ++u)
p = this.calculateOutputIndex(u - 1, p, n[u], o[u]);
this.setOutput(this.raggedRank, p, a, s);
}
return [s, a];
}
setOutput(e, t10, o, n) {
if (o.length === 0)
return;
let s = this.values, a = o, i = n.slice();
i = i.slice(e + 1);
let p = y.sizeFromShape(i), u = t10.length, c = this.defaultValue;
if (c.length !== p && c.length !== 1) {
let f = this.defaultValueShape;
De(() => {
let h = W(c, f);
c = ru(h, i).dataSync();
});
}
let l = 0, m = 0, d = 0;
for (let f = 0; f <= u; ++f) {
let h = f < u ? t10[f] : -1;
if (h === d) {
++d;
continue;
}
if (m < d) {
let g = s.subarray(l * p), x = a.subarray(m * p), b = (d - m) * p;
t_(x, g, b);
}
if (f >= u) {
let g = o.length;
h = Math.floor(g / p);
}
if (h > d)
if (this.defaultValue.length === 1)
a.subarray(d * p, h * p).fill(this.defaultValue[0]), d = h;
else
for (; h > d; ) {
let g = a.slice(d * p);
t_(g, c, p), ++d;
}
h < 0 ? (l = f + 1, m = d) : (l = f, m = d, d = m + 1);
}
}
};
function t_(r, e, t10) {
for (let o = 0; o < t10; o++)
r[o] = e[o];
}
function r_(r, e) {
let t10 = [];
for (let o of r) {
if (o < 0) {
if (!e)
throw new Error(`Dimension ${o} must be >= 0`);
if (o < -1)
throw new Error(`Dimension ${o} must be >= -1`);
o = -1;
}
t10.push(o);
}
return t10;
}
function Ef(r, e, t10, o, n, s, a, i, p, u) {
return new Cc(r, e, t10, o, n, s, a, i, p, u).compute();
}
function ap(r, e, t10, o) {
let n = r === e, s = r < e && t10 < 0, a = e < r && t10 > 1;
if (n || s || a)
return y.makeZerosTypedArray(0, o);
let i = Math.abs(Math.ceil((e - r) / t10)), p = y.makeZerosTypedArray(i, o);
e < r && t10 === 1 && (t10 = -1), p[0] = r;
for (let u = 1; u < p.length; u++)
p[u] = p[u - 1] + t10;
return p;
}
var US = jt((r) => 1 / Math.sqrt(r));
var x8 = Dr(us, US);
var o_ = { kernelName: us, backendName: "cpu", kernelFunc: x8 };
function Fs(r, e, t10, o, n, s, a, i, p, u) {
let c = [o / n, n], l = r.values, m = e.values;
if (o === 0)
return me(t10, e.dtype);
let d = p instanceof tt ? p : me(c, e.dtype);
typeof p == "string" || typeof p == "number" ? d.values.fill(p) : typeof p == "boolean" && d.values.fill(+p);
for (let f = 0; f < s; f++) {
let h = [], g = 0;
for (let x = 0; x < a; x++) {
let b = l[f * a + x];
h.push(b), g += b * i[x];
}
if (g < 0 || g >= o / n)
throw new Error(`Invalid indices: ${h} does not index into ${t10}`);
for (let x = 0; x < n; x++)
u ? d.values[g * n + x] += m[f * n + x] : d.values[g * n + x] = e.rank === 0 ? m[0] : m[f * n + x];
}
return d;
}
var n_ = jt((r) => 1 / (1 + Math.exp(-r)));
var GS = Ie(hs, (r) => 1 / (1 + Math.exp(-r)));
var s_ = { kernelName: hs, backendName: "cpu", kernelFunc: GS };
function ip(r, e, t10, o, n) {
let s = ct.isSliceContinous(o, e, t10), a = y.sizeFromShape(t10), i = y.computeStrides(o);
if (s) {
let l = ct.computeFlatOffset(e, i);
return n === "string" ? r.slice(l, l + a) : r.subarray(l, l + a);
}
let p = n === "string" ? C.fromUint8ToStringArray(r) : r, u = me(o, n, p), c = me(t10, n);
for (let l = 0; l < c.size; ++l) {
let m = c.indexToLoc(l), d = m.map((f, h) => f + e[h]);
c.set(u.get(...d), ...m);
}
return n === "string" ? C.fromStringArrayToUint8(c.values) : c.values;
}
function Eo(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o;
Y(n, "slice");
let [i, p] = ct.parseSliceParams(n, s, a);
ct.assertParamsValid(n, i, p);
let u = t10.data.get(n.dataId).values, c = ip(u, i, p, n.shape, n.dtype);
return t10.makeTensorInfo(p, n.dtype, c);
}
var a_ = { kernelName: pa, backendName: "cpu", kernelFunc: Eo };
function Rf(r, e, t10, o, n, s, a) {
let i = e[0], p = s[0], u = new Array(p), c = new Array(i), l = e[1];
if (p === 0) {
if (i !== 0)
throw new Error(C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));
let g = y.getArrayFromDType(t10, 0), x = y.getArrayFromDType(n, 0);
return [g, [0, l], x, u, c];
}
let m = true, d = 0, f = new Array(p).fill(0);
for (let g = 0; g < i; ++g) {
let x = r[g * l];
if (x < 0)
throw new Error(C.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, x));
if (x >= p)
throw new Error(C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g, x, p));
++f[x], m = m && x >= d, d = x;
}
let h = true;
for (let g = 0; g < p; ++g) {
let x = f[g] === 0;
u[g] = x, h = h && !x, f[g] = Math.max(f[g], 1), g > 0 && (f[g] += f[g - 1]);
}
if (h && m) {
let g = r, x = o;
for (let b = 0; b < i; ++b)
c[b] = b;
return [g, [i, l], x, u, c];
} else {
let g = f[p - 1], x = y.getArrayFromDType(t10, g * l), b = y.getArrayFromDType(n, g), w = new Array(p).fill(0);
for (let S = 0; S < i; ++S) {
let k = r[S * l], _ = w[k], E = (k === 0 ? 0 : f[k - 1]) + _;
w[k]++;
for (let R = 0; R < l; ++R)
x[E * l + R] = r[S * l + R];
b[E] = o[S], c[S] = E;
}
for (let S = 0; S < p; ++S)
if (w[S] === 0) {
let _ = S === 0 ? 0 : f[S - 1];
x[_ * l + 0] = S;
for (let E = 1; E < l; ++E)
x[_ * l + E] = 0;
b[_] = a;
}
return [x, [g, l], b, u, c];
}
}
function Df(r, e, t10, o, n) {
let s = y.sizeFromShape(o), a = e[0], i = n.length, p = [], u = 1, c = -1;
for (let g = 0; g < i; ++g) {
let x = n[g];
if (x === -1) {
if (c !== -1)
throw new Error(C.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c, g));
c = g, p.push(1);
} else {
if (x < 0)
throw new Error(C.getSparseReshapeNegativeOutputDimErrorMessage(g, x));
u *= x, p.push(x);
}
}
if (c !== -1) {
if (u <= 0)
throw new Error(C.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
let g = Math.trunc(s / u);
if (u * g !== s)
throw new Error(C.getSparseReshapeInputOutputMultipleErrorMessage(o, p));
p[c] = g;
}
if (y.sizeFromShape(p) !== s)
throw new Error(C.getSparseReshapeInputOutputMismatchErrorMessage(o, p));
let m = o.length, d = [];
if (m > 0) {
d[m - 1] = 1;
for (let g = m - 2; g >= 0; --g)
d[g] = d[g + 1] * o[g + 1];
}
let f = [];
if (i > 0) {
f[i - 1] = 1;
for (let g = i - 2; g >= 0; --g)
f[g] = f[g + 1] * p[g + 1];
}
let h = y.getArrayFromDType(t10, a * i);
for (let g = 0; g < a; ++g) {
let x = 0;
for (let b = 0; b < m; ++b)
x += r[g * m + b] * d[b];
for (let b = 0; b < i; ++b)
h[g * i + b] = Math.trunc(x / f[b]), x %= f[b];
}
return [h, [a, i], p];
}
function wc(r, e, t10, o, n, s = false, a = 0) {
let i = o.length, p = [e[0], r.length / e[0]], u = p[1], l = i > 0 ? n[i - 1] + 1 : 0;
if (l < 0)
throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
let m = e.slice();
m[0] = l;
let d = m.reduce((w, S) => w * S, 1), f = y.getArrayFromDType(t10, d);
if (i === 0)
return l > 0 && f.fill(a), [f, m];
if (l <= 0)
throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
let h = 0, g = 1, x = 0, b = n[h];
for (; ; ) {
let w = 0;
if (g < i) {
if (w = n[g], b === w) {
++g;
continue;
}
if (b >= w)
throw new Error(C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
}
if (b < 0 || b >= l)
throw new Error(C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b, l));
b > x && f.fill(a, x * u, b * u);
for (let S = h; S < g; ++S) {
let k = o[S];
if (k < 0 || k >= p[0])
throw new Error(C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(S, o[S], p[0]));
for (let _ = 0; _ < u; _++)
f[b * u + _] += r[k * u + _];
}
if (s)
for (let S = 0; S < u; S++)
f[b * u + S] /= g - h;
if (h = g, ++g, x = b + 1, b = w, g > i)
break;
}
return x < l && f.fill(a, x * u, l * u), [f, m];
}
var i_ = jt((r) => Math.sqrt(r));
var y8 = Ie(xs, (r) => Math.sqrt(r));
var u_ = { kernelName: xs, backendName: "cpu", kernelFunc: y8 };
var HS = ze((r, e) => {
let t10 = r - e;
return t10 * t10;
});
var b8 = je(ws, HS);
var p_ = { kernelName: ws, backendName: "cpu", kernelFunc: b8 };
var KS = jt((r, e) => {
let { pattern: t10, replaceGlobal: o, rewrite: n } = e;
return r.replace(new RegExp(t10, o ? "g" : ""), n);
});
var C8 = Dr(_u, KS);
var c_ = { kernelName: _u, backendName: "cpu", kernelFunc: C8 };
function Af(r, e, t10, o) {
let n = me(r, e.dtype);
for (let s = 0; s < n.size; s++) {
let a = n.indexToLoc(s), i = new Array(a.length);
for (let p = 0; p < i.length; p++)
i[p] = a[p] * t10[p] + o[p];
n.set(e.get(...i), ...a);
}
return n;
}
var qS = class {
constructor(e, t10, o, n, s, a) {
this.separator = y.encodeString(e), this.nGramWidths = t10, this.leftPad = y.encodeString(o), this.rightPad = y.encodeString(n), this.padWidth = s, this.preserveShort = a;
}
getPadWidth(e) {
return Math.min(this.padWidth < 0 ? e - 1 : this.padWidth, e - 1);
}
getNumNGrams(e, t10) {
let o = this.getPadWidth(t10);
return Math.max(0, e + 2 * o - t10 + 1);
}
createNGrams(e, t10, o, n, s, a) {
for (let i = 0; i < s; ++i) {
let p = this.getPadWidth(a), u = Math.max(0, p - i), c = Math.max(0, p - (s - (i + 1))), l = a - (u + c), m = t10 + (u > 0 ? 0 : i - p), d = 0;
d += u * this.leftPad.length;
for (let b = 0; b < l; ++b)
d += e[m + b].length;
d += c * this.rightPad.length;
let f = u + c + l - 1;
d += f * this.separator.length, o[n + i] = new Uint8Array(d);
let h = o[n + i], g = 0, x = (b) => b.forEach((w) => h[g++] = w);
for (let b = 0; b < u; ++b)
x(this.leftPad), x(this.separator);
for (let b = 0; b < l - 1; ++b)
x(e[m + b]), x(this.separator);
if (l > 0) {
x(e[m + l - 1]);
for (let b = 0; b < c; ++b)
x(this.separator), x(this.rightPad);
} else {
for (let b = 0; b < c - 1; ++b)
x(this.rightPad), x(this.separator);
x(this.rightPad);
}
}
}
compute(e, t10) {
let o = e.length, n = t10.length;
if (n > 0) {
let p = t10[0];
if (p !== 0)
throw new Error(`First split value must be 0, got ${p}`);
for (let u = 1; u < n; ++u) {
let c = t10[u] >= p;
if (c = c && t10[u] <= o, !c)
throw new Error(`Invalid split value ${t10[u]}, must be in [${p}, ${o}]`);
p = t10[u];
}
if (p !== o)
throw new Error(`Last split value must be data size. Expected ${o}, got ${p}`);
}
let s = n - 1, a = y.getArrayFromDType("int32", n);
if (o === 0 || n === 0) {
let p = new Array(o);
for (let u = 0; u <= s; ++u)
a[u] = 0;
return [p, a];
}
a[0] = 0;
for (let p = 1; p <= s; ++p) {
let u = t10[p] - t10[p - 1], c = 0;
this.nGramWidths.forEach((l) => {
c += this.getNumNGrams(u, l);
}), this.preserveShort && u > 0 && c === 0 && (c = 1), a[p] = a[p - 1] + c;
}
let i = new Array(a[s]);
for (let p = 0; p < s; ++p) {
let u = t10[p], c = a[p];
if (this.nGramWidths.forEach((l) => {
let m = t10[p + 1] - t10[p], d = this.getNumNGrams(m, l);
this.createNGrams(e, u, i, c, d, l), c += d;
}), this.preserveShort && c === a[p]) {
let l = t10[p + 1] - t10[p];
if (l === 0)
continue;
let m = l + 2 * this.padWidth, d = 1;
this.createNGrams(e, u, i, c, d, m);
}
}
return [i, a];
}
};
function up(r, e, t10, o, n, s, a, i) {
return new qS(t10, o, n, s, a, i).compute(r, e);
}
function w8(r, e, t10, o) {
if (!r.length)
return;
if (e.length === 0) {
for (let s = 0; s < r.length; ++s)
o.push(r.subarray(s, s + 1));
return;
}
if (e.length === 1) {
let s = e[0], a = r.indexOf(s);
for (; a !== -1; ) {
let i = r.subarray(0, a);
(!t10 || i.length !== 0) && o.push(i), r = r.subarray(a + 1), a = r.indexOf(s);
}
(!t10 || r.length !== 0) && o.push(r);
return;
}
let n = 0;
for (let s = 0; s < r.length + 1; s++)
if (s === r.length || e.indexOf(r[s]) !== -1) {
let a = r.subarray(n, s);
(!t10 || a.length !== 0) && o.push(a), n = s + 1;
}
}
function pp(r, e, t10) {
let o = r.length, n = [], s = 0, a = 0, i = new Array(o);
for (let m = 0; m < o; ++m) {
let d = n.length;
w8(r[m], e, t10, n);
let f = n.length - d;
i[m] = f, s += f, a = Math.max(a, f);
}
let p = y.getArrayFromDType("int32", s * 2), u = new Array(s), c = [o, a], l = 0;
for (let m = 0; m < o; ++m)
for (let d = 0; d < i[m]; ++d)
p[l * 2] = m, p[l * 2 + 1] = d, u[l] = n[l], ++l;
return [p, u, c];
}
function cp(r, e) {
let t10 = y.getArrayFromDType("int32", r.length);
for (let o = 0; o < r.length; ++o)
t10[o] = y.fingerPrint64(r[o]).modulo(e).getLowBitsUnsigned();
return t10;
}
var jS = ze((r, e) => r - e);
var S8 = xc((r, e, t10, o) => ({ real: r - t10, imag: e - o }));
var Ll = je(Is, jS, S8);
var l_ = { kernelName: Is, backendName: "cpu", kernelFunc: Ll };
function Ff(r, e) {
let t10 = new Array(r.rank);
for (let n = 0; n < t10.length; n++)
t10[n] = r.shape[n] * e[n];
let o = me(t10, r.dtype);
for (let n = 0; n < o.values.length; ++n) {
let s = o.indexToLoc(n), a = new Array(r.rank);
for (let p = 0; p < a.length; p++)
a[p] = s[p] % r.shape[p];
let i = r.locToIndex(a);
o.values[n] = r.values[i];
}
return o;
}
var Bl = (r, e) => {
let t10 = e.value - r.value;
return t10 === 0 ? r.index - e.index : t10;
};
function m_(r, e, t10 = 0, o = r.length - 1) {
for (; o > t10; ) {
if (o - t10 > 600) {
let i = o - t10 + 1, p = e - t10 + 1, u = Math.log(i), c = 0.5 * Math.exp(2 * u / 3), l = 0.5 * Math.sqrt(u * c * (i - c) / i) * Math.sign(p - i / 2), m = Math.max(t10, Math.floor(e - p * c / i + l)), d = Math.min(o, Math.floor(e + (i - p) * c / i + l));
m_(r, e, m, d);
}
let n = r[e], s = t10, a = o;
for (y.swap(r, t10, e), Bl(r[o], n) > 0 && y.swap(r, t10, o); s < a; ) {
for (y.swap(r, s, a), s++, a--; Bl(r[s], n) < 0; )
s = s + 1;
for (; Bl(r[a], n) > 0; )
a = a - 1;
}
Bl(r[t10], n) === 0 ? y.swap(r, t10, a) : (a = a + 1, y.swap(r, a, o)), a <= e && (t10 = a + 1), e <= a && (o = a - 1);
}
}
function Pf(r, e, t10, o, n) {
let s = e[e.length - 1], [a, i] = [r.length / s, s], p = y.getTypedArrayFromDType(t10, a * o), u = y.getTypedArrayFromDType("int32", a * o);
for (let l = 0; l < a; l++) {
let m = l * i, d = r.subarray(m, m + i), f = new Array(d.length);
d.forEach((b, w) => f[w] = { value: b, index: w }), o < f.length && (m_(f, o), f = f.slice(0, o)), n && f.sort(Bl);
let h = l * o, g = p.subarray(h, h + o), x = u.subarray(h, h + o);
for (let b = 0; b < o; b++)
g[b] = f[b].value, x[b] = f[b].index;
}
let c = e.slice();
return c[c.length - 1] = o, [me(c, t10, p), me(c, "int32", u)];
}
function lp(r, e, t10, o) {
let n = y.parseAxisParam(e, t10)[0], s = [1, t10[0], 1];
for (let f = 0; f < n; f++)
s[0] *= t10[f];
s[1] = t10[n];
for (let f = n + 1; f < t10.length; f++)
s[2] *= t10[f];
let a = /* @__PURE__ */ new Map(), i = new Int32Array(t10[n]), p = new tt(s, o, r), u = [], c = s[0] === 1 && s[2] === 1;
for (let f = 0; f < t10[n]; f++) {
let h;
if (c)
h = r[f].toString();
else {
let x = [];
for (let b = 0; b < s[0]; b++)
for (let w = 0; w < s[2]; w++)
x.push(p.get(b, f, w));
h = x.join(",");
}
let g = a.get(h);
if (g != null)
i[f] = g;
else {
let x = a.size;
a.set(h, x), i[f] = x, u.push(f);
}
}
let l = s.slice();
l[1] = a.size;
let m = new tt(l, o);
u.forEach((f, h) => {
for (let g = 0; g < s[0]; g++)
for (let x = 0; x < s[2]; x++)
m.set(p.get(g, f, x), g, h, x);
});
let d = t10.slice();
return d[n] = l[1], { outputValues: m.values, outputShape: d, indices: i };
}
var I8 = "4.5.0";
eu("cpu", () => new lu(), 1);
var XS = Ie(fn, (r) => r >= 0 ? r : Math.exp(r) - 1);
var d_ = { kernelName: fn, backendName: "cpu", kernelFunc: XS };
function YS(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o;
Y([n], "leakyRelu");
let a = y.sizeFromShape(n.shape), i = t10.data.get(n.dataId).values, p = y.getTypedArrayFromDType("float32", a);
for (let u = 0; u < i.length; u++)
p[u] = i[u] < 0 ? s * i[u] : i[u];
return t10.makeTensorInfo(n.shape, "float32", p);
}
var f_ = { kernelName: _n, backendName: "cpu", kernelFunc: YS };
var v8 = ze((r, e) => r < 0 ? e * r : r);
function QS(r) {
let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e;
Y([o, n], "prelu");
let s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, [i, p] = v8(o.shape, n.shape, s, a, "float32");
return t10.makeTensorInfo(p, "float32", i);
}
var h_ = { kernelName: Jn, backendName: "cpu", kernelFunc: QS };
var ZS = Ie(rs, (r) => Math.max(0, r));
var g_ = { kernelName: rs, backendName: "cpu", kernelFunc: ZS };
var JS = Ie(ss, (r) => Math.min(Math.max(0, r), 6));
var x_ = { kernelName: ss, backendName: "cpu", kernelFunc: JS };
function mp(r, e, t10, o, n) {
if (t10 === "linear")
return lr({ inputs: { x: e }, backend: r });
if (t10 === "relu")
return ZS({ inputs: { x: e }, backend: r });
if (t10 === "elu")
return XS({ inputs: { x: e }, backend: r });
if (t10 === "relu6")
return JS({ inputs: { x: e }, backend: r });
if (t10 === "prelu")
return QS({ inputs: { x: e, alpha: o }, backend: r });
if (t10 === "leakyrelu")
return YS({ inputs: { x: e }, backend: r, attrs: { alpha: n } });
if (t10 === "sigmoid")
return GS({ inputs: { x: e }, backend: r });
throw new Error(`Activation ${t10} has not been implemented for the CPU backend.`);
}
function Ve(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { shape: s } = o, a = y.sizeFromShape(n.shape), i = y.inferFromImplicitShape(s, a), p = y.sizeFromShape(i);
y.assert(a === p, () => `The new shape (${i}) has ${p} elements and the old shape (${n.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), t10.incRef(n.dataId);
let u = t10.data.get(n.dataId);
if (u.complexTensorInfos != null) {
let c = u.complexTensorInfos.real, l = u.complexTensorInfos.imag;
c.shape = i, l.shape = i;
}
return { dataId: n.dataId, shape: i, dtype: n.dtype };
}
var y_ = { kernelName: ia, backendName: "cpu", kernelFunc: Ve };
function eI(r) {
let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
Y([n, s], "matMul");
let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), w = Sr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]);
y.assert(c === l, () => `Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`);
let S = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Ve({ inputs: { x: n }, backend: t10, attrs: { shape: S } }), E = Ve({ inputs: { x: s }, backend: t10, attrs: { shape: k } }), R = a ? _.shape[1] : _.shape[2], D = a ? _.shape[2] : _.shape[1], F = i ? E.shape[1] : E.shape[2], O = Math.max(g, x), M = t10.data.get(_.dataId).values, L = t10.data.get(E.dataId).values, B = y.computeStrides(_.shape), z = y.computeStrides(E.shape), [U, j, H] = a ? [B[0], 1, B[1]] : [B[0], B[1], 1], [X, J, re] = i ? [1, z[1], z[0]] : [z[1], 1, z[0]], ne = D * F, ee = me([O, D, F], _.dtype), oe = ee.values, ie = t10.blockSize;
for (let le = 0; le < O; le++) {
let ye = le % g, _e = le % x;
for (let ve = 0; ve < D; ve += ie) {
let Fe = Math.min(ve + ie, D);
for (let Pe = 0; Pe < F; Pe += ie) {
let st = Math.min(Pe + ie, F);
for (let lt = 0; lt < R; lt += ie) {
let We = Math.min(lt + ie, R);
for (let mt = ve; mt < Fe; mt++)
for (let it = Pe; it < st; it++) {
let ht = 0;
for (let gt = lt; gt < We; gt++) {
let Or = M[ye * U + mt * j + gt * H], Mt = L[gt * X + it * J + _e * re];
ht += Or * Mt;
}
oe[le * ne + (mt * F + it)] += ht;
}
}
}
}
}
return t10.disposeIntermediateTensorInfo(_), t10.disposeIntermediateTensorInfo(E), t10.makeTensorInfo(w, ee.dtype, ee.values);
}
var b_ = { kernelName: Qo, backendName: "cpu", kernelFunc: eI };
function k8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m, d, f, h = [];
m = eI({ inputs: { a: n, b: s }, attrs: { transposeA: p, transposeB: u }, backend: t10 }), a && (d = _a({ inputs: { a: m, b: a }, backend: t10 }), h.push(m), m = d), c && (f = mp(t10, m, c, i, l), h.push(m), m = f);
for (let x of h)
t10.disposeIntermediateTensorInfo(x);
return m;
}
var C_ = { kernelName: bo, backendName: "cpu", kernelFunc: k8 };
var N8 = Ie(zo, (r) => Math.acos(r));
var w_ = { kernelName: zo, backendName: "cpu", kernelFunc: N8 };
var T8 = Ie(Vo, (r) => Math.acosh(r));
var S_ = { kernelName: Vo, backendName: "cpu", kernelFunc: T8 };
function _8(r) {
let { inputs: e, backend: t10 } = r, o = e;
Y(e, "addN");
let n = o.map((i) => t10.data.get(i.dataId).values), s = me(o[0].shape, o[0].dtype), a = s.values;
for (let i = 0; i < o.length; i++) {
let p = n[i];
for (let u = 0; u < a.length; u++)
a[u] += p[u];
}
return t10.makeTensorInfo(s.shape, s.dtype, s.values);
}
var I_ = { kernelName: Wo, backendName: "cpu", kernelFunc: _8 };
function $8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
Y(n, "all");
let i = y.parseAxisParam(s, n.shape), p = i, u = C.getAxesPermutation(p, n.shape.length), c = n;
u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = C.getInnerMostAxes(p.length, n.shape.length)), C.assertAxesAreInnerMostDims("all", p, c.shape.length);
let [l, m] = C.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values;
for (let x = 0; x < f.length; ++x) {
let b = x * d, w = h[b];
for (let S = 0; S < d; ++S) {
let k = h[b + S];
w = w && k;
}
f[x] = w;
}
u != null && t10.disposeIntermediateTensorInfo(c);
let g = t10.makeTensorInfo(l, c.dtype, f);
if (a) {
let x = C.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t10, attrs: { shape: x } });
return t10.disposeIntermediateTensorInfo(g), b;
}
return g;
}
var v_ = { kernelName: Uo, backendName: "cpu", kernelFunc: $8 };
function E8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
Y(n, "any");
let i = y.parseAxisParam(s, n.shape), p = i, u = C.getAxesPermutation(p, n.shape.length), c = n;
u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = C.getInnerMostAxes(p.length, n.shape.length)), C.assertAxesAreInnerMostDims("any", p, c.shape.length);
let [l, m] = C.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values;
for (let x = 0; x < f.length; ++x) {
let b = x * d, w = h[b];
for (let S = 0; S < d; ++S) {
let k = h[b + S];
w = w || k;
}
f[x] = w;
}
u != null && t10.disposeIntermediateTensorInfo(c);
let g = t10.makeTensorInfo(l, c.dtype, f);
if (a) {
let x = C.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t10, attrs: { shape: x } });
return t10.disposeIntermediateTensorInfo(g), b;
}
return g;
}
var k_ = { kernelName: Go, backendName: "cpu", kernelFunc: E8 };
function R8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o;
Y(n, "argMax");
let a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = St({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], C.assertAxesAreInnerMostDims("argMax", a, p.shape.length);
let [c, l] = C.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t10.data.get(p.dataId).values;
for (let g = 0; g < d.length; ++g) {
let x = g * f, b = h[x], w = 0;
for (let S = 0; S < f; ++S) {
let k = h[x + S];
k > b && (b = k, w = S);
}
d[g] = w;
}
return u.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.makeTensorInfo(c, "int32", d);
}
var N_ = { kernelName: Hs, backendName: "cpu", kernelFunc: R8 };
function D8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o;
Y(n, "argMin");
let a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = St({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], C.assertAxesAreInnerMostDims("argMin", a, p.shape.length);
let [c, l] = C.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t10.data.get(p.dataId).values;
for (let g = 0; g < d.length; ++g) {
let x = g * f, b = h[x], w = 0;
for (let S = 0; S < f; ++S) {
let k = h[x + S];
k < b && (b = k, w = S);
}
d[g] = w;
}
return u.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.makeTensorInfo(c, "int32", d);
}
var T_ = { kernelName: Ks, backendName: "cpu", kernelFunc: D8 };
var A8 = Ie(Ho, (r) => Math.asin(r));
var __ = { kernelName: Ho, backendName: "cpu", kernelFunc: A8 };
var F8 = Ie(Ko, (r) => Math.asinh(r));
var $_ = { kernelName: Ko, backendName: "cpu", kernelFunc: F8 };
var P8 = Ie(qo, (r) => Math.atan(r));
var E_ = { kernelName: qo, backendName: "cpu", kernelFunc: P8 };
var O8 = ze((r, e) => Math.atan2(r, e));
var M8 = je(Xo, O8);
var R_ = { kernelName: Xo, backendName: "cpu", kernelFunc: M8 };
var L8 = Ie(jo, (r) => Math.atanh(r));
var D_ = { kernelName: jo, backendName: "cpu", kernelFunc: L8 };
function Ic(r, e, t10, o, n, s) {
let a = n.strideHeight, i = n.strideWidth, p = n.dilationHeight, u = n.dilationWidth, c = n.effectiveFilterHeight, l = n.effectiveFilterWidth, m = n.padInfo.top, d = n.padInfo.left, f = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, h = me(n.outShape, t10), g = h.values, x = n.outShape[1] * n.outShape[2] * n.outShape[3], b = n.outShape[2] * n.outShape[3], w = n.outShape[3];
for (let S = 0; S < n.batchSize; ++S) {
let k = S * x, _ = S * o[0];
for (let E = 0; E < n.inChannels; ++E)
for (let R = 0; R < n.outHeight; ++R) {
let D = R * a - m, F = Math.max(0, D), O = Math.min(n.inHeight, c + D), M = k + R * b;
for (let L = 0; L < n.outWidth; ++L) {
let B = L * i - d, z = Math.max(0, B), U = Math.min(n.inWidth, l + B), j = f, H = 0, X = 0;
for (let re = F; re < O; re += p) {
let ne = _ + re * o[1];
for (let ee = z; ee < U; ee += u) {
let oe = ne + ee * o[2], ie = r[oe + E];
s === "max" && ie > j ? j = ie : s === "avg" && (H += ie, X++);
}
if (isNaN(j))
break;
}
let J = M + L * w + E;
g[J] = s === "avg" ? H / X : j;
}
}
}
return h;
}
function Of(r, e, t10, o, n = false, s = false) {
let a = me(o.outShape, "int32"), i = o.strideHeight, p = o.strideWidth, u = o.dilationHeight, c = o.dilationWidth, l = o.effectiveFilterHeight, m = o.effectiveFilterWidth, d = o.padInfo.top, f = o.padInfo.left, h = me(e, t10, r);
for (let g = 0; g < o.batchSize; ++g)
for (let x = 0; x < o.inChannels; ++x)
for (let b = 0; b < o.outHeight; ++b) {
let w = b * i - d, S = w;
for (; S < 0; )
S += u;
let k = Math.min(o.inHeight, l + w);
for (let _ = 0; _ < o.outWidth; ++_) {
let E = _ * p - f, R = E;
for (; R < 0; )
R += c;
let D = Math.min(o.inWidth, m + E), F = Number.NEGATIVE_INFINITY, O = -1;
for (let M = S; M < k; M += u) {
let L = M - w;
for (let B = R; B < D; B += c) {
let z = B - E, U = h.get(g, M, B, x);
U > F && (F = U, n ? O = s ? ((g * o.inHeight + M) * o.inWidth + B) * o.inChannels + x : (M * o.inWidth + B) * o.inChannels + x : O = L * m + z);
}
}
a.set(O, g, b, _, x);
}
}
return a;
}
function Mf(r, e, t10, o, n, s) {
let a = n.strideDepth, i = n.strideHeight, p = n.strideWidth, u = n.dilationDepth, c = n.dilationHeight, l = n.dilationWidth, m = n.effectiveFilterDepth, d = n.effectiveFilterHeight, f = n.effectiveFilterWidth, h = n.padInfo.front, g = n.padInfo.top, x = n.padInfo.left, b = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, w = me(n.outShape, t10), S = w.values, k = n.outShape[1] * n.outShape[2] * n.outShape[3] * n.outShape[4], _ = n.outShape[2] * n.outShape[3] * n.outShape[4], E = n.outShape[3] * n.outShape[4], R = n.outShape[4];
for (let D = 0; D < n.batchSize; ++D) {
let F = D * k, O = D * o[0];
for (let M = 0; M < n.inChannels; ++M)
for (let L = 0; L < n.outDepth; ++L) {
let B = L * a - h, z = B;
for (; z < 0; )
z += u;
let U = Math.min(n.inDepth, m + B), j = F + L * _;
for (let H = 0; H < n.outHeight; ++H) {
let X = H * i - g, J = X;
for (; J < 0; )
J += c;
let re = Math.min(n.inHeight, d + X), ne = j + H * E;
for (let ee = 0; ee < n.outWidth; ++ee) {
let oe = ee * p - x, ie = oe;
for (; ie < 0; )
ie += l;
let le = Math.min(n.inWidth, f + oe), ye = ne + ee * R, _e = b, ve = 0, Fe = 0;
for (let st = z; st < U; st += u) {
let lt = O + st * o[1];
for (let We = J; We < re; We += c) {
let mt = lt + We * o[2];
for (let it = ie; it < le; it += l) {
let ht = mt + it * o[3], gt = r[ht + M];
if (s === "max" && gt > _e ? _e = gt : s === "avg" && (ve += gt, Fe++), isNaN(_e))
break;
}
if (isNaN(_e))
break;
}
if (isNaN(_e))
break;
}
let Pe = ye + M;
S[Pe] = s === "avg" ? ve / Math.max(Fe, 1) : _e;
}
}
}
}
return w;
}
function A_(r, e) {
let t10 = me(e.outShape, "int32"), o = e.strideDepth, n = e.strideHeight, s = e.strideWidth, a = e.dilationDepth, i = e.dilationHeight, p = e.dilationWidth, u = e.effectiveFilterDepth, c = e.effectiveFilterHeight, l = e.effectiveFilterWidth, m = e.padInfo.front, d = e.padInfo.top, f = e.padInfo.left;
for (let h = 0; h < e.batchSize; ++h)
for (let g = 0; g < e.inChannels; ++g)
for (let x = 0; x < e.outDepth; ++x) {
let b = x * o - m, w = b;
for (; w < 0; )
w += a;
let S = Math.min(e.inDepth, u + b);
for (let k = 0; k < e.outHeight; ++k) {
let _ = k * n - d, E = _;
for (; E < 0; )
E += i;
let R = Math.min(e.inHeight, c + _);
for (let D = 0; D < e.outWidth; ++D) {
let F = D * s - f, O = F;
for (; O < 0; )
O += p;
let M = Math.min(e.inWidth, l + F), L = Number.NEGATIVE_INFINITY, B = -1;
for (let z = w; z < S; z += a) {
let U = z - b;
for (let j = E; j < R; j += i) {
let H = j - _;
for (let X = O; X < M; X += p) {
let J = X - F, re = r.get(h, z, j, X, g);
re >= L && (L = re, B = U * c * l + H * c + J);
}
}
}
t10.set(B, h, x, k, D, g);
}
}
}
return t10;
}
function B8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
Y(n, "avgPool");
let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
y.assert(C.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
let c = C.computePool2DInfo(n.shape, s, a, u, i, p), l;
if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape))
l = lr({ inputs: { x: n }, backend: t10 });
else {
let m = t10.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Ic(m, n.shape, n.dtype, d, c, "avg");
l = t10.makeTensorInfo(c.outShape, n.dtype, f.values);
}
return l;
}
var F_ = { kernelName: Yo, backendName: "cpu", kernelFunc: B8 };
function z8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o;
Y(n, "avgPool3d");
let c = C.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.data.get(n.dataId).values, m = Mf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "avg");
return t10.makeTensorInfo(m.shape, "float32", m.values);
}
var P_ = { kernelName: qs, backendName: "cpu", kernelFunc: z8 };
function V8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o;
Y([n, s], "avgPool3DGrad");
let c = C.computePool3DInfo(s.shape, a, i, 1, p, u), l = c.strideDepth, m = c.strideHeight, d = c.strideWidth, f = c.filterDepth, h = c.filterHeight, g = c.filterWidth, x = c.dilationDepth, b = c.dilationHeight, w = c.dilationWidth, S = c.effectiveFilterDepth, k = c.effectiveFilterHeight, _ = c.effectiveFilterWidth, E = S - 1 - c.padInfo.front, R = _ - 1 - c.padInfo.left, D = k - 1 - c.padInfo.top, F = me(s.shape, "float32"), O = 1 / (f * h * g), M = t10.bufferSync(n);
for (let L = 0; L < c.batchSize; ++L)
for (let B = 0; B < c.inChannels; ++B)
for (let z = 0; z < c.inDepth; ++z)
for (let U = 0; U < c.inHeight; ++U)
for (let j = 0; j < c.inWidth; ++j) {
let H = z - E, X = U - D, J = j - R, re = 0;
for (let ne = 0; ne < S; ne += x) {
let ee = (H + ne) / l;
if (!(ee < 0 || ee >= c.outDepth || Math.floor(ee) !== ee))
for (let oe = 0; oe < k; oe += b) {
let ie = (X + oe) / m;
if (!(ie < 0 || ie >= c.outHeight || Math.floor(ie) !== ie))
for (let le = 0; le < _; le += w) {
let ye = (J + le) / d;
if (ye < 0 || ye >= c.outWidth || Math.floor(ye) !== ye)
continue;
let _e = M.get(L, ee, ie, ye, B);
re += _e;
}
}
}
F.set(re * O, L, z, U, j, B);
}
return t10.makeTensorInfo(F.shape, F.dtype, F.values);
}
var O_ = { kernelName: Ni, backendName: "cpu", kernelFunc: V8 };
function W8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s;
Y([n, s], "avgPoolGrad");
let { filterSize: i, strides: p, pad: u } = o, c = C.computePool2DInfo(a.shape, i, p, 1, u), l = c.strideHeight, m = c.strideWidth, d = c.filterHeight, f = c.filterWidth, h = c.dilationHeight, g = c.dilationWidth, x = c.effectiveFilterHeight, b = c.effectiveFilterWidth, w = b - 1 - c.padInfo.left, S = x - 1 - c.padInfo.top, k = me(a.shape, "float32"), _ = 1 / (d * f), E = t10.data.get(n.dataId).values, R = me(n.shape, "float32", E);
for (let D = 0; D < c.batchSize; ++D)
for (let F = 0; F < c.inChannels; ++F)
for (let O = 0; O < c.inHeight; ++O)
for (let M = 0; M < c.inWidth; ++M) {
let L = O - S, B = M - w, z = 0;
for (let U = 0; U < x; U += h) {
let j = (L + U) / l;
if (!(j < 0 || j >= c.outHeight || Math.floor(j) !== j))
for (let H = 0; H < b; H += g) {
let X = (B + H) / m;
if (X < 0 || X >= c.outWidth || Math.floor(X) !== X)
continue;
let J = R.get(D, j, X, F);
z += J;
}
}
k.set(z * _, D, O, M, F);
}
return t10.makeTensorInfo(k.shape, k.dtype, k.values);
}
var M_ = { kernelName: Gp, backendName: "cpu", kernelFunc: W8 };
function U8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, scale: s, offset: a, mean: i, variance: p } = e;
y.assert(i.shape.length === p.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || i.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(s == null || i.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), Y([n, i, p, s, a], "batchNorm");
let { varianceEpsilon: u } = o;
u == null && (u = 1e-3);
let c = t10.data.get(n.dataId).values, l = t10.data.get(i.dataId).values, m = t10.data.get(p.dataId).values, d = s ? t10.data.get(s.dataId).values : new Float32Array([1]), f = a ? t10.data.get(a.dataId).values : new Float32Array([0]), h = new Float32Array(c.length), g = f.length, x = d.length, b = m.length, w = l.length, S = 0, k = 0, _ = 0, E = 0;
for (let R = 0; R < c.length; ++R)
h[R] = f[S++] + (c[R] - l[k++]) * d[_++] / Math.sqrt(m[E++] + u), S >= g && (S = 0), k >= w && (k = 0), _ >= x && (_ = 0), E >= b && (E = 0);
return t10.makeTensorInfo(n.shape, n.dtype, h);
}
var L_ = { kernelName: wn, backendName: "cpu", kernelFunc: U8 };
function G8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o;
Y([n], "batchToSpaceND");
let i = s.reduce((x, b) => x * b), p = C.getReshaped(n.shape, s, i), u = C.getPermuted(p.length, s.length), c = C.getReshapedPermuted(n.shape, s, i), l = C.getSliceBeginCoords(a, s.length), m = C.getSliceSize(c, a, s.length), d = Ve({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), f = St({ inputs: { x: d }, backend: t10, attrs: { perm: u } }), h = Ve({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = Eo({ inputs: { x: h }, backend: t10, attrs: { begin: l, size: m } });
return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), g;
}
var B_ = { kernelName: js, backendName: "cpu", kernelFunc: G8 };
function H8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, u = yc(i, p, s.dtype, s.shape, a);
return t10.makeTensorInfo([a], s.dtype, u);
}
var z_ = { kernelName: Zo, backendName: "cpu", kernelFunc: H8 };
function K8(r) {
let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, i = C.assertAndGetBroadcastShape(Array.from(s), Array.from(a));
return t10.makeTensorInfo([i.length], "int32", Int32Array.from(i));
}
var V_ = { kernelName: Xs, backendName: "cpu", kernelFunc: K8 };
var q8 = Ie(go, (r, e) => {
let t10 = e;
return r > t10.clipValueMax ? t10.clipValueMax : r < t10.clipValueMin ? t10.clipValueMin : r;
});
var W_ = { kernelName: go, backendName: "cpu", kernelFunc: q8 };
var j8 = (r) => {
let { x: e } = r.inputs, t10 = r.backend, o = new Float32Array(y.sizeFromShape(e.shape)), n = t10.data.get(e.dataId), s = n.complexTensorInfos.real, a = n.complexTensorInfos.imag, i = t10.data.get(s.dataId).values, p = t10.data.get(a.dataId).values;
for (let u = 0; u < i.length; u++) {
let c = i[u], l = p[u];
o[u] = Math.hypot(c, l);
}
return t10.makeOutput(o, e.shape, "float32");
};
var U_ = { kernelName: _i, backendName: "cpu", kernelFunc: j8 };
function $a(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.data.get(o.dataId).complexTensorInfos.imag, s = t10.data.get(n.dataId).values;
return t10.makeTensorInfo(n.shape, n.dtype, s);
}
var G_ = { kernelName: Mi, backendName: "cpu", kernelFunc: $a };
function mu(r) {
let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((h) => h.shape);
C.assertParamsConsistent(a, s);
let i = C.computeOutShape(e.map((h) => h.shape), s);
if (y.sizeFromShape(i) === 0)
return t10.makeTensorInfo(i, e[0].dtype, []);
let p = e.filter((h) => y.sizeFromShape(h.shape) > 0);
if (p.length === 1)
return lr({ inputs: { x: p[0] }, backend: t10 });
if (p[0].dtype === "complex64") {
let h = p.map((S) => To({ inputs: { input: S }, backend: t10 })), g = p.map((S) => $a({ inputs: { input: S }, backend: t10 })), x = mu({ inputs: h, backend: t10, attrs: { axis: s } }), b = mu({ inputs: g, backend: t10, attrs: { axis: s } }), w = Kt({ inputs: { real: x, imag: b }, backend: t10 });
return h.forEach((S) => t10.disposeIntermediateTensorInfo(S)), g.forEach((S) => t10.disposeIntermediateTensorInfo(S)), t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(b), w;
}
let u = p.map((h) => {
let x = [-1, y.sizeFromShape(h.shape.slice(s))];
return Ve({ inputs: { x: h }, backend: t10, attrs: { shape: x } });
}), c = u.map((h) => ({ vals: t10.data.get(h.dataId).values, shape: h.shape }));
i = C.computeOutShape(u.map((h) => h.shape), 1);
let l = u[0].shape[0] === 1, m = np(c, i, e[0].dtype, l), d = C.computeOutShape(p.map((h) => h.shape), s), f = t10.makeTensorInfo(d, e[0].dtype, m);
return u.forEach((h) => t10.disposeIntermediateTensorInfo(h)), f;
}
var H_ = { kernelName: Ys, backendName: "cpu", kernelFunc: mu };
function tI(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o;
Y([n, s], "conv2d");
let l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d = m.filterHeight, f = m.filterWidth, h = m.dilationHeight, g = m.dilationWidth, x = m.padInfo.left, b = m.padInfo.top, w = m.dataFormat === "channelsLast", S = new tt(m.outShape, n.dtype), k = y.computeStrides(n.shape), _ = y.computeStrides(s.shape), E = k[0], R = w ? k[1] : k[2], D = w ? k[2] : 1, F = w ? 1 : k[1], O = S.strides[0], M = w ? S.strides[1] : S.strides[2], L = w ? S.strides[2] : 1, B = w ? 1 : S.strides[1], z = t10.data.get(n.dataId).values, U = t10.data.get(s.dataId).values, j = S.values;
for (let H = 0; H < m.batchSize; ++H) {
let X = H * E, J = H * O;
for (let re = 0; re < m.outHeight; ++re) {
let ne = J + re * M, ee = re * m.strideHeight - b;
for (let oe = 0; oe < d; ++oe) {
let ie = ee + oe * h;
if (ie < 0 || ie >= m.inHeight)
continue;
let le = oe * _[0], ye = X + ie * R;
for (let _e = 0; _e < m.outWidth; ++_e) {
let ve = ne + _e * L, Fe = _e * m.strideWidth - x;
for (let Pe = 0; Pe < f; ++Pe) {
let st = Fe + Pe * g;
if (st < 0 || st >= m.inWidth)
continue;
let lt = le + Pe * _[1], We = ye + st * D, mt = lt;
for (let it = 0; it < m.inChannels; ++it) {
let ht = z[We + it * F];
for (let gt = 0; gt < m.outChannels; ++gt)
j[ve + gt * B] += ht * U[mt + gt];
mt += m.outChannels;
}
}
}
}
}
}
return t10.makeTensorInfo(S.shape, S.dtype, j);
}
var K_ = { kernelName: en, backendName: "cpu", kernelFunc: tI };
function X8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o;
Y([n, s], "conv2dBackpropFilter");
let l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), { strideHeight: d, strideWidth: f, filterHeight: h, filterWidth: g } = m, x = m.dataFormat === "channelsLast", b = new tt(m.filterShape, "float32"), w = m.padInfo.left, S = m.padInfo.top, k = t10.data.get(n.dataId).values, _ = t10.data.get(s.dataId).values, E = new tt(n.shape, n.dtype, k), R = new tt(s.shape, s.dtype, _);
for (let D = 0; D < h; ++D) {
let F = Math.max(0, Math.ceil((S - D) / d)), O = Math.min(m.outHeight, (m.inHeight + S - D) / d);
for (let M = 0; M < g; ++M) {
let L = Math.max(0, Math.ceil((w - M) / f)), B = Math.min(m.outWidth, (m.inWidth + w - M) / f);
for (let z = 0; z < m.inChannels; ++z)
for (let U = 0; U < m.outChannels; ++U) {
let j = 0;
for (let H = 0; H < m.batchSize; ++H)
for (let X = F; X < O; ++X) {
let J = D + X * d - S;
for (let re = L; re < B; ++re) {
let ne = M + re * f - w;
x ? j += E.get(H, J, ne, z) * R.get(H, X, re, U) : j += E.get(H, z, J, ne) * R.get(H, U, X, re);
}
}
b.set(j, D, M, z, U);
}
}
}
return t10.makeTensorInfo(b.shape, b.dtype, b.values);
}
var q_ = { kernelName: $i, backendName: "cpu", kernelFunc: X8 };
function Y8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o;
Y([n, s], "conv2dBackpropInput");
let l = y.computeStrides(s.shape), m = y.computeStrides(n.shape), d = C.convertConv2DDataFormat(u), f = C.computeConv2DInfo(a, s.shape, i, 1, p, c, false, d), h = new tt(f.inShape, "float32"), g = h.values, x = t10.data.get(n.dataId).values, b = t10.data.get(s.dataId).values, [w, S, k] = l, { batchSize: _, filterHeight: E, filterWidth: R, inChannels: D, inHeight: F, inWidth: O, outChannels: M, outHeight: L, outWidth: B, strideHeight: z, strideWidth: U } = f;
d = f.dataFormat;
let j = E - 1 - f.padInfo.top, H = R - 1 - f.padInfo.left, X = d === "channelsLast", J = h.strides[0], re = X ? h.strides[1] : h.strides[2], ne = X ? h.strides[2] : 1, ee = X ? 1 : h.strides[1], oe = m[0], ie = X ? m[1] : m[2], le = X ? m[2] : 1, ye = X ? 1 : m[1];
for (let _e = 0; _e < _; ++_e)
for (let ve = 0; ve < D; ++ve)
for (let Fe = 0; Fe < F; ++Fe) {
let Pe = Fe - j, st = Math.max(0, Math.ceil(Pe / z)), lt = Math.min(L, (E + Pe) / z);
for (let We = 0; We < O; ++We) {
let mt = We - H, it = Math.max(0, Math.ceil(mt / U)), ht = Math.min(B, (R + mt) / U), gt = 0;
for (let Mt = st; Mt < lt; ++Mt) {
let Qr = Mt * z - Pe;
for (let or = it; or < ht; ++or) {
let Tt = or * U - mt, nr = oe * _e + ie * Mt + le * or, sr = w * (E - 1 - Qr) + S * (R - 1 - Tt) + k * ve;
for (let Zr = 0; Zr < M; ++Zr) {
let Jr = x[nr + ye * Zr], fr = b[sr + Zr];
gt += Jr * fr;
}
}
}
let Or = J * _e + re * Fe + ne * We + ee * ve;
g[Or] = gt;
}
}
return t10.makeTensorInfo(h.shape, h.dtype, h.values);
}
var j_ = { kernelName: tn, backendName: "cpu", kernelFunc: Y8 };
function Q8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o;
Y([n, s], "conv3d");
let u = C.computeConv3DInfo(n.shape, s.shape, a, p, i), { filterDepth: c, filterHeight: l, filterWidth: m, dilationDepth: d, dilationHeight: f, dilationWidth: h, padInfo: g } = u, x = g.front, b = g.left, w = g.top, S = new tt(u.outShape, n.dtype), k = t10.data.get(n.dataId).values, _ = t10.data.get(s.dataId).values, E = S.values, R = y.computeStrides(n.shape), D = y.computeStrides(s.shape);
for (let F = 0; F < u.batchSize; ++F) {
let O = F * R[0], M = F * S.strides[0];
for (let L = 0; L < u.outDepth; ++L) {
let B = M + L * S.strides[1], z = L * u.strideDepth - x;
for (let U = 0; U < c; ++U) {
let j = z + U * d;
if (j < 0 || j >= u.inDepth)
continue;
let H = U * D[0], X = O + j * R[1];
for (let J = 0; J < u.outHeight; ++J) {
let re = B + J * S.strides[2], ne = J * u.strideHeight - w;
for (let ee = 0; ee < l; ++ee) {
let oe = ne + ee * f;
if (oe < 0 || oe >= u.inHeight)
continue;
let ie = H + ee * D[1], le = X + oe * R[2];
for (let ye = 0; ye < u.outWidth; ++ye) {
let _e = re + ye * u.outChannels, ve = ye * u.strideWidth - b;
for (let Fe = 0; Fe < m; ++Fe) {
let Pe = ve + Fe * h;
if (Pe < 0 || Pe >= u.inWidth)
continue;
let st = ie + Fe * D[2], lt = le + Pe * u.inChannels, We = st;
for (let mt = 0; mt < u.inChannels; ++mt) {
let it = k[lt + mt];
for (let ht = 0; ht < u.outChannels; ++ht)
E[_e + ht] += it * _[We + ht];
We += u.outChannels;
}
}
}
}
}
}
}
}
return t10.makeTensorInfo(S.shape, S.dtype, S.values);
}
var X_ = { kernelName: rn, backendName: "cpu", kernelFunc: Q8 };
function Z8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o;
Y([n, s], "conv3dBackpropFilterV2");
let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = C.computeConv3DInfo(n.shape, p, a, 1, i), m = l.strideDepth, d = l.strideHeight, f = l.strideWidth, h = l.filterDepth, g = l.filterHeight, x = l.filterWidth, b = new tt(l.filterShape, "float32"), w = b.values, [S, k, _, E] = b.strides, R = t10.data.get(s.dataId).values, [D, F, O, M] = c, L = t10.data.get(n.dataId).values, [B, z, U, j] = u, H = l.padInfo.front, X = l.padInfo.left, J = l.padInfo.top;
for (let re = 0; re < h; ++re) {
let ne = Math.max(0, Math.ceil((H - re) / m)), ee = Math.min(l.outDepth, (l.inDepth + H - re) / m), oe = re * S;
for (let ie = 0; ie < g; ++ie) {
let le = Math.max(0, Math.ceil((J - ie) / d)), ye = Math.min(l.outHeight, (l.inHeight + J - ie) / d), _e = ie * k + oe;
for (let ve = 0; ve < x; ++ve) {
let Fe = Math.max(0, Math.ceil((X - ve) / f)), Pe = Math.min(l.outWidth, (l.inWidth + X - ve) / f), st = ve * _ + _e;
for (let lt = 0; lt < l.inChannels; ++lt) {
let We = lt * E + st;
for (let mt = 0; mt < l.outChannels; ++mt) {
let it = 0;
for (let ht = 0; ht < l.batchSize; ++ht) {
let gt = ht * B, Or = ht * D;
for (let Mt = ne; Mt < ee; ++Mt) {
let or = (re + Mt * m - H) * z + gt, Tt = Mt * F + Or;
for (let nr = le; nr < ye; ++nr) {
let Zr = (ie + nr * d - J) * U + or, Jr = nr * O + Tt;
for (let fr = Fe; fr < Pe; ++fr) {
let Mo = (ve + fr * f - X) * j + Zr, Vs = fr * M + Jr;
it += L[Mo + lt] * R[Vs + mt];
}
}
}
}
w[We + mt] = it;
}
}
}
}
}
return t10.makeTensorInfo(b.shape, b.dtype, b.values);
}
var Y_ = { kernelName: za, backendName: "cpu", kernelFunc: Z8 };
function J8(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o;
Y([n], "conv3dBackpropInputV2");
let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = C.computeConv3DInfo(p, s.shape, i, 1, a), m = new tt(l.inShape, "float32"), d = m.values, [f, h, g, x] = m.strides, b = t10.data.get(n.dataId).values, [w, S, k, _] = u, E = t10.data.get(s.dataId).values, [R, D, F, O] = c, { batchSize: M, filterDepth: L, filterHeight: B, filterWidth: z, inChannels: U, inDepth: j, inHeight: H, inWidth: X, outChannels: J, outDepth: re, outHeight: ne, outWidth: ee, strideDepth: oe, strideHeight: ie, strideWidth: le } = l, ye = L - 1 - l.padInfo.front, _e = B - 1 - l.padInfo.top, ve = z - 1 - l.padInfo.left;
for (let Fe = 0; Fe < M; ++Fe)
for (let Pe = 0; Pe < U; ++Pe)
for (let st = 0; st < j; ++st) {
let lt = st - ye, We = Math.max(0, Math.ceil(lt / oe)), mt = Math.min(re, (L + lt) / oe);
for (let it = 0; it < H; ++it) {
let ht = it - _e, gt = Math.max(0, Math.ceil(ht / ie)), Or = Math.min(ne, (B + ht) / ie);
for (let Mt = 0; Mt < X; ++Mt) {
let Qr = Mt - ve, or = Math.max(0, Math.ceil(Qr / le)), Tt = Math.min(ee, (z + Qr) / le), nr = 0;
for (let sr = We; sr < mt; ++sr) {
let Zr = sr * oe - lt;
for (let Jr = gt; Jr < Or; ++Jr) {
let fr = Jr * ie - ht;
for (let Fa = or; Fa < Tt; ++Fa) {
let Mo = Fa * le - Qr, Vs = w * Fe + S * sr + k * Jr + _ * Fa, Xt = R * (L - 1 - Zr) + D * (B - 1 - fr) + F * (z - 1 - Mo) + O * Pe;
for (let Pa = 0; Pa < J; ++Pa) {
let el = b[Vs + Pa], tl = E[Xt + Pa];
nr += el * tl;
}
}
}
}
d[f * Fe + h * st + g * it + x * Mt + Pe] = nr;
}
}
}
return t10.makeTensorInfo(m.shape, m.dtype, m.values);
}
var Q_ = { kernelName: on, backendName: "cpu", kernelFunc: J8 };
var eY = Ie(nn, (r) => Math.cos(r));
var Z_ = { kernelName: nn, backendName: "cpu", kernelFunc: eY };
var tY = Ie(sn, (r) => Math.cosh(r));
var J_ = { kernelName: sn, backendName: "cpu", kernelFunc: tY };
function rY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, [c, l, m, d] = n.shape, f = s.shape[0], [h, g] = i, x = me([f, h, g, d], "float32"), b = t10.data.get(s.dataId).values, w = t10.data.get(a.dataId).values, S = t10.data.get(n.dataId).values, k = y.computeStrides(n.shape), _ = y.computeStrides(x.shape);
for (let E = 0; E < f; E++) {
let R = E * 4, D = b[R], F = b[R + 1], O = b[R + 2], M = b[R + 3], L = w[E];
if (L >= c)
continue;
let B = h > 1 ? (O - D) * (l - 1) / (h - 1) : 0, z = g > 1 ? (M - F) * (m - 1) / (g - 1) : 0;
for (let U = 0; U < h; U++) {
let j = h > 1 ? D * (l - 1) + U * B : 0.5 * (D + O) * (l - 1);
if (j < 0 || j > l - 1) {
for (let H = 0; H < g; H++)
for (let X = 0; X < d; X++) {
let J = X + H * _[2] + U * _[1] + E * _[0];
x.values[J] = u;
}
continue;
}
if (p === "bilinear") {
let H = Math.floor(j), X = Math.ceil(j), J = j - H;
for (let re = 0; re < g; re++) {
let ne = g > 1 ? F * (m - 1) + re * z : 0.5 * (F + M) * (m - 1);
if (ne < 0 || ne > m - 1) {
for (let le = 0; le < d; le++) {
let ye = le + re * _[2] + U * _[1] + E * _[0];
x.values[ye] = u;
}
continue;
}
let ee = Math.floor(ne), oe = Math.ceil(ne), ie = ne - ee;
for (let le = 0; le < d; le++) {
let ye = le + ee * k[2] + H * k[1] + L * k[0], _e = S[ye];
ye = le + oe * k[2] + H * k[1] + L * k[0];
let ve = S[ye];
ye = le + ee * k[2] + X * k[1] + L * k[0];
let Fe = S[ye];
ye = le + oe * k[2] + X * k[1] + L * k[0];
let Pe = S[ye], st = _e + (ve - _e) * ie, lt = Fe + (Pe - Fe) * ie;
ye = le + re * _[2] + U * _[1] + E * _[0], x.values[ye] = st + (lt - st) * J;
}
}
} else
for (let H = 0; H < g; ++H) {
let X = g > 1 ? F * (m - 1) + H * z : 0.5 * (F + M) * (m - 1);
if (X < 0 || X > m - 1) {
for (let ne = 0; ne < d; ne++) {
let ee = ne + H * _[2] + U * _[1] + E * _[0];
x.values[ee] = u;
}
continue;
}
let J = Math.round(X), re = Math.round(j);
for (let ne = 0; ne < d; ne++) {
let ee = ne + J * k[2] + re * k[1] + L * k[0], oe = ne + H * _[2] + U * _[1] + E * _[0];
x.values[oe] = S[ee];
}
}
}
}
return t10.makeTensorInfo(x.shape, x.dtype, x.values);
}
var e$ = { kernelName: pn, backendName: "cpu", kernelFunc: rY };
function oY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
Y(n, "cumprod");
let p = C.getAxesPermutation([s], n.shape.length), u = n;
p != null && (u = St({ inputs: { x: n }, backend: t10, attrs: { perm: p } }));
let c = C.getInnerMostAxes(1, n.shape.length)[0];
if (c !== u.shape.length - 1)
throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`);
let l = dt(u.dtype, "int32"), m = y.makeOnesTypedArray(y.sizeFromShape(u.shape), l), d = t10.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b;
for (let x = 0; x < d.length; x += f)
for (let b = 0; b < f; b++) {
let w = h(x, b);
if (b === 0)
m[w] = a ? 1 : d[w];
else {
let S = h(x, b - 1);
m[w] = a ? d[S] * m[S] : d[w] * m[S];
}
}
let g = t10.makeTensorInfo(u.shape, l, m);
if (p != null) {
let x = C.getUndoAxesPermutation(p), b = St({ inputs: { x: g }, backend: t10, attrs: { perm: x } });
return t10.disposeIntermediateTensorInfo(g), t10.disposeIntermediateTensorInfo(u), b;
}
return g;
}
var t$ = { kernelName: an, backendName: "cpu", kernelFunc: oY };
function nY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
Y(n, "cumsum");
let p = C.getAxesPermutation([s], n.shape.length), u = n;
p != null && (u = St({ inputs: { x: n }, backend: t10, attrs: { perm: p } }));
let c = C.getInnerMostAxes(1, n.shape.length)[0];
if (c !== u.shape.length - 1)
throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`);
let l = dt(u.dtype, "int32"), m = y.makeZerosTypedArray(y.sizeFromShape(u.shape), l), d = t10.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b;
for (let x = 0; x < d.length; x += f)
for (let b = 0; b < f; b++) {
let w = h(x, b);
if (b === 0)
m[w] = a ? 0 : d[w];
else {
let S = h(x, b - 1);
m[w] = a ? d[S] + m[S] : d[w] + m[S];
}
}
let g = t10.makeTensorInfo(u.shape, l, m);
if (p != null) {
let x = C.getUndoAxesPermutation(p), b = St({ inputs: { x: g }, backend: t10, attrs: { perm: x } });
return t10.disposeIntermediateTensorInfo(g), t10.disposeIntermediateTensorInfo(u), b;
}
return g;
}
var r$ = { kernelName: un, backendName: "cpu", kernelFunc: nY };
function sY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o;
if (n.shape.length === 1) {
let p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = yc(p, u, s.dtype, s.shape, a);
return t10.makeTensorInfo([a], s.dtype, c);
} else if (n.shape.length === 2) {
let p = t10.bufferSync(n), u = t10.bufferSync(s), c = If(p, u, a, i);
return t10.makeTensorInfo(c.shape, s.dtype, c.values);
}
throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`);
}
var o$ = { kernelName: Qs, backendName: "cpu", kernelFunc: sY };
function aY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o;
y.assert(a === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);
let i = n.shape[0], p = n.shape[1], u = n.shape[2], c = n.shape[3], l = p * s, m = u * s, d = c / (s * s), f = t10.data.get(n.dataId).values, h = new Float32Array(i * l * m * d), g = 0;
for (let x = 0; x < i; ++x)
for (let b = 0; b < l; ++b) {
let w = Math.floor(b / s), S = b % s;
for (let k = 0; k < m; ++k) {
let _ = Math.floor(k / s), E = k % s, R = (S * s + E) * d;
for (let D = 0; D < d; ++D) {
let O = D + R + c * (_ + u * (w + p * x));
h[g++] = f[O];
}
}
}
return t10.makeTensorInfo([i, l, m, d], n.dtype, h);
}
var n$ = { kernelName: cn, backendName: "cpu", kernelFunc: aY };
function rI(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o;
Y([n, s], "depthwiseConv2DNative");
let c = y.computeStrides(n.shape), l = y.computeStrides(s.shape), m = p;
m == null && (m = [1, 1]), y.assert(C.eitherStridesOrDilationsAreOne(a, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);
let d = C.computeConv2DInfo(n.shape, s.shape, a, m, i, u, true), { filterHeight: f, filterWidth: h, dilationHeight: g, dilationWidth: x, padInfo: b } = d, w = b.left, S = b.top, k = d.outChannels / d.inChannels, _ = new tt(d.outShape, n.dtype), E = t10.data.get(n.dataId).values, R = t10.data.get(s.dataId).values, D = _.values;
for (let F = 0; F < d.batchSize; ++F) {
let O = F * c[0], M = F * _.strides[0];
for (let L = 0; L < d.outHeight; ++L) {
let B = M + L * _.strides[1], z = L * d.strideHeight - S;
for (let U = 0; U < f; ++U) {
let j = z + U * g;
if (j < 0 || j >= d.inHeight)
continue;
let H = U * l[0], X = O + j * c[1];
for (let J = 0; J < d.outWidth; ++J) {
let re = B + J * _.strides[2], ne = J * d.strideWidth - w;
for (let ee = 0; ee < h; ++ee) {
let oe = ne + ee * x;
if (oe < 0 || oe >= d.inWidth)
continue;
let ie = H + ee * l[1], le = X + oe * d.inChannels, ye = re, _e = ie;
for (let ve = 0; ve < d.inChannels; ++ve) {
let Fe = E[le + ve];
for (let Pe = 0; Pe < k; ++Pe)
D[ye + Pe] += Fe * R[_e + Pe];
ye += k, _e += k;
}
}
}
}
}
}
return t10.makeTensorInfo(_.shape, _.dtype, _.values);
}
var s$ = { kernelName: ln, backendName: "cpu", kernelFunc: rI };
function iY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o;
Y([n, s], "depthwiseConv2dNativeBackpropFilter");
let l = C.computeConv2DInfo(n.shape, c, a, i, p, u, true), { strideHeight: m, strideWidth: d, filterHeight: f, filterWidth: h } = l, g = new tt(l.filterShape, "float32"), x = l.padInfo.left, b = l.padInfo.top, w = l.outChannels / l.inChannels, S = t10.data.get(n.dataId).values, k = new tt(n.shape, n.dtype, S), _ = t10.data.get(s.dataId).values, E = new tt(s.shape, s.dtype, _);
for (let R = 0; R < f; ++R) {
let D = Math.max(0, Math.ceil((b - R) / m)), F = Math.min(l.outHeight, (l.inHeight + b - R) / m);
for (let O = 0; O < h; ++O) {
let M = Math.max(0, Math.ceil((x - O) / d)), L = Math.min(l.outWidth, (l.inWidth + x - O) / d);
for (let B = 0; B < l.outChannels; ++B) {
let z = Math.trunc(B / w), U = B % w, j = 0;
for (let H = 0; H < l.batchSize; ++H)
for (let X = D; X < F; ++X) {
let J = R + X * m - b;
for (let re = M; re < L; ++re) {
let ne = O + re * d - x;
j += k.get(H, J, ne, z) * E.get(H, X, re, B);
}
}
g.set(j, R, O, z, U);
}
}
}
return t10.makeTensorInfo(g.shape, g.dtype, g.values);
}
var a$ = { kernelName: Ei, backendName: "cpu", kernelFunc: iY };
function uY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o;
Y([n, s], "depthwiseConv2DNativeBackpropInput");
let l = y.computeStrides(n.shape), m = y.computeStrides(s.shape), d = C.computeConv2DInfo(c, s.shape, a, i, p, u, true), f = new tt(d.inShape, "float32"), h = f.values, [g, x, b] = f.strides, w = t10.data.get(n.dataId).values, [S, k, _] = l, E = t10.data.get(s.dataId).values, [R, D, F] = m, { batchSize: O, filterHeight: M, filterWidth: L, inChannels: B, inHeight: z, inWidth: U, outChannels: j, outHeight: H, outWidth: X, strideHeight: J, strideWidth: re } = d, ne = M - 1 - d.padInfo.top, ee = L - 1 - d.padInfo.left, oe = j / B;
for (let ie = 0; ie < O; ++ie)
for (let le = 0; le < B; ++le)
for (let ye = 0; ye < z; ++ye) {
let _e = ye - ne, ve = Math.max(0, Math.ceil(_e / J)), Fe = Math.min(H, (M + _e) / J);
for (let Pe = 0; Pe < U; ++Pe) {
let st = Pe - ee, lt = Math.max(0, Math.ceil(st / re)), We = Math.min(X, (L + st) / re), mt = 0;
for (let it = ve; it < Fe; ++it) {
let ht = it * J - _e;
for (let gt = lt; gt < We; ++gt) {
let Or = gt * re - st, Mt = S * ie + k * it + _ * gt, Qr = R * (M - 1 - ht) + D * (L - 1 - Or) + F * le;
for (let or = 0; or < oe; ++or) {
let Tt = le * oe + or, nr = w[Mt + Tt], sr = E[Qr + or];
mt += nr * sr;
}
}
}
h[g * ie + x * ye + b * Pe + le] = mt;
}
}
return t10.makeTensorInfo(f.shape, f.dtype, f.values);
}
var i$ = { kernelName: Ri, backendName: "cpu", kernelFunc: uY };
function pY(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t10.data.get(o.dataId).values, a = me([n, n], o.dtype), i = a.values;
for (let u = 0; u < s.length; u++)
i[u * n + u] = s[u];
let p = [...o.shape, ...o.shape];
return t10.makeTensorInfo(p, a.dtype, a.values);
}
var u$ = { kernelName: Zs, backendName: "cpu", kernelFunc: pY };
var p$ = { kernelName: mn, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => {
let { x: o, filter: n } = r, { strides: s, pad: a, dilations: i } = t10, p = e, u = p.data.get(o.dataId).values, c = o.shape.length, l = p.data.get(n.dataId).values, m = n.shape.length, { batchSize: d, inHeight: f, inWidth: h, inChannels: g, outHeight: x, outWidth: b, padInfo: w, strideHeight: S, strideWidth: k, filterHeight: _, filterWidth: E, dilationHeight: R, dilationWidth: D, outShape: F } = C.computeDilation2DInfo(o.shape, n.shape, s, a, "NHWC", i), O = y.sizeFromShape(F), M = F.length, L = y.getArrayFromDType(o.dtype, O);
for (let z = 0; z < d; ++z)
for (let U = 0; U < x; ++U) {
let j = U * S - w.top;
for (let H = 0; H < b; ++H) {
let X = H * k - w.left;
for (let J = 0; J < g; ++J) {
let re = Number.MIN_SAFE_INTEGER;
for (let ee = 0; ee < _; ++ee) {
let oe = j + ee * R;
if (oe >= 0 && oe < f)
for (let ie = 0; ie < E; ++ie) {
let le = X + ie * D;
if (le >= 0 && le < h) {
let ye = y.locToIndex([z, oe, le, J], c, y.computeStrides(o.shape)), _e = y.locToIndex([ee, ie, J], m, y.computeStrides(n.shape)), ve = u[ye] + l[_e];
ve > re && (re = ve);
}
}
}
let ne = y.locToIndex([z, U, H, J], M, y.computeStrides(F));
L[ne] = re;
}
}
}
return { dataId: p.write(y.toTypedArray(L, o.dtype), F, o.dtype), shape: F, dtype: o.dtype };
} };
var c$ = { kernelName: Ai, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => {
let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t10, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: w, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = C.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p);
y.assert(s.rank === D.length, () => `Error in ${Ai}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);
let F = y.toNestedArray(D, u.data.get(s.dataId).values), O = y.makeZerosNestedTypedArray(n.shape, n.dtype);
for (let L = 0; L < m; ++L)
for (let B = 0; B < g; ++B) {
let z = B * w - b.top;
for (let U = 0; U < x; ++U) {
let j = U * S - b.left;
for (let H = 0; H < h; ++H) {
let X = Number.MIN_SAFE_INTEGER, J = 0, re = 0;
for (let ne = 0; ne < k; ++ne) {
let ee = z + ne * E;
if (ee >= 0 && ee < d)
for (let oe = 0; oe < _; ++oe) {
let ie = j + oe * R;
if (ie >= 0 && ie < f) {
let le = c[L][ee][ie][H] + l[ne][oe][H];
le > X && (X = le, J = ne, re = oe);
}
}
}
O[J][re][H] += F[L][B][U][H];
}
}
}
return { dataId: u.write(y.toTypedArray(O, o.dtype), n.shape, n.dtype), shape: n.shape, dtype: n.dtype };
} };
var l$ = { kernelName: Di, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => {
let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t10, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: w, strideWidth: S, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: D } = C.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p);
y.assert(s.rank === D.length, () => `Error in ${Di}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);
let F = y.toNestedArray(D, u.data.get(s.dataId).values), O = y.makeZerosNestedTypedArray(o.shape, o.dtype);
for (let L = 0; L < m; ++L)
for (let B = 0; B < g; ++B) {
let z = B * w - b.top;
for (let U = 0; U < x; ++U) {
let j = U * S - b.left;
for (let H = 0; H < h; ++H) {
let X = Number.MIN_SAFE_INTEGER, J = z < 0 ? 0 : z, re = j < 0 ? 0 : j;
for (let ne = 0; ne < k; ++ne) {
let ee = z + ne * E;
if (ee >= 0 && ee < d)
for (let oe = 0; oe < _; ++oe) {
let ie = j + oe * R;
if (ie >= 0 && ie < f) {
let le = c[L][ee][ie][H] + l[ne][oe][H];
le > X && (X = le, J = ee, re = ie);
}
}
}
O[L][J][re][H] += F[L][B][U][H];
}
}
}
return { dataId: u.write(y.toTypedArray(O, o.dtype), o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
function li(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
Y(n, "sum");
let i;
n.dtype === "bool" ? i = _o({ inputs: { x: n }, backend: t10, attrs: { dtype: "int32" } }) : i = lr({ inputs: { x: n }, backend: t10 });
let p = i.shape.length, u = y.parseAxisParam(s, i.shape), c = C.getAxesPermutation(u, p), l = u, m = i;
c != null && (m = St({ inputs: { x: i }, backend: t10, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, p)), C.assertAxesAreInnerMostDims("sum", l, m.shape.length);
let [d, f] = C.computeOutAndReduceShapes(m.shape, l), h = C.upcastType(m.dtype, "int32"), g = gc(t10, d, h), x = y.sizeFromShape(f), b = t10.data.get(g.dataId).values, w = t10.data.get(m.dataId).values;
for (let S = 0; S < b.length; ++S) {
let k = S * x, _ = 0;
for (let E = 0; E < x; ++E)
_ += w[k + E];
b[S] = _;
}
if (a) {
let S = C.expandShapeToKeepDim(g.shape, u), k = g;
g = Ve({ inputs: { x: g }, backend: t10, attrs: { shape: S } }), t10.disposeIntermediateTensorInfo(k);
}
return t10.disposeIntermediateTensorInfo(i), c != null && t10.disposeIntermediateTensorInfo(m), g;
}
var m$ = { kernelName: ys, backendName: "cpu", kernelFunc: li };
function cY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = C.decodeEinsumEquation(n, s.length);
C.checkEinsumDimSizes(a.length, p, s);
let { path: u, steps: c } = C.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = [];
for (let h = 0; h < l; ++h) {
for (let g of c[h]) {
let { permutationIndices: x, expandDims: b } = C.getEinsumPermutation(d, p[g]), w;
C.isIdentityPermutation(x) ? w = s[g] : (w = St({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(w));
let S = w.shape.slice();
for (let k = 0; k < b.length; ++k)
S.splice(b[k], 0, 1);
y.arraysEqual(w.shape, S) || (w = Ve({ inputs: { x: w }, backend: t10, attrs: { shape: S } }), f.push(w)), m === null ? m = w : (m = sp({ inputs: { a: w, b: m }, backend: t10 }), f.push(m));
}
h < l - 1 && (u[h] >= 0 && (m = li({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--);
}
for (let h of f)
h !== m && t10.disposeIntermediateTensorInfo(h);
return m;
}
var d$ = { kernelName: Fi, backendName: "cpu", kernelFunc: cY };
function lY(r) {
let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e;
Y([o, n], "eluGrad");
let s = new Float32Array(y.sizeFromShape(n.shape)), a = t10.data.get(n.dataId).values, i = t10.data.get(o.dataId).values;
for (let p = 0; p < a.length; ++p) {
let u = a[p];
u >= 0 ? s[p] = i[p] : s[p] = i[p] * (u + 1);
}
return t10.makeTensorInfo(n.shape, "float32", s);
}
var f$ = { kernelName: Va, backendName: "cpu", kernelFunc: lY };
var mY = C.ERF_P;
var dY = C.ERF_A1;
var fY = C.ERF_A2;
var hY = C.ERF_A3;
var gY = C.ERF_A4;
var xY = C.ERF_A5;
var yY = Ie(Wa, (r) => {
let e = Math.sign(r), t10 = Math.abs(r), o = 1 / (1 + mY * t10);
return e * (1 - ((((xY * o + gY) * o + hY) * o + fY) * o + dY) * o * Math.exp(-t10 * t10));
});
var h$ = { kernelName: Wa, backendName: "cpu", kernelFunc: yY };
function vc(r) {
let { inputs: e, backend: t10, attrs: o } = r, { input: n } = e, { dim: s } = o, a = n.shape.length, i = n.shape.slice(), p = s;
return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Ve({ inputs: { x: n }, backend: t10, attrs: { shape: i } });
}
var g$ = { kernelName: Js, backendName: "cpu", kernelFunc: vc };
var bY = ze((r, e) => r / e);
var zl = je(dn, bY);
var Vl = { kernelName: dn, backendName: "cpu", kernelFunc: zl };
function Lf(r, e, t10) {
let o = r.shape, n = o[0], s = o[1], a = t10.data.get(r.dataId), i = a.complexTensorInfos.real, p = a.complexTensorInfos.imag, u = [n, s], c = y.sizeFromShape(u), l = y.getTypedArrayFromDType("float32", c), m = y.getTypedArrayFromDType("float32", c);
for (let g = 0; g < n; g++) {
let x = Eo({ inputs: { x: i }, backend: t10, attrs: { begin: [g, 0], size: [1, s] } }), b = Eo({ inputs: { x: p }, backend: t10, attrs: { begin: [g, 0], size: [1, s] } }), w = Kt({ inputs: { real: x, imag: b }, backend: t10 }), { real: S, imag: k } = CY(w, e, t10), _ = C.mergeRealAndImagArrays(S, k);
for (let E = 0; E < s; E++) {
let R = C.getComplexWithIndex(_, E);
l[g * s + E] = R.real, m[g * s + E] = R.imag;
}
t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(b), t10.disposeIntermediateTensorInfo(w);
}
let d = t10.makeTensorInfo(u, "float32", l), f = t10.makeTensorInfo(u, "float32", m), h = Kt({ inputs: { real: d, imag: f }, backend: t10 });
return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), h;
}
function CY(r, e, t10) {
let o = y.sizeFromShape(r.shape), n = t10.data.get(r.dataId), s = t10.data.get(n.complexTensorInfos.real.dataId).values, a = t10.data.get(n.complexTensorInfos.imag.dataId).values;
if (wY(o)) {
let i = oI(s, a, o, e, t10), p = [r.shape[0], r.shape[1]];
if (e) {
let u = t10.makeTensorInfo(p, "float32", i.real), c = t10.makeTensorInfo(p, "float32", i.imag), l = t10.makeTensorInfo([], "float32", y.createScalarValue(o, "float32")), m = lr({ inputs: { x: l }, backend: t10 }), d = Vl.kernelFunc({ inputs: { a: u, b: l }, backend: t10 }), f = Vl.kernelFunc({ inputs: { a: c, b: m }, backend: t10 }), h = t10.data.get(d.dataId).values, g = t10.data.get(f.dataId).values;
return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), { real: h, imag: g };
}
return i;
} else {
let i = C.mergeRealAndImagArrays(s, a), p = SY(i, o, e);
return C.splitRealAndImagArrays(p);
}
}
function wY(r) {
return (r & r - 1) === 0;
}
function oI(r, e, t10, o, n) {
if (t10 === 1)
return { real: r, imag: e };
let s = C.mergeRealAndImagArrays(r, e), a = t10 / 2, i = C.complexWithEvenIndex(s), p = i.real, u = i.imag, c = [p.length], l = n.makeTensorInfo(c, "float32", p), m = n.makeTensorInfo(c, "float32", u), d = Kt({ inputs: { real: l, imag: m }, backend: n }), f = C.complexWithOddIndex(s), h = f.real, g = f.imag, x = [h.length], b = n.makeTensorInfo(x, "float32", h), w = n.makeTensorInfo(x, "float32", g), S = Kt({ inputs: { real: b, imag: w }, backend: n }), k = oI(p, u, a, o, n), _ = k.real, E = k.imag, R = [_.length], D = n.makeTensorInfo(R, "float32", _), F = n.makeTensorInfo(R, "float32", E), O = Kt({ inputs: { real: D, imag: F }, backend: n }), M = oI(h, g, a, o, n), L = M.real, B = M.imag, z = [L.length], U = n.makeTensorInfo(z, "float32", L), j = n.makeTensorInfo(z, "float32", B), H = Kt({ inputs: { real: U, imag: j }, backend: n }), X = C.exponents(t10, o), J = [X.real.length], re = n.makeTensorInfo(J, "float32", X.real), ne = n.makeTensorInfo(J, "float32", X.imag), ee = Kt({ inputs: { real: re, imag: ne }, backend: n }), oe = sp({ inputs: { a: ee, b: H }, backend: n }), ie = _a({ inputs: { a: O, b: oe }, backend: n }), le = Ll({ inputs: { a: O, b: oe }, backend: n }), ye = To({ inputs: { input: ie }, backend: n }), _e = To({ inputs: { input: le }, backend: n }), ve = $a({ inputs: { input: ie }, backend: n }), Fe = $a({ inputs: { input: le }, backend: n }), Pe = mu({ inputs: [ye, _e], backend: n, attrs: { axis: 0 } }), st = mu({ inputs: [ve, Fe], backend: n, attrs: { axis: 0 } }), lt = n.data.get(Pe.dataId).values, We = n.data.get(st.dataId).values;
return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(w), n.disposeIntermediateTensorInfo(S), n.disposeIntermediateTensorInfo(D), n.disposeIntermediateTensorInfo(F), n.disposeIntermediateTensorInfo(O), n.disposeIntermediateTensorInfo(U), n.disposeIntermediateTensorInfo(j), n.disposeIntermediateTensorInfo(H), n.disposeIntermediateTensorInfo(re), n.disposeIntermediateTensorInfo(ne), n.disposeIntermediateTensorInfo(ee), n.disposeIntermediateTensorInfo(oe), n.disposeIntermediateTensorInfo(ie), n.disposeIntermediateTensorInfo(le), n.disposeIntermediateTensorInfo(ye), n.disposeIntermediateTensorInfo(ve), n.disposeIntermediateTensorInfo(_e), n.disposeIntermediateTensorInfo(Fe), n.disposeIntermediateTensorInfo(Pe), n.disposeIntermediateTensorInfo(st), { real: lt, imag: We };
}
function SY(r, e, t10) {
let o = new Float32Array(e * 2);
for (let n = 0; n < e; n++) {
let s = 0, a = 0;
for (let i = 0; i < e; i++) {
let p = C.exponent(n * i, e, t10), u = C.getComplexWithIndex(r, i);
s += u.real * p.real - u.imag * p.imag, a += u.real * p.imag + u.imag * p.real;
}
t10 && (s /= e, a /= e), C.assignToTypedArray(o, s, a, n);
}
return o;
}
function IY(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Ve({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = Lf(i, false, t10), u = Ve({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } });
return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u;
}
var x$ = { kernelName: Pi, backendName: "cpu", kernelFunc: IY };
function Wl(r) {
let { backend: e, attrs: t10 } = r, { shape: o, value: n, dtype: s } = t10, a = s || y.inferDtype(n), i = y.getArrayFromDType(a, y.sizeFromShape(o));
return vY(i, n, a), e.makeTensorInfo(o, a, i);
}
var y$ = { kernelName: ea, backendName: "cpu", kernelFunc: Wl };
function vY(r, e, t10) {
r.fill(e);
}
var b$ = { kernelName: yn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
let { image: o } = r, n = t10, s = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [a, i, p, u] = o.shape, c = n.data.get(o.dataId).values;
for (let m = 0; m < a; m++) {
let d = m * p * i * u;
for (let f = 0; f < i; f++) {
let h = f * (p * u);
for (let g = 0; g < p; g++) {
let x = g * u;
for (let b = 0; b < u; b++) {
let w = Math.round(p - g - 1), S = d + h + x + b, k = c[S];
if (w >= 0 && w < p) {
let _ = w * u, E = d + h + _ + b;
k = c[E];
}
s[S] = k;
}
}
}
}
return { dataId: n.write(s, o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
function kY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = tI({ inputs: { x: n, filter: s }, backend: t10, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } });
if (a) {
let g = h;
if (c === "NCHW" && a.shape.length === 1 && a.shape[0] !== 1) {
let x = Ve({ inputs: { x: a }, backend: t10, attrs: { shape: [a.shape[0], 1, 1] } });
h = _a({ inputs: { a: h, b: x }, backend: t10 }), t10.disposeIntermediateTensorInfo(x);
} else
h = _a({ inputs: { a: h, b: a }, backend: t10 });
t10.disposeIntermediateTensorInfo(g);
}
if (d) {
let g = h;
if (c === "NCHW" && d === "prelu" && i.shape.length === 1 && i.shape[0] !== 1) {
let x = Ve({ inputs: { x: i }, backend: t10, attrs: { shape: [i.shape[0], 1, 1] } });
h = mp(t10, h, d, x, f), t10.disposeIntermediateTensorInfo(x);
} else
h = mp(t10, h, d, i, f);
t10.disposeIntermediateTensorInfo(g);
}
return h;
}
var C$ = { kernelName: Co, backendName: "cpu", kernelFunc: kY };
function NY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = rI({ inputs: { x: n, filter: s }, backend: t10, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } });
if (a) {
let g = h;
h = _a({ inputs: { a: h, b: a }, backend: t10 }), t10.disposeIntermediateTensorInfo(g);
}
if (d) {
let g = h;
h = mp(t10, h, d, i, f), t10.disposeIntermediateTensorInfo(g);
}
return h;
}
var w$ = { kernelName: wo, backendName: "cpu", kernelFunc: NY };
function TY(r) {
let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = y.sizeFromShape(o.shape), a = n.shape, i = a[a.length - 1], [p, u, c, l] = C.prepareAndValidate(o, n);
if (u === 0)
return t10.makeTensorInfo(p, o.dtype, []);
let m = t10.data.get(n.dataId).values, d = t10.bufferSync(o), f = vf(m, d, o.dtype, u, i, c, l, o.shape, s);
return t10.makeTensorInfo(p, o.dtype, f.values);
}
var S$ = { kernelName: Sn, backendName: "cpu", kernelFunc: TY };
function _Y(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o;
Y([n, s], "gatherV2");
let p = y.parseAxisParam(a, n.shape)[0], u = t10.data.get(s.dataId).values, c = n.shape[p];
for (let S = 0; S < u.length; ++S) {
let k = u[S];
y.assert(k <= c - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${c - 1}]`);
}
let l = i;
i == null && (l = 0);
let m = y.sizeFromShape(s.shape), d = C.segment_util.collectGatherOpShapeInfo(n, s, p, l), f = Ve({ inputs: { x: n }, backend: t10, attrs: { shape: [d.batchSize, d.outerSize, d.dimSize, d.sliceSize] } }), h = Ve({ inputs: { x: s }, backend: t10, attrs: { shape: [d.batchSize, m / d.batchSize] } }), g = [d.batchSize, d.outerSize, m / d.batchSize, d.sliceSize], x = t10.bufferSync(h), b = t10.bufferSync(f), w = kf(b, x, g);
return t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), t10.makeTensorInfo(d.outputShape, w.dtype, w.values);
}
var I$ = { kernelName: ta, backendName: "cpu", kernelFunc: _Y };
function $Y(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Ve({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = Lf(i, true, t10), u = Ve({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } });
return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u;
}
var v$ = { kernelName: Oi, backendName: "cpu", kernelFunc: $Y };
var EY = Ie(kn, (r) => Number.isFinite(r) ? 1 : 0, "bool");
var k$ = { kernelName: kn, backendName: "cpu", kernelFunc: EY };
var RY = Ie(Nn, (r) => Math.abs(r) === 1 / 0 ? 1 : 0, "bool");
var N$ = { kernelName: Nn, backendName: "cpu", kernelFunc: RY };
var DY = Ie(Tn, (r) => Number.isNaN(r) ? 1 : 0, "bool");
var T$ = { kernelName: Tn, backendName: "cpu", kernelFunc: DY };
function AY(r) {
let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = Nf(o, n, s);
return e.makeTensorInfo([a.length], "float32", a);
}
var _$ = { kernelName: Rn, backendName: "cpu", kernelFunc: AY };
var FY = Ie(An, (r) => Math.log1p(r));
var $$ = { kernelName: An, backendName: "cpu", kernelFunc: FY };
var PY = ze((r, e) => r && e);
var OY = je(Fn, PY, null, "bool");
var E$ = { kernelName: Fn, backendName: "cpu", kernelFunc: OY };
var MY = Ie(Pn, (r) => r ? 0 : 1, "bool");
var R$ = { kernelName: Pn, backendName: "cpu", kernelFunc: MY };
var LY = ze((r, e) => r || e);
var BY = je(On, LY, null, "bool");
var D$ = { kernelName: On, backendName: "cpu", kernelFunc: BY };
function zY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o;
Y(n, "LRN");
let u = n.shape[3], c = u - 1, l = t10.data.get(n.dataId).values, m = y.sizeFromShape(n.shape), d = new Float32Array(m);
function f(h) {
let g = h % u, x = h - g + Math.max(0, g - s), b = h - g + Math.min(g + s, c), w = 0;
for (; x <= b; x++) {
let S = l[x];
w += S * S;
}
return w;
}
for (let h = 0; h < m; h++) {
let g = f(h), x = l[h] * Math.pow(a + i * g, -p);
d[h] = x;
}
return t10.makeTensorInfo(n.shape, n.dtype, d);
}
var A$ = { kernelName: Mn, backendName: "cpu", kernelFunc: zY };
function VY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o;
Y(a, "LRNGrad");
let l = y.sizeFromShape(a.shape), m = a.shape[3], d = t10.data.get(a.dataId).values, f = t10.data.get(n.dataId).values, h = t10.data.get(s.dataId).values, g = new Float32Array(l), x = l;
for (let b = 0; b < x; b++) {
let w = b % m, S = b - w + Math.max(0, w - i), k = b - w + Math.min(m, w + i + 1), _ = 0;
for (let E = S; E < k; E++)
_ += Math.pow(f[E], 2);
_ = u * _ + p;
for (let E = S; E < k; E++) {
let R = -2 * u * c * f[E] * h[b] / _;
b === E && (R += Math.pow(_, -c)), R *= d[b], g[E] += R;
}
}
return t10.makeTensorInfo(a.shape, n.dtype, g);
}
var F$ = { kernelName: Ua, backendName: "cpu", kernelFunc: VY };
function nI(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = t10, p = n.shape, u = p.length, c = y.parseAxisParam(s, p), l = c, m = C.getAxesPermutation(l, u), d = i.data.get(n.dataId).values;
if (m != null) {
let S = new Array(u);
for (let k = 0; k < S.length; k++)
S[k] = p[m[k]];
d = bc(d, p, n.dtype, m, S), l = C.getInnerMostAxes(l.length, u), p = S;
}
Y(n, "max"), C.assertAxesAreInnerMostDims("max", l, u);
let [f, h] = C.computeOutAndReduceShapes(p, l), g = y.sizeFromShape(h), x = Tf(d, g, f, n.dtype), b = i.write(x, f, n.dtype), w = f;
return a && (w = C.expandShapeToKeepDim(f, c)), { dataId: b, shape: w, dtype: n.dtype };
}
var P$ = { kernelName: Ln, backendName: "cpu", kernelFunc: nI };
function WY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
Y(n, "maxPool");
let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
y.assert(C.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
let c = C.computePool2DInfo(n.shape, s, a, u, i, p), l;
if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape))
l = lr({ inputs: { x: n }, backend: t10 });
else {
let m = t10.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Ic(m, n.shape, n.dtype, d, c, "max");
l = t10.makeTensorInfo(c.outShape, n.dtype, f.values);
}
return l;
}
var O$ = { kernelName: zn, backendName: "cpu", kernelFunc: WY };
function UY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o;
Y(n, "maxPool3d");
let c = C.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.data.get(n.dataId).values, m = Mf(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "max");
return t10.makeTensorInfo(m.shape, "float32", m.values);
}
var M$ = { kernelName: ra, backendName: "cpu", kernelFunc: UY };
function GY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o;
Y([n, s], "maxPool3DGrad");
let c = C.computePool3DInfo(s.shape, a, i, 1, p, u), l = t10.bufferSync(s), m = A_(l, c), d = c.strideDepth, f = c.strideHeight, h = c.strideWidth, g = c.dilationDepth, x = c.dilationHeight, b = c.dilationWidth, w = c.effectiveFilterDepth, S = c.effectiveFilterHeight, k = c.effectiveFilterWidth, _ = w - 1 - c.padInfo.front, E = k - 1 - c.padInfo.left, R = S - 1 - c.padInfo.top, D = me(s.shape, "float32"), F = t10.bufferSync(n);
for (let O = 0; O < c.batchSize; ++O)
for (let M = 0; M < c.inChannels; ++M)
for (let L = 0; L < c.inDepth; ++L)
for (let B = 0; B < c.inHeight; ++B)
for (let z = 0; z < c.inWidth; ++z) {
let U = L - _, j = B - R, H = z - E, X = 0;
for (let J = 0; J < w; J += g) {
let re = (U + J) / d;
if (!(re < 0 || re >= c.outDepth || Math.floor(re) !== re))
for (let ne = 0; ne < S; ne += x) {
let ee = (j + ne) / f;
if (!(ee < 0 || ee >= c.outHeight || Math.floor(ee) !== ee))
for (let oe = 0; oe < k; oe += b) {
let ie = (H + oe) / h;
if (ie < 0 || ie >= c.outWidth || Math.floor(ie) !== ie)
continue;
let le = w * S * k - 1 - m.get(O, re, ee, ie, M), ye = J * S * k + ne * k + oe, _e = le === ye ? 1 : 0;
if (_e === 0)
continue;
let ve = F.get(O, re, ee, ie, M);
X += ve * _e;
}
}
}
D.set(X, O, L, B, z, M);
}
return t10.makeTensorInfo(D.shape, D.dtype, D.values);
}
var L$ = { kernelName: Li, backendName: "cpu", kernelFunc: GY };
function HY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s;
Y([s, a], "maxPoolGrad");
let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = C.computePool2DInfo(i.shape, p, u, 1, c, l), d = t10.data.get(i.dataId).values, f = me(m.outShape, i.dtype, Of(d, i.shape, i.dtype, m).values), h = m.strideHeight, g = m.strideWidth, x = m.dilationHeight, b = m.dilationWidth, w = m.effectiveFilterHeight, S = m.effectiveFilterWidth, k = S - 1 - m.padInfo.left, _ = w - 1 - m.padInfo.top, E = me(i.shape, "float32"), R = t10.data.get(n.dataId).values, D = me(n.shape, "float32", R);
for (let F = 0; F < m.batchSize; ++F)
for (let O = 0; O < m.inChannels; ++O)
for (let M = 0; M < m.inHeight; ++M)
for (let L = 0; L < m.inWidth; ++L) {
let B = M - _, z = L - k, U = 0;
for (let j = 0; j < w; j += x) {
let H = (B + j) / h;
if (!(H < 0 || H >= m.outHeight || Math.floor(H) !== H))
for (let X = 0; X < S; X += b) {
let J = (z + X) / g;
if (J < 0 || J >= m.outWidth || Math.floor(J) !== J)
continue;
let re = w * S - 1 - f.get(F, H, J, O), ne = j * S + X, ee = re === ne ? 1 : 0;
if (ee === 0)
continue;
let oe = D.get(F, H, J, O);
U += oe * ee;
}
}
E.set(U, F, M, L, O);
}
return t10.makeTensorInfo(E.shape, E.dtype, E.values);
}
var B$ = { kernelName: Hp, backendName: "cpu", kernelFunc: HY };
function z$(r, e, t10, o, n) {
let s = y.computeStrides(e), a = Ic(r, e, t10, s, n, "max"), i = Of(r, e, t10, n, true, o);
return [a.values, i.values];
}
var V$ = { kernelName: Bi, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t10;
Y(o, "MaxPoolWithArgmax");
let u = p.data.get(o.dataId).values, c = C.computePool2DInfo(o.shape, n, s, [1, 1], a), [l, m] = z$(u, o.shape, o.dtype, i, c), d = p.write(l, c.outShape, o.dtype), f = p.write(m, c.outShape, o.dtype);
return [{ dataId: d, shape: c.outShape, dtype: o.dtype }, { dataId: f, shape: c.outShape, dtype: "int32" }];
} };
function KY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = y.parseAxisParam(s, n.shape), u = C.computeOutAndReduceShapes(n.shape, i)[1], c = y.sizeFromShape(u), l = [], m = t10.makeTensorInfo([], "float32", new Float32Array([c]));
l.push(m);
let d = _o({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } });
l.push(d);
let f = zl({ inputs: { a: d, b: m }, backend: t10 });
l.push(f);
let h = li({ inputs: { x: f }, backend: t10, attrs: { axis: s, keepDims: a } });
return l.forEach((g) => t10.disposeIntermediateTensorInfo(g)), h;
}
var W$ = { kernelName: Vn, backendName: "cpu", kernelFunc: KY };
function qY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
Y(n, "min");
let i = y.parseAxisParam(s, n.shape), p = i, u = C.getAxesPermutation(p, n.shape.length), c = n;
u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = C.getInnerMostAxes(p.length, n.shape.length)), C.assertAxesAreInnerMostDims("min", p, c.shape.length);
let [l, m] = C.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values;
for (let x = 0; x < f.length; ++x) {
let b = x * d, w = h[b];
for (let S = 0; S < d; ++S) {
let k = h[b + S];
(Number.isNaN(k) || k < w) && (w = k);
}
f[x] = w;
}
u != null && t10.disposeIntermediateTensorInfo(c);
let g = t10.makeTensorInfo(l, c.dtype, f);
if (a) {
let x = C.expandShapeToKeepDim(l, i), b = Ve({ inputs: { x: g }, backend: t10, attrs: { shape: x } });
return t10.disposeIntermediateTensorInfo(g), b;
}
return g;
}
var U$ = { kernelName: Wn, backendName: "cpu", kernelFunc: qY };
function jY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, mode: a } = o;
Y(n, "mirrorPad");
let i = s.map((w, S) => w[0] + n.shape[S] + w[1]), p = s.map((w) => w[0]), u = s.map((w, S) => w[0] + n.shape[S]), c = a === "reflect" ? 0 : 1, l = t10.data.get(n.dataId).values, m = n.shape.length, d = y.computeStrides(n.shape), f = y.sizeFromShape(i), h = i.length, g = y.computeStrides(i), x = y.getTypedArrayFromDType(n.dtype, f);
for (let w = 0; w < f; w++) {
let S = y.indexToLoc(w, h, g);
for (let _ = 0; _ < h; _++)
S[_] < p[_] ? S[_] = p[_] * 2 - S[_] - c : S[_] >= u[_] && (S[_] = (u[_] - 1) * 2 - S[_] + c);
S = S.map((_, E) => _ - p[E]);
let k = y.locToIndex(S, m, d);
x[w] = l[k];
}
return { dataId: t10.write(x, i, n.dtype), shape: i, dtype: n.dtype };
}
var G$ = { kernelName: Gn, backendName: "cpu", kernelFunc: jY };
var XY = ze((r, e) => {
let t10 = r % e;
return r < 0 && e < 0 || r >= 0 && e >= 0 ? t10 : (t10 + e) % e;
});
var YY = je(Ga, XY);
var H$ = { kernelName: Ga, backendName: "cpu", kernelFunc: YY };
var q$ = Bp(Rw());
function sI(r) {
let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = n.shape.length, i = s;
if (i === -1 && (i = a - 1), i !== a - 1)
throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${i}`);
let p = y.parseAxisParam([i], n.shape), u = nI({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: p, keepDims: false } }), c = C.expandShapeToKeepDim(u.shape, p), l = Ve({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), m = Ll({ inputs: { a: n, b: l }, backend: t10 }), d = $S({ inputs: { x: m }, backend: t10 }), f = li({ inputs: { x: d }, backend: t10, attrs: { axis: p, keepDims: false } }), h = Ve({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = zl({ inputs: { a: d, b: h }, backend: t10 });
return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), g;
}
var K$ = { kernelName: bs, backendName: "cpu", kernelFunc: sI };
function QY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o;
Y(n, "multinomial");
let p = i ? n : sI({ inputs: { logits: n }, backend: t10, attrs: { dim: -1 } }), u = p.shape[0], c = p.shape[1], l = t10.data.get(p.dataId).values, m = [u, s], d = y.makeZerosTypedArray(y.sizeFromShape(m), "int32");
for (let f = 0; f < u; ++f) {
let h = f * c, g = new Float32Array(c - 1);
g[0] = l[h];
for (let w = 1; w < g.length; ++w)
g[w] = g[w - 1] + l[h + w];
let x = q$.alea(a.toString()), b = f * s;
for (let w = 0; w < s; ++w) {
let S = x();
d[b + w] = g.length;
for (let k = 0; k < g.length; k++)
if (S < g[k]) {
d[b + w] = k;
break;
}
}
}
return i || t10.disposeIntermediateTensorInfo(p), t10.makeTensorInfo(m, "int32", d);
}
var j$ = { kernelName: Hn, backendName: "cpu", kernelFunc: QY };
var ZY = Wt.nonMaxSuppressionV3Impl;
function JY(r) {
let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o;
Y(n, "NonMaxSuppression");
let u = t10.data.get(n.dataId).values, c = t10.data.get(s.dataId).values, { selectedIndices: l } = ZY(u, c, a, i, p);
return t10.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var X$ = { kernelName: jn, backendName: "cpu", kernelFunc: JY };
var eQ = Wt.nonMaxSuppressionV4Impl;
function tQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o;
Y(n, "NonMaxSuppressionPadded");
let c = t10.data.get(n.dataId).values, l = t10.data.get(s.dataId).values, { selectedIndices: m, validOutputs: d } = eQ(c, l, a, i, p, u);
return [t10.makeTensorInfo([m.length], "int32", new Int32Array(m)), t10.makeTensorInfo([], "int32", new Int32Array([d]))];
}
var Y$ = { kernelName: Ha, backendName: "cpu", kernelFunc: tQ };
var rQ = Wt.nonMaxSuppressionV5Impl;
function oQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o;
Y(n, "NonMaxSuppressionWithScore");
let c = t10.data.get(n.dataId).values, l = t10.data.get(s.dataId).values, m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = rQ(c, l, m, d, f, h);
return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))];
}
var Q$ = { kernelName: Xn, backendName: "cpu", kernelFunc: oQ };
function nQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o;
Y(n, "oneHot");
let u = y.sizeFromShape(n.shape), c = new Float32Array(u * a);
c.fill(p);
let l = t10.data.get(n.dataId).values;
for (let m = 0; m < u; ++m)
l[m] >= 0 && l[m] < a && (c[m * a + l[m]] = i);
return t10.makeTensorInfo([...n.shape, a], s, c);
}
var Z$ = { kernelName: Yn, backendName: "cpu", kernelFunc: nQ };
function Ul(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
if (o.dtype === "string")
throw new Error("zerosLike is not supported for string tensors");
if (o.dtype === "complex64") {
let n = To({ inputs: { input: o }, backend: t10 }), s = Ul({ inputs: { x: n }, backend: t10 }), a = $a({ inputs: { input: o }, backend: t10 }), i = Ul({ inputs: { x: a }, backend: t10 }), p = Kt({ inputs: { real: s, imag: i }, backend: t10 });
return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p;
} else
return Wl({ backend: t10, attrs: { shape: o.shape, value: 0, dtype: o.dtype } });
}
var J$ = { kernelName: fa, backendName: "cpu", kernelFunc: Ul };
function eE(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
if (o.dtype === "string")
throw new Error("onesLike is not supported for string tensors");
if (o.dtype === "complex64") {
let n = To({ inputs: { input: o }, backend: t10 }), s = eE({ inputs: { x: n }, backend: t10 }), a = $a({ inputs: { input: o }, backend: t10 }), i = Ul({ inputs: { x: a }, backend: t10 }), p = Kt({ inputs: { real: s, imag: i }, backend: t10 });
return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p;
} else
return Wl({ backend: t10, attrs: { shape: o.shape, value: 1, dtype: o.dtype } });
}
var tE = { kernelName: na, backendName: "cpu", kernelFunc: eE };
function aI(r) {
let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
if (e.length === 1)
return vc({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
let s = e[0].shape, a = e[0].dtype;
e.forEach((c) => {
y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
});
let i = [], p = e.map((c) => {
let l = vc({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
return i.push(l), l;
}), u = mu({ inputs: p, backend: t10, attrs: { axis: n } });
return i.forEach((c) => t10.disposeIntermediateTensorInfo(c)), u;
}
var rE = { kernelName: sa, backendName: "cpu", kernelFunc: aI };
function sQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o;
Y(n, "pad");
let i = s.map((b, w) => b[0] + n.shape[w] + b[1]), p = s.map((b) => b[0]), u = t10.data.get(n.dataId).values, c = y.sizeFromShape(n.shape), l = n.shape.length, m = y.computeStrides(n.shape), d = y.sizeFromShape(i), f = i.length, h = y.computeStrides(i), g = y.getTypedArrayFromDType(n.dtype, d);
a !== 0 && g.fill(a);
for (let b = 0; b < c; b++) {
let S = y.indexToLoc(b, l, m).map((_, E) => _ + p[E]), k = y.locToIndex(S, f, h);
g[k] = u[b];
}
return { dataId: t10.write(g, i, n.dtype), shape: i, dtype: n.dtype };
}
var Bf = { kernelName: Qn, backendName: "cpu", kernelFunc: sQ };
var aQ = ze((r, e) => Math.pow(r, e));
var iQ = je(Zn, aQ);
var oE = { kernelName: Zn, backendName: "cpu", kernelFunc: iQ };
function uQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t10.data.get(x.dataId).values), u = n.map((x) => x.shape), c = t10.data.get(s.dataId).values, l = t10.data.get(a.dataId).values, [m, d, f] = _f(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t10.makeTensorInfo([x.length], "int32", x)), g = t10.makeTensorInfo(f, s.dtype, d);
return h.concat([g]);
}
var nE = { kernelName: Kp, backendName: "cpu", kernelFunc: uQ };
function pQ(r) {
let { inputs: e, backend: t10 } = r, { starts: o, limits: n, deltas: s } = e, a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = $f(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t10.makeTensorInfo([u.length], "int32", u), m = t10.makeTensorInfo([c.length], o.dtype, c);
return [l, m];
}
var sE = { kernelName: qp, backendName: "cpu", kernelFunc: pQ };
function cQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t10.data.get(n.dataId).values, c = t10.data.get(s.dataId).values, l = t10.data.get(a.dataId).values, m = i.map((g) => t10.data.get(g.dataId).values), d = i.map((g) => g.shape), [f, h] = Ef(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p);
return t10.makeTensorInfo(f, s.dtype, h);
}
var aE = { kernelName: jp, backendName: "cpu", kernelFunc: cQ };
function lQ(r) {
let { backend: e, attrs: t10 } = r, { start: o, stop: n, dtype: s, step: a } = t10, i = ap(o, n, a, s);
return e.makeTensorInfo([i.length], s, i);
}
var iE = { kernelName: aa, backendName: "cpu", kernelFunc: lQ };
var mQ = Ie(ts, (r) => 1 / r);
var uE = { kernelName: ts, backendName: "cpu", kernelFunc: mQ };
function dQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o;
Y(n, "resizeBilinear");
let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t10.data.get(n.dataId).values, g = new Float32Array(y.sizeFromShape([l, u, c, f])), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], w = 0, S = x[0] / b[0], k = x[1] / b[1];
for (let _ = 0; _ < l; _++)
for (let E = 0; E < u; E++) {
let R;
a ? R = S * (E + 0.5) - 0.5 : R = S * E;
let D = Math.max(0, Math.floor(R)), F = R - D, O = Math.min(m - 1, Math.ceil(R)), M = _ * p[0] + D * p[1], L = _ * p[0] + O * p[1];
for (let B = 0; B < c; B++) {
let z;
a ? z = k * (B + 0.5) - 0.5 : z = k * B;
let U = Math.max(0, Math.floor(z)), j = z - U, H = Math.min(d - 1, Math.ceil(z)), X = M + U * p[2], J = L + U * p[2], re = M + H * p[2], ne = L + H * p[2];
for (let ee = 0; ee < f; ee++) {
let oe = h[X + ee], ie = h[J + ee], le = h[re + ee], ye = h[ne + ee], _e = oe + (le - oe) * j, ve = ie + (ye - ie) * j, Fe = _e + (ve - _e) * F;
g[w++] = Fe;
}
}
}
return t10.makeTensorInfo([l, u, c, f], "float32", g);
}
var pE = { kernelName: ns, backendName: "cpu", kernelFunc: dQ };
function fQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o;
Y([s, n], "resizeBilinearGrad");
let i = y.computeStrides(n.shape), [p, u, c, l] = n.shape, [, m, d] = s.shape, f = new Float32Array(p * u * c * l), h = [a && m > 1 ? u - 1 : u, a && d > 1 ? c - 1 : c], g = [a && m > 1 ? m - 1 : m, a && d > 1 ? d - 1 : d], x = h[0] / g[0], b = h[1] / g[1], w = t10.data.get(s.dataId).values, S = 0;
for (let k = 0; k < p; k++) {
let _ = k * i[0];
for (let E = 0; E < m; E++) {
let R = E * x, D = Math.floor(R), F = Math.min(Math.ceil(R), u - 1), O = _ + D * i[1], M = _ + F * i[1], L = R - D, B = 1 - L;
for (let z = 0; z < d; z++) {
let U = z * b, j = Math.floor(U), H = Math.min(Math.ceil(U), c - 1), X = U - j, J = 1 - X, re = O + j * i[2], ne = O + H * i[2], ee = M + j * i[2], oe = M + H * i[2], ie = B * J, le = B * X, ye = L * J, _e = L * X;
for (let ve = 0; ve < l; ve++) {
let Fe = w[S++];
f[re + ve] += Fe * ie, f[ne + ve] += Fe * le, f[ee + ve] += Fe * ye, f[oe + ve] += Fe * _e;
}
}
}
}
return t10.makeTensorInfo([p, c, u, l], "float32", f);
}
var cE = { kernelName: qa, backendName: "cpu", kernelFunc: fQ };
function hQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o;
Y(n, "resizeNearestNeighbor");
let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t10.data.get(n.dataId).values, g = new Float32Array(l * u * c * f), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], w = x[0] / b[0], S = x[1] / b[1], k = 0;
for (let _ = 0; _ < l; _++) {
let E = _ * p[0];
for (let R = 0; R < u; R++) {
let D = a ? w * (R + 0.5) : w * R, F = Math.min(m - 1, s ? Math.round(D) : Math.floor(D));
a && (F = Math.max(0, F));
let O = E + F * p[1];
for (let M = 0; M < c; M++) {
let L = a ? S * (M + 0.5) : S * M, B = Math.min(d - 1, s ? Math.round(L) : Math.floor(L));
a && (B = Math.max(0, B));
let z = O + B * p[2];
for (let U = 0; U < f; U++) {
let j = h[z + U];
g[k++] = j;
}
}
}
}
return t10.makeTensorInfo([l, u, c, f], n.dtype, g);
}
var lE = { kernelName: os, backendName: "cpu", kernelFunc: hQ };
function gQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o;
Y([s, n], "resizeNearestNeighborGrad");
let i = y.computeStrides(n.shape), p = y.computeStrides(s.shape), [u, c, l, m] = n.shape, [, d, f] = s.shape, h = new Float32Array(u * c * l * m), g = t10.data.get(s.dataId).values, x = [a && d > 1 ? c - 1 : c, a && f > 1 ? l - 1 : l], b = [a && d > 1 ? d - 1 : d, a && f > 1 ? f - 1 : f], w = x[0] / b[0], S = x[1] / b[1], k = 1 / w, _ = 1 / S, E = Math.ceil(k) * 2 + 2, R = Math.ceil(_) * 2 + 2;
for (let D = 0; D < u; D++) {
let F = D * i[0];
for (let O = 0; O < c; O++) {
let M = F + O * i[1], L = Math.floor(O * k), B = Math.floor(L - E / 2);
for (let z = 0; z < l; z++) {
let U = M + z * i[2], j = Math.floor(z * _), H = Math.floor(j - R / 2);
for (let X = 0; X < m; X++) {
let J = 0;
for (let re = 0; re < E; re++) {
let ne = re + B;
if (ne < 0 || ne >= d)
continue;
let ee = F + ne * p[1], oe = ne * w, ie = Math.min(c - 1, a ? Math.round(oe) : Math.floor(oe));
if (O === ie)
for (let le = 0; le < R; le++) {
let ye = le + H;
if (ye < 0 || ye >= f)
continue;
let _e = ee + ye * p[2], ve = ye * S, Fe = Math.min(l - 1, a ? Math.round(ve) : Math.floor(ve));
z === Fe && (J += g[_e + X]);
}
}
h[U + X] = J;
}
}
}
}
return t10.makeTensorInfo(n.shape, n.dtype, h);
}
var mE = { kernelName: Ka, backendName: "cpu", kernelFunc: gQ };
function xQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o;
Y(n, "reverse");
let a = n.shape.length, i = y.parseAxisParam(s, n.shape);
if (a === 0)
return lr({ inputs: { x: n }, backend: t10 });
let p = new tt(n.shape, n.dtype), u = t10.bufferSync(n);
for (let c = 0; c < p.size; c++) {
let l = p.indexToLoc(c), m = l.slice();
i.forEach((d) => m[d] = n.shape[d] - 1 - m[d]), p.set(u.get(...m), ...l);
}
return t10.makeTensorInfo(p.shape, p.dtype, p.values);
}
var dE = { kernelName: as, backendName: "cpu", kernelFunc: xQ };
var fE = { kernelName: _s, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [u, c, l, m] = o.shape, [d, f] = C.getImageCenter(a, c, l), h = 255, g = Math.sin(n), x = Math.cos(n), b = i.data.get(o.dataId).values;
for (let S = 0; S < u; S++) {
let k = S * l * c * m;
for (let _ = 0; _ < c; _++) {
let E = _ * (l * m);
for (let R = 0; R < l; R++) {
let D = R * m;
for (let F = 0; F < m; F++) {
let O = [u, _, R, F], M = O[2], L = O[1], B = (M - d) * x - (L - f) * g, z = (M - d) * g + (L - f) * x;
B = Math.round(B + d), z = Math.round(z + f);
let U = s;
if (typeof s != "number" && (F === 3 ? U = h : U = s[F]), B >= 0 && B < l && z >= 0 && z < c) {
let H = z * (l * m), X = B * m, J = k + H + X + F;
U = b[J];
}
let j = k + E + D + F;
p[j] = U;
}
}
}
}
return { dataId: i.write(p, o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
var yQ = Ie(is, (r) => {
let e = Math.floor(r);
return r - e < 0.5 ? Math.floor(r) : r - e > 0.5 ? Math.ceil(r) : e % 2 === 0 ? e : e + 1;
});
var hE = { kernelName: is, backendName: "cpu", kernelFunc: yQ };
function bQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = C.calculateShapes(s, n, a), m = true, d = t10.bufferSync(n), f = t10.bufferSync(s), h = Fs(d, f, a, l, u, p, i, c, 0, m);
return t10.makeTensorInfo(a, h.dtype, h.values);
}
var gE = { kernelName: ps, backendName: "cpu", kernelFunc: bQ };
function CQ(r, e) {
let t10 = 0, o = r.length, n = 0;
for (; t10 < o; )
n = Math.floor((t10 + o) / 2), r[n] < e ? t10 = n + 1 : o = n;
return o;
}
function wQ(r, e) {
let t10 = 0, o = r.length, n = 0;
for (; t10 < o; )
n = Math.floor((t10 + o) / 2), r[n] <= e ? t10 = n + 1 : o = n;
return o;
}
function xE(r, e, t10, o, n, s) {
let a = y.getArrayFromDType("int32", t10 * n);
for (let i = 0; i < t10; ++i) {
let p = r.slice(i * o, (i + 1) * o), u = i * n;
for (let c = 0; c < n; ++c)
a[u + c] = s === "left" ? CQ(p, e[c + u]) : wQ(p, e[c + u]);
}
return a;
}
function SQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, u = xE(i, p, n.shape[0], n.shape[1], s.shape[1], a);
return t10.makeTensorInfo(s.shape, "int32", u);
}
var yE = { kernelName: ls, backendName: "cpu", kernelFunc: SQ };
function IQ(r) {
let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e;
Y([o, n, s], "select");
let a = o.shape.length, i = t10.data.get(o.dataId).values, p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = dt(n.dtype, s.dtype), l = y.makeZerosTypedArray(y.sizeFromShape(n.shape), c), m = 0, d = a === 0 || a > 1 || n.shape.length === 1 ? 1 : y.sizeFromShape(n.shape.slice(1));
for (let f = 0; f < i.length; f++)
for (let h = 0; h < d; h++)
i[f] === 1 ? l[m++] = p[f] : l[m++] = u[f];
return t10.makeTensorInfo(n.shape, c, l);
}
var bE = { kernelName: ua, backendName: "cpu", kernelFunc: IQ };
var vQ = C.SELU_SCALEALPHA;
var kQ = C.SELU_SCALE;
var NQ = Ie(ms, (r) => r >= 0 ? kQ * r : vQ * (Math.exp(r) - 1));
var CE = { kernelName: ms, backendName: "cpu", kernelFunc: NQ };
var TQ = Ie(fs, (r) => r < 0 ? -1 : r > 0 ? 1 : 0);
var wE = { kernelName: fs, backendName: "cpu", kernelFunc: TQ };
var _Q = Ie(ds, (r) => Math.sin(r));
var SE = { kernelName: ds, backendName: "cpu", kernelFunc: _Q };
var $Q = Ie(ja, (r) => Math.sinh(r));
var IE = { kernelName: ja, backendName: "cpu", kernelFunc: $Q };
var EQ = 11920928955078125e-23;
var vE = Math.log(EQ) + 2;
var RQ = Ie(gs, (r) => {
let e = r > -vE, t10 = r < vE, o = Math.exp(r), n;
return t10 ? n = o : e ? n = r : n = Math.log(1 + o), n;
});
var kE = { kernelName: gs, backendName: "cpu", kernelFunc: RQ };
function DQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o;
Y([n], "spaceToBatchND");
let i = y.sizeFromShape(s), p = [[0, 0]];
p.push(...a);
for (let _ = 1 + s.length; _ < n.shape.length; ++_)
p.push([0, 0]);
let u = Bf.kernelFunc({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), c = C.getReshaped(u.shape, s, i, false), l = C.getPermuted(c.length, s.length, false), m = C.getReshapedPermuted(u.shape, s, i, false), h = Ve({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), b = St({ inputs: { x: h }, backend: t10, attrs: { perm: l } }), k = Ve({ inputs: { x: b }, backend: t10, attrs: { shape: m } });
return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(b), k;
}
var NE = { kernelName: ca, backendName: "cpu", kernelFunc: DQ };
function AQ(r) {
let { inputs: e, backend: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e;
if (s.shape.length !== 1)
throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);
if (o.shape.length !== 2)
throw new Error(`Indices must be a matrix, saw:
${o.shape}`);
if (n.shape.length !== 1)
throw new Error(`Values must be a vector, saw:
${n.shape}`);
if (a.shape.length !== 0)
throw new Error(`Default value must be a scalar, saw:
${a.shape}`);
let i = t10.data.get(o.dataId).values, p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = t10.data.get(a.dataId).values[0], [l, m, d, f, h] = Rf(i, o.shape, o.dtype, p, n.dtype, u, c);
return [t10.makeTensorInfo(m, o.dtype, l), t10.makeTensorInfo([m[0]], n.dtype, d), t10.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t10.makeTensorInfo([h.length], o.dtype, new Int32Array(h))];
}
var TE = { kernelName: Vi, backendName: "cpu", kernelFunc: AQ };
function FQ(r) {
let { inputs: e, backend: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = e;
if (o.shape.length !== 2)
throw new Error(`Input indices should be a matrix but received shape
${o.shape}`);
if (n.shape.length !== 1)
throw new Error(`Input shape should be a vector but received shape
${n.shape}`);
if (s.shape.length !== 1)
throw new Error(`Target shape should be a vector but received shape ${s.shape}`);
let a = Array.from(t10.data.get(n.dataId).values), i = t10.data.get(o.dataId).values, p = Array.from(t10.data.get(s.dataId).values), [u, c, l] = Df(i, o.shape, o.dtype, a, p);
return [t10.makeTensorInfo(c, o.dtype, u), t10.makeTensorInfo([l.length], s.dtype, new Int32Array(l))];
}
var _E = { kernelName: Xa, backendName: "cpu", kernelFunc: FQ };
function PQ(r) {
let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e;
if (o.shape.length < 1)
throw new Error("Data should be at least 1 dimensional but received scalar");
if (n.shape.length !== 1)
throw new Error(`Indices should be a vector but received shape
${n.shape}`);
if (s.shape.length !== 1)
throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);
if (n.shape[0] !== s.shape[0])
throw new Error("segmentIds and indices should have same size.");
let a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = wc(a, o.shape, o.dtype, i, p, true);
return t10.makeTensorInfo(c, o.dtype, u);
}
var $E = { kernelName: Wi, backendName: "cpu", kernelFunc: PQ };
function OQ(r) {
let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e;
if (o.shape.length < 1)
throw new Error("Data should be at least 1 dimensional but received scalar");
if (n.shape.length !== 1)
throw new Error(`Indices should be a vector but received shape
${n.shape}`);
if (s.shape.length !== 1)
throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);
if (n.shape[0] !== s.shape[0])
throw new Error("segmentIds and indices should have same size.");
let a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = wc(a, o.shape, o.dtype, i, p);
return t10.makeTensorInfo(c, o.dtype, u);
}
var EE = { kernelName: Ui, backendName: "cpu", kernelFunc: OQ };
function MQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = C.calculateShapes(s, n, i), d = false, f = t10.bufferSync(n), h;
switch (s.dtype) {
case "bool": {
let g = t10.bufferSync(s), x = !!t10.data.get(a.dataId).values[0];
h = Fs(f, g, i, m, c, u, p, l, x, d);
break;
}
case "float32": {
let g = t10.bufferSync(s), x = t10.data.get(a.dataId).values[0];
h = Fs(f, g, i, m, c, u, p, l, x, d);
break;
}
case "int32": {
let g = t10.bufferSync(s), x = t10.data.get(a.dataId).values[0];
h = Fs(f, g, i, m, c, u, p, l, x, d);
break;
}
case "string": {
let g = t10.bufferSync(s), x = y.decodeString(t10.data.get(a.dataId).values[0]);
h = Fs(f, g, i, m, c, u, p, l, x, d);
break;
}
default:
throw new Error(`Unsupported type ${s.dtype}`);
}
return t10.makeTensorInfo(i, h.dtype, h.values);
}
var RE = { kernelName: Cs, backendName: "cpu", kernelFunc: MQ };
function LQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = C.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice();
return p.map((l) => {
let m = [...c];
m[i] = l;
let d = Eo({ inputs: { x: n }, backend: t10, attrs: { begin: u, size: m } });
return u[i] += l, d;
});
}
var DE = { kernelName: la, backendName: "cpu", kernelFunc: LQ };
var AE = { kernelName: Gi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e }) => {
let { x: t10 } = r, o = e;
Y(t10, "square");
let n = o.data.get(t10.dataId).values, s = new Float32Array(n.length);
for (let i = 0; i < n.length; ++i) {
let p = n[i];
s[i] = p * p;
}
return { dataId: o.write(s, t10.shape, t10.dtype), shape: t10.shape, dtype: t10.dtype };
} };
var BQ = Ie(yo, (r, e) => {
let t10 = e;
return isNaN(r) ? NaN : r > 0 ? 1 : t10.alpha;
});
var FE = { kernelName: yo, backendName: "cpu", kernelFunc: BQ };
function zQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o;
Y(n, "stridedSlice");
let { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: w, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
if (h)
k = Ve({ inputs: { x: n }, backend: t10, attrs: { shape: f } });
else if (g || x) {
y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
let _ = ct.computeOutShape(b, w, S), E = Eo({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: _ } });
k = Ve({ inputs: { x: E }, backend: t10, attrs: { shape: f } }), t10.disposeIntermediateTensorInfo(E);
} else {
let _ = t10.bufferSync(n), E = Af(d, _, S, b);
k = t10.makeTensorInfo(f, E.dtype, E.values);
}
return k;
}
var PE = { kernelName: Ss, backendName: "cpu", kernelFunc: zQ };
function VQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.data.get(c.dataId).values, d = t10.data.get(l.dataId).values, [f, h] = up(m, d, n, s, a, i, p, u);
return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)];
}
var OE = { kernelName: ma, backendName: "cpu", kernelFunc: VQ };
function WQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { skipEmpty: n } = o, { 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 = t10.data.get(s.dataId).values, p = t10.data.get(a.dataId).values[0], [u, c, l] = pp(i, p, n), m = c.length;
return [t10.makeTensorInfo([m, 2], "int32", u), t10.makeTensorInfo([m], "string", c), t10.makeTensorInfo([2], "int32", new Int32Array(l))];
}
var ME = { kernelName: Hi, backendName: "cpu", kernelFunc: WQ };
function UQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { numBuckets: n } = o, { input: s } = e;
if (s.dtype !== "string")
throw new Error("Input must be of datatype string");
if (n <= 0)
throw new Error("Number of buckets must be at least 1");
let a = t10.data.get(s.dataId).values, i = cp(a, n);
return t10.makeTensorInfo(s.shape, "int32", i);
}
var LE = { kernelName: Ki, backendName: "cpu", kernelFunc: UQ };
var GQ = Ie(vs, (r) => Math.tan(r));
var BE = { kernelName: vs, backendName: "cpu", kernelFunc: GQ };
var HQ = Ie(ks, (r) => Math.tanh(r));
var zE = { kernelName: ks, backendName: "cpu", kernelFunc: HQ };
function KQ(r) {
let { inputs: e, backend: t10 } = r, { tensor: o, indices: n, updates: s } = e, { sliceRank: a, numUpdates: i, sliceSize: p, strides: u, outputSize: c } = C.calculateShapes(s, n, o.shape), l = false, m = t10.bufferSync(n), d = t10.bufferSync(s), f = t10.bufferSync(o), h = Fs(m, d, o.shape, c, p, i, a, u, f, l);
return t10.makeTensorInfo(o.shape, h.dtype, h.values);
}
var VE = { kernelName: cs, backendName: "cpu", kernelFunc: KQ };
function qQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o;
Y(n, "tile");
let a = Ff(t10.bufferSync(n), s);
return t10.makeTensorInfo(a.shape, a.dtype, a.values);
}
var WE = { kernelName: so, backendName: "cpu", kernelFunc: qQ };
function jQ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o;
Y(n, "topk");
let i = t10.data.get(n.dataId).values, [p, u] = Pf(i, n.shape, n.dtype, s, a);
return [t10.makeTensorInfo(p.shape, p.dtype, p.values), t10.makeTensorInfo(u.shape, u.dtype, u.values)];
}
var UE = { kernelName: Ns, backendName: "cpu", kernelFunc: jQ };
function XQ(r) {
let { inputs: e, attrs: t10, backend: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = t10, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = y.computeStrides(n.shape), b = x[0], w = x[1], S = x[2], k = y.computeStrides(g), _ = k[0], E = k[1], R = k[2], D = y.getTypedArrayFromDType(n.dtype, y.sizeFromShape(g));
D.fill(p);
let F = o.data.get(n.dataId).values, O = o.data.get(s.dataId).values;
for (let L = 0; L < c; ++L) {
let B = s.shape[0] === 1 ? O : O.subarray(L * 8, L * 8 + 8);
for (let z = 0; z < f; ++z)
for (let U = 0; U < h; ++U)
for (let j = 0; j < d; ++j) {
let H, X = B[6] * U + B[7] * z + 1;
if (X === 0)
continue;
let J = (B[0] * U + B[1] * z + B[2]) / X, re = (B[3] * U + B[4] * z + B[5]) / X, ne = GE(J, m, i), ee = GE(re, l, i);
switch (a) {
case "nearest":
H = e7(F, l, m, b, w, S, L, ee, ne, j, p);
break;
case "bilinear":
H = t7(F, l, m, b, w, S, L, ee, ne, j, p);
break;
default:
throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${a}`);
}
let oe = L * _ + z * E + U * R + j;
D[oe] = H;
}
return o.makeTensorInfo(g, n.dtype, D);
}
return { dataId: o.write(D, g, n.dtype), shape: n.shape, dtype: n.dtype };
}
var HE = { kernelName: Ts, backendName: "cpu", kernelFunc: XQ };
function GE(r, e, t10) {
switch (t10) {
case "reflect":
return YQ(r, e);
case "wrap":
return QQ(r, e);
case "nearest":
return JQ(r, e);
case "constant":
default:
return ZQ(r, e);
}
}
function YQ(r, e) {
let t10 = r;
if (t10 < 0)
if (e <= 1)
t10 = 0;
else {
let o = 2 * e;
t10 < o && (t10 = o * Math.trunc(-t10 / o) + t10), t10 = t10 < -e ? t10 + o : -t10 - 1;
}
else if (t10 > e - 1)
if (e <= 1)
t10 = 0;
else {
let o = 2 * e;
t10 -= o * Math.trunc(t10 / o), t10 >= e && (t10 = o - t10 - 1);
}
return y.clamp(0, t10, e - 1);
}
function QQ(r, e) {
let t10 = r;
if (t10 < 0)
if (e <= 1)
t10 = 0;
else {
let o = e - 1;
t10 += e * (Math.trunc(-t10 / o) + 1);
}
else if (t10 > e - 1)
if (e <= 1)
t10 = 0;
else {
let o = e - 1;
t10 -= e * Math.trunc(t10 / o);
}
return y.clamp(0, t10, e - 1);
}
function ZQ(r, e) {
return r;
}
function JQ(r, e) {
return y.clamp(0, r, e - 1);
}
function Gl(r, e, t10, o, n, s, a, i, p, u, c) {
let l = a * o + i * n + p * s + u;
return 0 <= i && i < e && 0 <= p && p < t10 ? r[l] : c;
}
function e7(r, e, t10, o, n, s, a, i, p, u, c) {
let l = Math.round(i), m = Math.round(p);
return Gl(r, e, t10, o, n, s, a, l, m, u, c);
}
function t7(r, e, t10, o, n, s, a, i, p, u, c) {
let l = Math.floor(i), m = Math.floor(p), d = l + 1, f = m + 1, h = (f - p) * Gl(r, e, t10, o, n, s, a, l, m, u, c) + (p - m) * Gl(r, e, t10, o, n, s, a, l, f, u, c), g = (f - p) * Gl(r, e, t10, o, n, s, a, d, m, u, c) + (p - m) * Gl(r, e, t10, o, n, s, a, d, f, u, c);
return (d - i) * h + (i - l) * g;
}
function r7(r) {
let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e;
Y(s, "unique");
let a = o.data.get(s.dataId).values, { outputValues: i, outputShape: p, indices: u } = lp(a, n, s.shape, s.dtype);
return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)];
}
var KE = { kernelName: qi, backendName: "cpu", kernelFunc: r7 };
function o7(r) {
let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o;
s < 0 && (s += n.shape.length);
let a = n.shape.length, i = n.shape[s], p = new Array(a - 1), u = 0;
for (let d = 0; d < a; d++)
d !== s && (p[u++] = n.shape[d]);
let c = new Array(a).fill(0), l = n.shape.slice();
l[s] = 1;
let m = new Array(i);
for (let d = 0; d < m.length; d++) {
c[s] = d;
let f = Eo({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: l } });
m[d] = Ve({ inputs: { x: f }, backend: t10, attrs: { shape: p } }), t10.disposeIntermediateTensorInfo(f);
}
return m;
}
var qE = { kernelName: da, backendName: "cpu", kernelFunc: o7 };
function n7(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o;
Y(n, "unsortedSegmentSum");
let i = n.shape.length, p = s.shape.length, u = [], c = [], l = i - p, m = s;
for (let f = 0; f < l; ++f) {
let h = vc({ inputs: { input: m }, backend: t10, attrs: { dim: f + 1 } });
m = h, c.push(h);
}
for (let f = 0; f < a; ++f) {
let h = y.createScalarValue(f, "int32"), g = t10.makeTensorInfo([], "int32", h), x = TS({ inputs: { a: g, b: m }, backend: t10 }), b = _o({ inputs: { x }, backend: t10, attrs: { dtype: "float32" } }), w = sp({ inputs: { a: b, b: n }, backend: t10 }), S = li({ inputs: { x: w }, backend: t10, attrs: { axis: 0, keepDims: false } });
u.push(S), c.push(g), c.push(x), c.push(b), c.push(w), c.push(S);
}
let d = aI({ inputs: u, backend: t10, attrs: { axis: 0 } });
return c.forEach((f) => t10.disposeIntermediateTensorInfo(f)), d;
}
var jE = { kernelName: ji, backendName: "cpu", kernelFunc: n7 };
var s7 = [C_, _T, w_, S_, AT, I_, v_, k_, N_, T_, __, $_, E_, R_, D_, F_, P_, O_, M_, b_, L_, B_, z_, FT, V_, DT, PT, W_, $T, U_, H_, K_, q_, j_, X_, Y_, Q_, Z_, J_, e$, t$, r$, o$, n$, s$, a$, i$, u$, p$, c$, l$, d$, d_, f$, OT, h$, MT, g$, LT, x$, y$, b$, BT, zT, C$, w$, S$, I$, VT, WT, ET, v$, G_, k$, N$, T$, f_, UT, GT, _$, HT, $$, E$, R$, D$, A$, F$, P$, KT, O$, M$, L$, B$, V$, W$, U$, qT, G$, H$, j$, jT, XT, X$, Y$, Q$, YT, Z$, tE, rE, Bf, oE, h_, ZT, nE, sE, aE, iE, RT, Vl, uE, g_, x_, y_, pE, cE, lE, mE, dE, fE, hE, o_, gE, yE, bE, CE, s_, wE, SE, IE, a_, K$, kE, NE, TE, _E, $E, EE, RE, DE, u_, AE, p_, c_, FE, PE, OE, ME, LE, l_, m$, BE, zE, VE, WE, UE, HE, QT, KE, qE, jE, J$];
for (let r of s7)
Ya(r);
var _c = {};
He(_c, { assertNotComplex: () => Ps, bindCanvasToFramebuffer: () => f7, bindColorTextureToFramebuffer: () => jl, bindTextureToProgramUniformSampler: () => SI, bindTextureUnit: () => ZE, bindVertexBufferToProgramAttribute: () => Hf, callAndCheck: () => ce, canBeRepresented: () => cI, createFragmentShader: () => mI, createFramebuffer: () => bI, createProgram: () => dI, createStaticIndexBuffer: () => gI, createStaticVertexBuffer: () => hI, createTexture: () => xI, createVertexShader: () => lI, getBatchDim: () => di, getExtensionOrThrow: () => kc, getFramebufferErrorMessage: () => JE, getMaxTexturesInShader: () => kI, getNumChannels: () => m7, getProgramUniformLocation: () => wI, getProgramUniformLocationOrThrow: () => CI, getRowsCols: () => fi, getShapeAs3D: () => Tc, getTextureShapeFromLogicalShape: () => II, getWebGLDisjointQueryTimerVersion: () => NI, getWebGLErrorMessage: () => QE, getWebGLMaxTextureSize: () => vI, hasExtension: () => Hr, isCapableOfRenderingToFloatTexture: () => TI, isDownloadFloatTextureEnabled: () => _I, isReshapeFree: () => fu, isWebGLFenceEnabled: () => $I, isWebGLVersionEnabled: () => qf, linkProgram: () => fI, logShaderSourceAndInfoLog: () => Gf, resetMaxTextureSize: () => h7, resetMaxTexturesInShader: () => g7, unbindColorTextureFromFramebuffer: () => Kf, unbindTextureUnit: () => d7, validateFramebuffer: () => Nc, validateProgram: () => ql, validateTextureSize: () => yI });
var dp = {};
var zf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
function iI(r, e) {
dp[r] = e;
}
function Gr(r, e) {
if (!(r in dp) || e != null) {
let o = i7(r, e);
if (o !== null)
dp[r] = o;
else
return console.log("Could not get context for WebGL version", r), null;
}
let t10 = dp[r];
return t10 == null || t10.isContextLost() ? (delete dp[r], Gr(r)) : (t10.disable(t10.DEPTH_TEST), t10.disable(t10.STENCIL_TEST), t10.disable(t10.BLEND), t10.disable(t10.DITHER), t10.disable(t10.POLYGON_OFFSET_FILL), t10.disable(t10.SAMPLE_COVERAGE), t10.enable(t10.SCISSOR_TEST), t10.enable(t10.CULL_FACE), t10.cullFace(t10.BACK), dp[r]);
}
function a7(r) {
if (!P().getBool("IS_SAFARI") && typeof OffscreenCanvas != "undefined" && r === 2)
return new OffscreenCanvas(300, 150);
if (typeof document != "undefined")
return document.createElement("canvas");
throw new Error("Cannot create a canvas in this context");
}
function i7(r, e) {
if (r !== 1 && r !== 2)
throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
let t10 = e == null ? a7(r) : e;
return t10.addEventListener("webglcontextlost", (o) => {
o.preventDefault(), delete dp[r];
}, false), P().getBool("SOFTWARE_WEBGL_ENABLED") && (zf.failIfMajorPerformanceCaveat = false), r === 1 ? t10.getContext("webgl", zf) || t10.getContext("experimental-webgl", zf) : t10.getContext("webgl2", zf);
}
var du;
(function(r) {
r[r.DENSE = 0] = "DENSE", r[r.SHARED_BATCH = 1] = "SHARED_BATCH";
})(du || (du = {}));
var mr;
(function(r) {
r[r.RENDER = 0] = "RENDER", r[r.UPLOAD = 1] = "UPLOAD", r[r.PIXELS = 2] = "PIXELS", r[r.DOWNLOAD = 3] = "DOWNLOAD";
})(mr || (mr = {}));
var er;
(function(r) {
r[r.UNPACKED_FLOAT16 = 0] = "UNPACKED_FLOAT16", r[r.UNPACKED_FLOAT32 = 1] = "UNPACKED_FLOAT32", r[r.PACKED_4X1_UNSIGNED_BYTE = 2] = "PACKED_4X1_UNSIGNED_BYTE", r[r.PACKED_2X2_FLOAT32 = 3] = "PACKED_2X2_FLOAT32", r[r.PACKED_2X2_FLOAT16 = 4] = "PACKED_2X2_FLOAT16";
})(er || (er = {}));
function fp(r, e) {
return [e, r];
}
function XE(r, e) {
return r * e;
}
function Hl(r) {
let e = y.sizeFromShape(r), t10 = Math.ceil(e / 4);
return y.sizeToSquarishShape(t10);
}
function Ea(r, e) {
return [Math.max(1, Math.ceil(e / 2)), Math.max(1, Math.ceil(r / 2))];
}
function YE(r, e) {
let [t10, o] = Ea(r, e);
return t10 * o * 4;
}
function Kl(r, e) {
let t10 = r, o, n, s, a, i, p, u, c, l, m;
return P().getNumber("WEBGL_VERSION") === 2 ? (o = t10.R32F, n = t10.R16F, s = t10.RGBA16F, a = t10.RGBA32F, i = t10.RED, u = 4, c = 1, l = t10.HALF_FLOAT, m = t10.FLOAT, p = t10.RGBA8) : (o = r.RGBA, n = r.RGBA, s = r.RGBA, a = t10.RGBA, i = r.RGBA, u = 4, c = 4, l = e != null ? e.HALF_FLOAT_OES : null, m = r.FLOAT, p = r.RGBA), { internalFormatFloat: o, internalFormatHalfFloat: n, internalFormatPackedHalfFloat: s, internalFormatPackedFloat: a, textureFormatFloat: i, downloadTextureFormat: p, downloadUnpackNumChannels: u, defaultNumChannels: c, textureTypeHalfFloat: l, textureTypeFloat: m };
}
function ce(r, e) {
let t10 = e();
return P().getBool("DEBUG") && u7(r), t10;
}
function u7(r) {
let e = r.getError();
if (e !== r.NO_ERROR)
throw new Error("WebGL Error: " + QE(r, e));
}
var p7 = 596e-10;
var c7 = 65504;
function cI(r) {
return !!(P().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || r === 0 || p7 < Math.abs(r) && Math.abs(r) < c7);
}
function QE(r, e) {
switch (e) {
case r.NO_ERROR:
return "NO_ERROR";
case r.INVALID_ENUM:
return "INVALID_ENUM";
case r.INVALID_VALUE:
return "INVALID_VALUE";
case r.INVALID_OPERATION:
return "INVALID_OPERATION";
case r.INVALID_FRAMEBUFFER_OPERATION:
return "INVALID_FRAMEBUFFER_OPERATION";
case r.OUT_OF_MEMORY:
return "OUT_OF_MEMORY";
case r.CONTEXT_LOST_WEBGL:
return "CONTEXT_LOST_WEBGL";
default:
return `Unknown error code ${e}`;
}
}
function kc(r, e) {
return mi(r, () => r.getExtension(e), 'Extension "' + e + '" not supported on this browser.');
}
function lI(r, e) {
let t10 = mi(r, () => r.createShader(r.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
if (ce(r, () => r.shaderSource(t10, e)), ce(r, () => r.compileShader(t10)), r.getShaderParameter(t10, r.COMPILE_STATUS) === false)
throw console.log(r.getShaderInfoLog(t10)), new Error("Failed to compile vertex shader.");
return t10;
}
function mI(r, e) {
let t10 = mi(r, () => r.createShader(r.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
if (ce(r, () => r.shaderSource(t10, e)), ce(r, () => r.compileShader(t10)), P().get("ENGINE_COMPILE_ONLY"))
return t10;
if (r.getShaderParameter(t10, r.COMPILE_STATUS) === false)
throw Gf(e, r.getShaderInfoLog(t10)), new Error("Failed to compile fragment shader.");
return t10;
}
var l7 = /ERROR: [0-9]+:([0-9]+):/g;
function Gf(r, e) {
let t10 = l7.exec(e);
if (t10 == null) {
console.log(`Couldn't parse line number in error: ${e}`), console.log(r);
return;
}
let o = +t10[1], n = r.split(`
`), s = n.length.toString().length + 2, a = n.map((l, m) => y.rightPad((m + 1).toString(), s) + l), i = 0;
for (let l = 0; l < a.length; l++)
i = Math.max(a[l].length, i);
let p = a.slice(0, o - 1), u = a.slice(o - 1, o), c = a.slice(o);
console.log(p.join(`
`)), console.log(e.split(`
`)[0]), console.log(`%c ${y.rightPad(u[0], i)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(`
`));
}
function dI(r) {
return mi(r, () => r.createProgram(), "Unable to create WebGLProgram.");
}
function fI(r, e) {
if (ce(r, () => r.linkProgram(e)), !P().get("ENGINE_COMPILE_ONLY") && r.getProgramParameter(e, r.LINK_STATUS) === false)
throw console.log(r.getProgramInfoLog(e)), new Error("Failed to link vertex and fragment shaders.");
}
function ql(r, e) {
if (ce(r, () => r.validateProgram(e)), r.getProgramParameter(e, r.VALIDATE_STATUS) === false)
throw console.log(r.getProgramInfoLog(e)), new Error("Shader program validation failed.");
}
function hI(r, e) {
let t10 = mi(r, () => r.createBuffer(), "Unable to create WebGLBuffer");
return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, t10)), ce(r, () => r.bufferData(r.ARRAY_BUFFER, e, r.STATIC_DRAW)), t10;
}
function gI(r, e) {
let t10 = mi(r, () => r.createBuffer(), "Unable to create WebGLBuffer");
return ce(r, () => r.bindBuffer(r.ELEMENT_ARRAY_BUFFER, t10)), ce(r, () => r.bufferData(r.ELEMENT_ARRAY_BUFFER, e, r.STATIC_DRAW)), t10;
}
function m7() {
return P().getNumber("WEBGL_VERSION") === 2 ? 1 : 4;
}
function xI(r) {
return mi(r, () => r.createTexture(), "Unable to create WebGLTexture.");
}
function yI(r, e) {
let t10 = P().getNumber("WEBGL_MAX_TEXTURE_SIZE");
if (r <= 0 || e <= 0) {
let o = `[${r}x${e}]`;
throw new Error("Requested texture size " + o + " is invalid.");
}
if (r > t10 || e > t10) {
let o = `[${r}x${e}]`, n = `[${t10}x${t10}]`;
throw new Error("Requested texture size " + o + " greater than WebGL maximum on this browser / GPU " + n + ".");
}
}
function bI(r) {
return mi(r, () => r.createFramebuffer(), "Unable to create WebGLFramebuffer.");
}
function Hf(r, e, t10, o, n, s, a) {
let i = r.getAttribLocation(e, t10);
return i === -1 ? false : (ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, o)), ce(r, () => r.vertexAttribPointer(i, n, r.FLOAT, false, s, a)), ce(r, () => r.enableVertexAttribArray(i)), true);
}
function ZE(r, e, t10) {
eR(r, t10), ce(r, () => r.activeTexture(r.TEXTURE0 + t10)), ce(r, () => r.bindTexture(r.TEXTURE_2D, e));
}
function d7(r, e) {
eR(r, e), ce(r, () => r.activeTexture(r.TEXTURE0 + e)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function CI(r, e, t10) {
return mi(r, () => r.getUniformLocation(e, t10), 'uniform "' + t10 + '" not present in program.');
}
function wI(r, e, t10) {
return r.getUniformLocation(e, t10);
}
function SI(r, e, t10, o) {
ce(r, () => ZE(r, e, o)), ce(r, () => r.uniform1i(t10, o));
}
function f7(r) {
ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, null)), ce(r, () => r.viewport(0, 0, r.canvas.width, r.canvas.height)), ce(r, () => r.scissor(0, 0, r.canvas.width, r.canvas.height));
}
function jl(r, e, t10) {
ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, t10)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, e, 0));
}
function Kf(r, e) {
ce(r, () => r.bindFramebuffer(r.FRAMEBUFFER, e)), ce(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, null, 0));
}
function Nc(r) {
let e = r.checkFramebufferStatus(r.FRAMEBUFFER);
if (e !== r.FRAMEBUFFER_COMPLETE)
throw new Error("Error binding framebuffer: " + JE(r, e));
}
function JE(r, e) {
switch (e) {
case r.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
case r.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";
case r.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";
case r.FRAMEBUFFER_UNSUPPORTED:
return "FRAMEBUFFER_UNSUPPORTED";
default:
return `unknown error ${e}`;
}
}
function mi(r, e, t10) {
let o = ce(r, () => e());
if (o == null)
throw new Error(t10);
return o;
}
function eR(r, e) {
let t10 = r.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, o = e + r.TEXTURE0;
if (o < r.TEXTURE0 || o > t10) {
let n = `[gl.TEXTURE0, gl.TEXTURE${t10}]`;
throw new Error(`textureUnit must be in ${n}.`);
}
}
function di(r, e = 2) {
return y.sizeFromShape(r.slice(0, r.length - e));
}
function fi(r) {
if (r.length === 0)
throw Error("Cannot get rows and columns of an empty shape array.");
return [r.length > 1 ? r[r.length - 2] : 1, r[r.length - 1]];
}
function Tc(r) {
let e = [1, 1, 1];
return r.length === 0 || r.length === 1 && r[0] === 1 || (e = [di(r), ...fi(r)]), e;
}
function II(r, e = false) {
let t10 = P().getNumber("WEBGL_MAX_TEXTURE_SIZE"), o = P().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");
o === 1 / 0 && P().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE") && (o = t10 / 2), e && (t10 = t10 * 2, o = o * 2, r = r.map((i, p) => p >= r.length - 2 ? y.nearestLargerEven(r[p]) : r[p]), r.length === 1 && (r = [2, r[0]])), r.length !== 2 && (r = y.squeezeShape(r).newShape);
let n = y.sizeFromShape(r), s = null;
r.length <= 1 && n <= t10 ? s = [1, n] : r.length === 2 && r[0] <= t10 && r[1] <= t10 ? s = r : r.length === 3 && r[0] * r[1] <= t10 && r[2] <= t10 ? s = [r[0] * r[1], r[2]] : r.length === 3 && r[0] <= t10 && r[1] * r[2] <= t10 ? s = [r[0], r[1] * r[2]] : r.length === 4 && r[0] * r[1] * r[2] <= t10 && r[3] <= t10 ? s = [r[0] * r[1] * r[2], r[3]] : r.length === 4 && r[0] <= t10 && r[1] * r[2] * r[3] <= t10 && (s = [r[0], r[1] * r[2] * r[3]]);
let a = s != null && Math.max(...s) > o && Math.min(...s) <= (e ? 2 : 1) && Math.min(...s) > 0;
if (s == null || a)
if (e) {
let i = di(r), p = 2, u = 2;
r.length && ([p, u] = fi(r)), n = i * (p / 2) * (u / 2), s = y.sizeToSquarishShape(n).map((c) => c * 2);
} else
s = y.sizeToSquarishShape(n);
return s;
}
function Vf(r) {
return r % 2 === 0;
}
function fu(r, e) {
if (r = r.slice(-2), e = e.slice(-2), y.arraysEqual(r, e) || !r.length || !e.length || r[0] === 0 || r[1] === 0 || e[0] === 0 || e[1] === 0)
return true;
if (r.length !== e.length) {
let t10 = r[r.length - 1], o = e[e.length - 1];
if (t10 === o || Vf(t10) && Vf(o) && (r[0] === 1 || e[0] === 1))
return true;
}
return r[1] === e[1] && Vf(r[0]) && Vf(e[0]);
}
var Wf;
var Uf;
function vI(r) {
if (Wf == null) {
let e = Gr(r);
Wf = e.getParameter(e.MAX_TEXTURE_SIZE);
}
return Wf;
}
function h7() {
Wf = null;
}
function g7() {
Uf = null;
}
function kI(r) {
if (Uf == null) {
let e = Gr(r);
Uf = e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS);
}
return Math.min(16, Uf);
}
function NI(r) {
if (r === 0)
return 0;
let e, t10 = Gr(r);
return Hr(t10, "EXT_disjoint_timer_query_webgl2") && r === 2 ? e = 2 : Hr(t10, "EXT_disjoint_timer_query") ? e = 1 : e = 0, e;
}
function Hr(r, e) {
return r.getExtension(e) != null;
}
function qf(r) {
try {
if (Gr(r) != null)
return true;
} catch (e) {
return console.log("Error when getting WebGL context: ", e), false;
}
return false;
}
function TI(r) {
if (r === 0)
return false;
let e = Gr(r);
if (r === 1) {
if (!Hr(e, "OES_texture_float"))
return false;
} else if (!Hr(e, "EXT_color_buffer_float"))
return false;
return pI(e);
}
function _I(r) {
if (r === 0)
return false;
let e = Gr(r);
if (r === 1) {
if (!Hr(e, "OES_texture_float") || !Hr(e, "WEBGL_color_buffer_float"))
return false;
} else {
if (Hr(e, "EXT_color_buffer_float"))
return pI(e);
let o = "EXT_color_buffer_half_float";
if (Hr(e, o)) {
let n = e.getExtension(o);
return x7(e, n);
}
return false;
}
return pI(e);
}
function pI(r) {
let e = Kl(r), t10 = r.createTexture();
r.bindTexture(r.TEXTURE_2D, t10);
let o = 1, n = 1;
r.texImage2D(r.TEXTURE_2D, 0, e.internalFormatFloat, o, n, 0, e.textureFormatFloat, e.textureTypeFloat, null);
let s = r.createFramebuffer();
r.bindFramebuffer(r.FRAMEBUFFER, s), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, t10, 0);
let a = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE;
return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(t10), r.deleteFramebuffer(s), a;
}
function x7(r, e) {
let t10 = Kl(r, e), o = r.createTexture();
r.bindTexture(r.TEXTURE_2D, o);
let n = 1, s = 1;
r.texImage2D(r.TEXTURE_2D, 0, t10.internalFormatHalfFloat, n, s, 0, t10.textureFormatFloat, t10.textureTypeHalfFloat, null);
let a = r.createFramebuffer();
r.bindFramebuffer(r.FRAMEBUFFER, a), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, o, 0);
let i = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE;
return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(o), r.deleteFramebuffer(a), i;
}
function $I(r) {
return r !== 2 ? false : Gr(r).fenceSync != null;
}
function Ps(r, e) {
Array.isArray(r) || (r = [r]), r.forEach((t10) => {
t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGL backend.`);
});
}
var Se = P();
Se.registerFlag("HAS_WEBGL", () => Se.getNumber("WEBGL_VERSION") > 0);
Se.registerFlag("WEBGL_VERSION", () => qf(2) ? 2 : qf(1) ? 1 : 0);
Se.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
Se.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Se.get("WEBGL_VERSION") === 2);
Se.registerFlag("WEBGL_CPU_FORWARD", () => true);
Se.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false);
Se.registerFlag("WEBGL_PACK", () => Se.getBool("HAS_WEBGL"));
Se.registerFlag("WEBGL_PACK_NORMALIZATION", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_CLIP", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_REDUCE", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_LAZILY_UNPACK", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_CONV_IM2COL", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => vI(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => kI(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
let r = Se.getNumber("WEBGL_VERSION");
return r === 0 ? 0 : NI(r);
});
Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !Zi.isMobile());
Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => TI(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Se.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Se.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));
Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => _I(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_FENCE_API_ENABLED", () => $I(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => Se.getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? 4 : 0);
Se.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (r) => {
if (r < 0 && r !== -1)
throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`);
});
Se.registerFlag("WEBGL_FLUSH_THRESHOLD", () => Zi.isMobile() ? 1 : -1, (r) => {
if (r < 0 && r !== -1)
throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`);
});
Se.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128);
Se.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false);
Se.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5);
Se.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128);
Se.registerFlag("WEBGL_EXP_CONV", () => false);
Se.registerFlag("SOFTWARE_WEBGL_ENABLED", () => Se.getBool("IS_TEST"));
Se.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE", () => 1 / 0);
Se.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE", () => false);
Se.registerFlag("WEBGL2_ISNAN_CUSTOM", () => false);
Se.registerFlag("ENGINE_COMPILE_ONLY", () => false);
function It() {
let r, e, t10, o, n, s, a, i, p, u;
return P().getNumber("WEBGL_VERSION") === 2 ? (r = "#version 300 es", e = "in", t10 = "out", o = "in", n = "texture", s = "outputColor", a = "out vec4 outputColor;", i = P().getBool("WEBGL2_ISNAN_CUSTOM") ? `
bool isnan_custom(float val) {
uint floatToUint = floatBitsToUint(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
` : "", p = "", u = `
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`) : (r = "", e = "attribute", t10 = "varying", o = "varying", n = "texture2D", s = "gl_FragColor", a = "", i = `
#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));
}
`, p = `
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`, u = `
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`), { version: r, attribute: e, varyingVs: t10, varyingFs: o, texture2D: n, output: s, defineOutput: a, defineSpecialNaN: i, defineSpecialInf: p, defineRound: u };
}
function Os(r, e, t10 = "index") {
let o = y.computeStrides(e);
return o.map((n, s) => {
let a = `int ${r[s]} = ${t10} / ${n}`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t10} - ${r[s]} * ${n}` : `index -= ${r[s]} * ${n}`;
return `${a}; ${i};`;
}).join("");
}
function hp(r, e, t10 = "index") {
let o = y.computeStrides(e);
return o.map((n, s) => {
let a = `int ${r[s]} = ${t10} / outShapeStrides[${s}]`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t10} - ${r[s]} * outShapeStrides[${s}]` : `index -= ${r[s]} * outShapeStrides[${s}]`;
return `${a}; ${i};`;
}).join("");
}
function y7(r, e) {
let t10 = r.length, o = r.map((s) => `${e}[${s}]`), n = new Array(t10 - 1);
n[t10 - 2] = o[t10 - 1];
for (let s = t10 - 3; s >= 0; --s)
n[s] = `(${n[s + 1]} * ${o[s + 1]})`;
return n;
}
function tR(r, e, t10 = "index") {
let o = r.map((s, a) => a), n = y7(o, e);
return n.map((s, a) => {
let i = `int ${r[a]} = ${t10} / ${n[a]}`, p = a === n.length - 1 ? `int ${r[a + 1]} = ${t10} - ${r[a]} * ${n[a]}` : `index -= ${r[a]} * ${n[a]}`;
return `${i}; ${p};`;
}).join("");
}
function $c(r) {
let e = y.computeStrides(r).map((t10) => t10.toString());
return `
int getFlatIndex(ivec3 coords) {
return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
}
`;
}
function Ec() {
return `
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`;
}
var jf = `
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: rR } = C;
function oR(r, e, t10) {
let o = [];
if (r.forEach((d) => {
let f = y.sizeFromShape(d.shapeInfo.logicalShape);
if (d.shapeInfo.isUniform ? o.push(`uniform float ${d.name}${f > 1 ? `[${f}]` : ""};`) : (o.push(`uniform sampler2D ${d.name};`), o.push(`uniform int offset${d.name};`)), t10.enableShapeUniforms) {
let { uniformShape: h } = Xf(t10.packedInputs, d.shapeInfo.logicalShape, d.shapeInfo.texShape);
switch (h.length) {
case 1:
o.push(`uniform int ${d.name}Shape;`);
break;
case 2:
o.push(`uniform ivec2 ${d.name}Shape;`);
break;
case 3:
o.push(`uniform ivec3 ${d.name}Shape;`);
break;
case 4:
o.push(`uniform ivec4 ${d.name}Shape;`);
break;
default:
break;
}
o.push(`uniform ivec2 ${d.name}TexShape;`);
}
}), t10.enableShapeUniforms) {
switch (e.logicalShape.length) {
case 1:
o.push("uniform int outShape;");
break;
case 2:
o.push("uniform ivec2 outShape;"), o.push("uniform int outShapeStrides;");
break;
case 3:
o.push("uniform ivec3 outShape;"), o.push("uniform ivec2 outShapeStrides;");
break;
case 4:
o.push("uniform ivec4 outShape;"), o.push("uniform ivec3 outShapeStrides;");
break;
default:
break;
}
o.push("uniform ivec2 outTexShape;");
}
t10.customUniforms && t10.customUniforms.forEach((d) => {
o.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`);
});
let n = o.join(`
`), s = r.map((d) => b7(d, e, t10.packedInputs, t10.enableShapeUniforms)).join(`
`), a = e.texShape, i = It(), p = S7(i), u, c, l = k7(i);
return e.isPacked ? (u = C7(e.logicalShape, a, t10.enableShapeUniforms), c = v7(i)) : (u = w7(e.logicalShape, a, t10.enableShapeUniforms), c = I7(i)), t10.packedInputs && (l += $7), [l, p, c, n, u, s, t10.userCode].join(`
`);
}
function Dc(r, e = false) {
let t10 = r.shapeInfo.logicalShape;
switch (t10.length) {
case 0:
return V7(r, e);
case 1:
return U7(r, e);
case 2:
return H7(r, e);
case 3:
return q7(r, e);
case 4:
return X7(r, e);
case 5:
return Y7(r);
case 6:
return Q7(r);
default:
throw new Error(`${t10.length}-D input sampling is not yet supported`);
}
}
function nR(r, e) {
switch (r.shapeInfo.logicalShape.length) {
case 0:
return z7(r);
case 1:
return W7(r, e);
case 2:
return G7(r, e);
case 3:
return K7(r, e);
default:
return j7(r, e);
}
}
function b7(r, e, t10 = false, o) {
let n = "";
t10 ? n += nR(r, o) : n += Dc(r, o);
let s = r.shapeInfo.logicalShape, a = e.logicalShape;
return s.length <= a.length && (t10 ? n += Z7(r, e) : n += J7(r, e)), n;
}
function C7(r, e, t10) {
switch (r.length) {
case 0:
return sR();
case 1:
return E7(r, e, t10);
case 2:
return L7(r, e, t10);
case 3:
return D7(r, e, t10);
default:
return F7(r, e, t10);
}
}
function w7(r, e, t10) {
switch (r.length) {
case 0:
return sR();
case 1:
return R7(r, e, t10);
case 2:
return B7(r, e, t10);
case 3:
return A7(r, e, t10);
case 4:
return P7(r, e, t10);
case 5:
return O7(r, e);
case 6:
return M7(r, e);
default:
throw new Error(`${r.length}-D output sampling is not yet supported`);
}
}
function S7(r) {
return `
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${r.texture2D}(textureSampler, uv).r;
}
`;
}
function I7(r) {
return `
void setOutput(float val) {
${r.output} = vec4(val, 0, 0, 0);
}
`;
}
function v7(r) {
return `
void setOutput(vec4 val) {
${r.output} = val;
}
`;
}
function k7(r) {
return `${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);
}
${N7}
${T7}
${_7}
`;
}
var N7 = `
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);
}
`;
var T7 = `
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);
}
`;
var _7 = `
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);
}
`;
var $7 = `
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 sR() {
return `
int getOutputCoords() {
return 0;
}
`;
}
function E7(r, e, t10) {
let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)];
return o[0] === 1 ? t10 ? `
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
` : `
int getOutputCoords() {
return 2 * int(resultUV.x * ${o[1]}.0);
}
` : o[1] === 1 ? t10 ? `
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
` : `
int getOutputCoords() {
return 2 * int(resultUV.y * ${o[0]}.0);
}
` : t10 ? `
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
` : `
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${o[0]}, ${o[1]}));
return 2 * (resTexRC.x * ${o[1]} + resTexRC.y);
}
`;
}
function R7(r, e, t10) {
return e[0] === 1 ? t10 ? `
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
` : `
int getOutputCoords() {
return int(resultUV.x * ${e[1]}.0);
}
` : e[1] === 1 ? t10 ? `
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
` : `
int getOutputCoords() {
return int(resultUV.y * ${e[0]}.0);
}
` : t10 ? `
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
` : `
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
return resTexRC.x * ${e[1]} + resTexRC.y;
}
`;
}
function D7(r, e, t10) {
if (t10)
return `
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;
let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)], n = Math.ceil(r[2] / 2), s = n * Math.ceil(r[1] / 2);
return `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${o[0]}, ${o[1]}));
int index = resTexRC.x * ${o[1]} + resTexRC.y;
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${n});
int c = imod(index, ${n}) * 2;
return ivec3(b, r, c);
}
`;
}
function A7(r, e, t10) {
if (t10)
return `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${hp(["r", "c", "d"], r)}
return ivec3(r, c, d);
}
`;
let o = Os(["r", "c", "d"], r);
return `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${o}
return ivec3(r, c, d);
}
`;
}
function F7(r, e, t10) {
if (t10)
return `
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;
let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)], n = Math.ceil(r[r.length - 1] / 2), s = n * Math.ceil(r[r.length - 2] / 2), a = s, i = "", p = "b, r, c";
for (let u = 2; u < r.length - 1; u++)
a *= r[r.length - u - 1], i = `
int b${u} = index / ${a};
index -= b${u} * ${a};
` + i, p = `b${u}, ` + p;
return `
ivec${r.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${o[0]}, ${o[1]}));
int index = resTexRC.x * ${o[1]} + resTexRC.y;
${i}
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${n});
int c = imod(index, ${n}) * 2;
return ivec${r.length}(${p});
}
`;
}
function P7(r, e, t10) {
if (t10)
return `
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${hp(["r", "c", "d", "d2"], r)}
return ivec4(r, c, d, d2);
}
`;
let o = Os(["r", "c", "d", "d2"], r);
return `
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${o}
return ivec4(r, c, d, d2);
}
`;
}
function O7(r, e) {
let t10 = Os(["r", "c", "d", "d2", "d3"], r);
return `
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t10}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`;
}
function M7(r, e) {
let t10 = Os(["r", "c", "d", "d2", "d3", "d4"], r);
return `
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t10}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`;
}
function L7(r, e, t10) {
let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)];
if (y.arraysEqual(r, e))
return t10 ? `
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
` : `
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${o[0]}, ${o[1]}));
}
`;
let n = Math.ceil(r[1] / 2);
return t10 ? `
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
` : `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${o[0]}, ${o[1]}));
int index = resTexRC.x * ${o[1]} + resTexRC.y;
int r = 2 * (index / ${n});
int c = imod(index, ${n}) * 2;
return ivec2(r, c);
}
`;
}
function B7(r, e, t10) {
return y.arraysEqual(r, e) ? t10 ? `
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
` : `
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
}
` : r[1] === 1 ? t10 ? `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
` : `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(index, 0);
}
` : r[0] === 1 ? t10 ? `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
` : `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(0, index);
}
` : t10 ? `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
` : `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
int r = index / ${r[1]};
int c = index - r * ${r[1]};
return ivec2(r, c);
}
`;
}
function gp(r) {
return `offset${r}`;
}
function z7(r) {
let e = r.name, t10 = "get" + e.charAt(0).toUpperCase() + e.slice(1), o = It();
return `
vec4 ${t10}() {
return ${o.texture2D}(${e}, halfCR);
}
`;
}
function V7(r, e) {
let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1);
if (r.shapeInfo.isUniform)
return `float ${o}() {return ${t10};}`;
let [n, s] = r.shapeInfo.texShape;
if (n === 1 && s === 1)
return `
float ${o}() {
return sampleTexture(${t10}, halfCR);
}
`;
let a = gp(t10);
if (e)
return `
float ${o}() {
vec2 uv = uvFromFlat(${t10}TexShape[0], ${t10}TexShape[1], ${a});
return sampleTexture(${t10}, uv);
}
`;
let [i, p] = r.shapeInfo.texShape;
return `
float ${o}() {
vec2 uv = uvFromFlat(${i}, ${p}, ${a});
return sampleTexture(${t10}, uv);
}
`;
}
function W7(r, e) {
let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = r.shapeInfo.texShape, s = It();
if (e)
return `
vec4 ${o}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${t10}TexShape[0]) / 2.0), ceil(float(${t10}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${s.texture2D}(${t10}, uv);
}
`;
let a = [Math.ceil(n[0] / 2), Math.ceil(n[1] / 2)];
return `
vec4 ${o}(int index) {
vec2 uv = packedUVfrom1D(
${a[0]}, ${a[1]}, index);
return ${s.texture2D}(${t10}, uv);
}
`;
}
function U7(r, e) {
let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1);
if (r.shapeInfo.isUniform)
return `
float ${o}(int index) {
${Ac(r)}
}
`;
let n = r.shapeInfo.texShape, s = n[0], a = n[1];
if (a === 1 && s === 1)
return `
float ${o}(int index) {
return sampleTexture(${t10}, halfCR);
}
`;
let i = gp(t10);
return a === 1 ? e ? `
float ${o}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t10}TexShape[0]));
return sampleTexture(${t10}, uv);
}
` : `
float ${o}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${s}.0);
return sampleTexture(${t10}, uv);
}
` : s === 1 ? e ? `
float ${o}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t10}TexShape[1]), 0.5);
return sampleTexture(${t10}, uv);
}
` : `
float ${o}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${a}.0, 0.5);
return sampleTexture(${t10}, uv);
}
` : e ? `
float ${o}(int index) {
vec2 uv = uvFromFlat(${t10}TexShape[0], ${t10}TexShape[1], index + ${i});
return sampleTexture(${t10}, uv);
}
` : `
float ${o}(int index) {
vec2 uv = uvFromFlat(${s}, ${a}, index + ${i});
return sampleTexture(${t10}, uv);
}
`;
}
function G7(r, e) {
let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = s[0], i = s[1], p = It();
if (s != null && y.arraysEqual(t10, s))
return e ? `
vec4 ${n}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);
return ${p.texture2D}(${o}, uv);
}
` : `
vec4 ${n}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${a}.0);
return ${p.texture2D}(${o}, uv);
}
`;
if (e)
return `
vec4 ${n}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${o}TexShape[0]) / 2.0), ceil(float(${o}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${o}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${p.texture2D}(${o}, uv);
}
`;
let u = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)], c = Math.ceil(t10[1] / 2);
return `
vec4 ${n}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
return ${p.texture2D}(${o}, uv);
}
`;
}
function H7(r, e) {
let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape;
if (s != null && y.arraysEqual(t10, s)) {
if (e)
return `
float ${n}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);
return sampleTexture(${o}, uv);
}
`;
let m = s[0], d = s[1];
return `
float ${n}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${m}.0);
return sampleTexture(${o}, uv);
}
`;
}
let { newShape: a, keptDims: i } = y.squeezeShape(t10), p = a;
if (p.length < t10.length) {
let m = Fc(r, p), d = ["row", "col"];
return `
${Dc(m, e)}
float ${n}(int row, int col) {
return ${n}(${Pc(d, i)});
}
`;
}
if (r.shapeInfo.isUniform)
return `
float ${n}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t10[1]}, 1)));
${Ac(r)}
}
`;
let u = s[0], c = s[1], l = gp(o);
return c === 1 ? e ? `
float ${n}(int row, int col) {
float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${o}TexShape[0]));
return sampleTexture(${o}, uv);
}
` : `
float ${n}(int row, int col) {
float index = dot(vec3(row, col, ${l}), vec3(${t10[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
return sampleTexture(${o}, uv);
}
` : u === 1 ? e ? `
float ${n}(int row, int col) {
float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${o}TexShape[1]), 0.5);
return sampleTexture(${o}, uv);
}
` : `
float ${n}(int row, int col) {
float index = dot(vec3(row, col, ${l}), vec3(${t10[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${o}, uv);
}
` : e ? `
float ${n}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o}Shape[1] + col + ${l};
vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index);
return sampleTexture(${o}, uv);
}
` : `
float ${n}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t10[1]} + col + ${l};
vec2 uv = uvFromFlat(${u}, ${c}, index);
return sampleTexture(${o}, uv);
}
`;
}
function K7(r, e) {
let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)];
if (t10[0] === 1) {
let m = t10.slice(1), d = [1, 2], f = Fc(r, m), h = ["b", "row", "col"];
return `
${nR(f, e)}
vec4 ${n}(int b, int row, int col) {
return ${n}(${Pc(h, d)});
}
`;
}
let i = It();
if (e)
return `
vec4 ${n}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${o}TexShape[0]) / 2.0), ceil(float(${o}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${o}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${o}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${i.texture2D}(${o}, uv);
}
`;
let p = a[0], u = a[1], c = Math.ceil(t10[2] / 2), l = c * Math.ceil(t10[1] / 2);
return `
vec4 ${n}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${p}, ${u}, ${l}, ${c}, b, row, col);
return ${i.texture2D}(${o}, uv);
}
`;
}
function q7(r, e) {
let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t10[1] * t10[2], a = t10[2], { newShape: i, keptDims: p } = y.squeezeShape(t10), u = i;
if (u.length < t10.length) {
let h = Fc(r, u), g = ["row", "col", "depth"];
return `
${Dc(h, e)}
float ${n}(int row, int col, int depth) {
return ${n}(${Pc(g, p)});
}
`;
}
if (r.shapeInfo.isUniform)
return `
float ${n}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${s}, ${a}, 1)));
${Ac(r)}
}
`;
let c = r.shapeInfo.texShape, l = c[0], m = c[1], d = r.shapeInfo.flatOffset;
if (m === s && d == null)
return e ? `
float ${n}(int row, int col, int depth) {
int stride1 = ${o}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${o}TexShape[1], ${o}TexShape[0]);
return sampleTexture(${o}, uv);
}
` : `
float ${n}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${l}.0);
return sampleTexture(${o}, uv);
}
`;
if (m === a && d == null)
return e ? `
float ${n}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${o}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);
return sampleTexture(${o}, uv);
}
` : `
float ${n}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t10[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${l}.0);
return sampleTexture(${o}, uv);
}
`;
let f = gp(o);
return e ? `
float ${n}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${o}Shape[1] * ${o}Shape[2];
int stride1 = ${o}Shape[2];
int index = row * stride0 + col * stride1 + depth + ${f};
vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index);
return sampleTexture(${o}, uv);
}
` : `
float ${n}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s} + col * ${a} + depth + ${f};
vec2 uv = uvFromFlat(${l}, ${m}, index);
return sampleTexture(${o}, uv);
}
`;
}
function j7(r, e) {
let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = It();
if (e)
return `
vec4 ${o}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${t10}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${t10}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${t10}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${t10}TexShape[0]) / 2.0), ceil(float(${t10}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 ${n.texture2D}(${t10}, uv);
}
`;
let s = r.shapeInfo.logicalShape, a = s.length, i = r.shapeInfo.texShape, p = [Math.ceil(i[0] / 2), Math.ceil(i[1] / 2)], u = p[0], c = p[1], l = Math.ceil(s[a - 1] / 2), m = l * Math.ceil(s[a - 2] / 2), d = "int b, int row, int col", f = `b * ${m} + (row / 2) * ${l} + (col / 2)`;
for (let h = 2; h < a - 1; h++)
d = `int b${h}, ` + d, m *= s[a - h - 1], f = `b${h} * ${m} + ` + f;
return `
vec4 ${o}(${d}) {
int index = ${f};
int texR = index / ${c};
int texC = index - texR * ${c};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
return ${n.texture2D}(${t10}, uv);
}
`;
}
function X7(r, e) {
let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t10[3], a = t10[2] * s, i = t10[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(t10);
if (p.length < t10.length) {
let b = Fc(r, p), w = ["row", "col", "depth", "depth2"];
return `
${Dc(b, e)}
float ${n}(int row, int col, int depth, int depth2) {
return ${n}(${Pc(w, u)});
}
`;
}
if (r.shapeInfo.isUniform)
return `
float ${n}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${a}, ${s}, 1)));
${Ac(r)}
}
`;
let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], d = l[1], f = `int stride2 = ${o}Shape[3];`, h = `int stride1 = ${o}Shape[2] * stride2;`, g = `int stride0 = ${o}Shape[1] * stride1;`;
if (d === i && c == null)
return e ? `
float ${n}(int row, int col, int depth, int depth2) {
${f}
${h}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${o}TexShape[1], ${o}TexShape[0]);
return sampleTexture(${o}, uv);
}
` : `
float ${n}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${a}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${m}.0);
return sampleTexture(${o}, uv);
}
`;
if (d === s && c == null)
return e ? `
float ${n}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${o}Shape[1] * ${o}Shape[2], ${o}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${o}TexShape[1], ${o}TexShape[0]);
return sampleTexture(${o}, uv);
}
` : `
float ${n}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t10[1] * t10[2]}, ${t10[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${m}.0);
return sampleTexture(${o}, uv);
}
`;
let x = gp(o);
return e ? `
float ${n}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${h}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index + ${x});
return sampleTexture(${o}, uv);
}
` : `
float ${n}(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}, ${d}, index + ${x});
return sampleTexture(${o}, uv);
}
`;
}
function Y7(r) {
let e = r.shapeInfo.logicalShape, t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = e[4], s = e[3] * n, a = e[2] * s, i = e[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(e);
if (p.length < e.length) {
let h = Fc(r, p), g = ["row", "col", "depth", "depth2", "depth3"];
return `
${Dc(h)}
float ${o}(int row, int col, int depth, int depth2, int depth3) {
return ${o}(${Pc(g, u)});
}
`;
}
if (r.shapeInfo.isUniform)
return `
float ${o}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${a}, ${s}, ${n})) +
depth3;
${Ac(r)}
}
`;
let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], d = l[1];
if (d === i && c == null)
return `
float ${o}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${a}, ${s}, ${n}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${m}.0);
return sampleTexture(${t10}, uv);
}
`;
if (d === n && c == null)
return `
float ${o}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${e[1] * e[2] * e[3]},
${e[2] * e[3]}, ${e[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${m}.0);
return sampleTexture(${t10}, uv);
}
`;
let f = gp(t10);
return `
float ${o}(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 * ${n} + depth3 + ${f};
vec2 uv = uvFromFlat(${m}, ${d}, index);
return sampleTexture(${t10}, uv);
}
`;
}
function Q7(r) {
let e = r.shapeInfo.logicalShape, t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), { newShape: n, keptDims: s } = y.squeezeShape(e);
if (n.length < e.length) {
let g = Fc(r, n), x = ["row", "col", "depth", "depth2", "depth3", "depth4"];
return `
${Dc(g)}
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${o}(${Pc(x, s)});
}
`;
}
let a = e[5], i = e[4] * a, p = e[3] * i, u = e[2] * p, c = e[1] * u;
if (r.shapeInfo.isUniform)
return `
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${u}, ${p}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${a}, 1)));
${Ac(r)}
}
`;
let l = r.shapeInfo.flatOffset, m = r.shapeInfo.texShape, d = m[0], f = m[1];
if (f === c && l == null)
return `
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${p}, ${i}, ${a})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${d}.0);
return sampleTexture(${t10}, uv);
}
`;
if (f === a && l == null)
return `
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${e[1] * e[2] * e[3] * e[4]},
${e[2] * e[3] * e[4]},
${e[3] * e[4]},
${e[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${d}.0);
return sampleTexture(${t10}, uv);
}
`;
let h = gp(t10);
return `
float ${o}(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 * ${p} +
depth2 * ${i} + depth3 * ${a} + depth4 + ${h};
vec2 uv = uvFromFlat(${d}, ${f}, index);
return sampleTexture(${t10}, uv);
}
`;
}
function Ac(r) {
let e = r.name, t10 = y.sizeFromShape(r.shapeInfo.logicalShape);
return t10 < 2 ? `return ${e};` : `
for (int i = 0; i < ${t10}; i++) {
if (i == index) {
return ${e}[i];
}
}
`;
}
function Z7(r, e) {
let t10 = r.name, o = t10.charAt(0).toUpperCase() + t10.slice(1), n = "get" + o + "AtOutCoords", s = r.shapeInfo.logicalShape.length, a = e.logicalShape.length, i = rR(r.shapeInfo.logicalShape, e.logicalShape), p = Re(a), u = a - s, c, l = ["x", "y", "z", "w", "u", "v"];
s === 0 ? c = "" : a < 2 && i.length >= 1 ? c = "coords = 0;" : c = i.map((b) => `coords.${l[b + u]} = 0;`).join(`
`);
let m = "";
a < 2 && s > 0 ? m = "coords" : m = r.shapeInfo.logicalShape.map((b, w) => `coords.${l[w + u]}`).join(", ");
let d = "return outputValue;", h = y.sizeFromShape(r.shapeInfo.logicalShape) === 1, x = y.sizeFromShape(e.logicalShape) === 1;
if (s === 1 && !h && !x)
d = `
return vec4(outputValue.xy, outputValue.xy);
`;
else if (h && !x)
a === 1 ? d = `
return vec4(outputValue.x, outputValue.x, 0., 0.);
` : d = `
return vec4(outputValue.x);
`;
else if (i.length) {
let b = s - 2, w = s - 1;
i.indexOf(b) > -1 && i.indexOf(w) > -1 ? d = "return vec4(outputValue.x);" : i.indexOf(b) > -1 ? d = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : i.indexOf(w) > -1 && (d = "return vec4(outputValue.xx, outputValue.zz);");
}
return `
vec4 ${n}() {
${p} coords = getOutputCoords();
${c}
vec4 outputValue = get${o}(${m});
${d}
}
`;
}
function J7(r, e) {
let t10 = r.name, o = t10.charAt(0).toUpperCase() + t10.slice(1), n = "get" + o + "AtOutCoords", s = e.texShape, a = r.shapeInfo.texShape, i = r.shapeInfo.logicalShape.length, p = e.logicalShape.length;
if (!r.shapeInfo.isUniform && i === p && r.shapeInfo.flatOffset == null && y.arraysEqual(a, s))
return `
float ${n}() {
return sampleTexture(${t10}, resultUV);
}
`;
let u = Re(p), c = rR(r.shapeInfo.logicalShape, e.logicalShape), l = p - i, m, d = ["x", "y", "z", "w", "u", "v"];
i === 0 ? m = "" : p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((h) => `coords.${d[h + l]} = 0;`).join(`
`);
let f = "";
return p < 2 && i > 0 ? f = "coords" : f = r.shapeInfo.logicalShape.map((h, g) => `coords.${d[g + l]}`).join(", "), `
float ${n}() {
${u} coords = getOutputCoords();
${m}
return get${o}(${f});
}
`;
}
function Re(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 ${r} is not yet supported`);
}
function Xf(r, e, t10) {
let { newShape: o, keptDims: n } = y.squeezeShape(e), s = e.length, a = r && s === 3 && e[0] === 1, i = a ? e.slice(1) : o, p = !r && s > 1 && !y.arraysEqual(e, t10) && o.length < s || a;
return { useSqueezeShape: p, uniformShape: p ? i : e, keptDims: n };
}
function Fc(r, e) {
let t10 = JSON.parse(JSON.stringify(r));
return t10.shapeInfo.logicalShape = e, t10;
}
function Pc(r, e) {
return e.map((t10) => r[t10]).join(", ");
}
function iR(r, e, t10, o) {
let n = t10.map((c, l) => {
let m = { logicalShape: c.shape, texShape: c.isUniform ? null : c.texData.texShape, isUniform: c.isUniform, isPacked: c.isUniform ? false : c.texData.isPacked, flatOffset: null };
return c.texData != null && c.texData.slice != null && c.texData.slice.flatOffset > 0 && (m.flatOffset = c.texData.slice.flatOffset), { name: e.variableNames[l], shapeInfo: m };
}), s = n.map((c) => c.shapeInfo), a = { logicalShape: o.shape, texShape: o.texData.texShape, isUniform: false, isPacked: o.texData.isPacked, flatOffset: null }, i = oR(n, a, e), p = mI(r.gl, i), u = r.createProgram(p);
return P().get("ENGINE_COMPILE_ONLY") ? { program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a, variablesLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : (r.buildVao(u), Object.assign({ program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a }, EI(r, e, u)));
}
function EI(r, e, t10) {
let o = [], n = [], s, a, i, p = null, u = null;
u = r.getUniformLocation(t10, "NAN", false), P().getNumber("WEBGL_VERSION") === 1 && (p = r.getUniformLocation(t10, "INFINITY", false));
let c = false;
for (let l of e.variableNames) {
let m = { name: l, uniform: r.getUniformLocation(t10, l, c), offset: r.getUniformLocation(t10, `offset${l}`, c) };
e.enableShapeUniforms && (m.shape = r.getUniformLocation(t10, `${l}Shape`, c), m.texShape = r.getUniformLocation(t10, `${l}TexShape`, c)), o.push(m);
}
if (e.enableShapeUniforms && (s = r.getUniformLocation(t10, "outShape", c), i = r.getUniformLocation(t10, "outShapeStrides", c), a = r.getUniformLocation(t10, "outTexShape", c)), e.customUniforms)
for (let l of e.customUniforms)
n.push(r.getUniformLocation(t10, l.name, c));
return { variablesLocations: o, customUniformLocations: n, infLoc: p, nanLoc: u, outShapeLocation: s, outShapeStridesLocation: i, outTexShapeLocation: a };
}
function aR(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((t10, o) => {
let n = t10.logicalShape, s = e[o], a = s.shape;
if (!y.arraysEqual(n, a))
throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`);
if (t10.isUniform && s.isUniform)
return;
let i = t10.texShape, p = s.isUniform ? null : s.texData.texShape;
if (!y.arraysEqual(i, p))
throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${p} must match`);
});
}
function uR(r, e, t10, o, n) {
e.program.enableShapeUniforms || (aR(e.inShapeInfos, t10), aR([e.outShapeInfo], [o]));
let s = o.texData.texture, a = o.texData.texShape;
o.texData.isPacked ? r.setOutputPackedMatrixTexture(s.texture, a[0], a[1]) : r.setOutputMatrixTexture(s.texture, a[0], a[1]), r.setProgram(e.webGLProgram), r.bindVertexArray(e.webGLProgram.vao), P().getNumber("WEBGL_VERSION") === 1 && e.infLoc !== null && r.gl.uniform1f(e.infLoc, 1 / 0), e.nanLoc !== null && r.gl.uniform1f(e.nanLoc, NaN);
for (let p = 0; p < t10.length; ++p) {
let u = t10[p], { uniform: c, offset: l, shape: m, texShape: d } = e.variablesLocations[p];
if (m) {
let { uniformShape: f } = Xf(e.program.packedInputs, u.shape, u.texData.texShape);
switch (f.length) {
case 1:
r.gl.uniform1iv(m, new Int32Array(f));
break;
case 2:
r.gl.uniform2iv(m, new Int32Array(f));
break;
case 3:
r.gl.uniform3iv(m, new Int32Array(f));
break;
case 4:
r.gl.uniform4iv(m, new Int32Array(f));
break;
default:
break;
}
}
if (d && r.gl.uniform2i(d, u.texData.texShape[0], u.texData.texShape[1]), c != null) {
if (u.isUniform) {
if (y.sizeFromShape(u.shape) < 2)
r.gl.uniform1f(c, u.uniformValues[0]);
else {
let f = u.uniformValues;
f instanceof Float32Array || (f = new Float32Array(f)), r.gl.uniform1fv(c, f);
}
continue;
}
u.texData.slice != null && l != null && r.gl.uniform1i(l, u.texData.slice.flatOffset), r.setInputMatrixTexture(u.texData.texture.texture, c, p);
}
}
let i = e.outShapeLocation;
if (i)
switch (o.shape.length) {
case 1:
r.gl.uniform1iv(i, new Int32Array(o.shape));
break;
case 2:
r.gl.uniform2iv(i, new Int32Array(o.shape));
break;
case 3:
r.gl.uniform3iv(i, new Int32Array(o.shape));
break;
case 4:
r.gl.uniform4iv(i, new Int32Array(o.shape));
break;
default:
break;
}
if (e.outShapeStridesLocation) {
let p = y.computeStrides(o.shape);
switch (o.shape.length) {
case 2:
r.gl.uniform1iv(e.outShapeStridesLocation, new Int32Array(p));
break;
case 3:
r.gl.uniform2iv(e.outShapeStridesLocation, new Int32Array(p));
break;
case 4:
r.gl.uniform3iv(e.outShapeStridesLocation, new Int32Array(p));
break;
default:
break;
}
}
if (e.outTexShapeLocation && r.gl.uniform2i(e.outTexShapeLocation, o.texData.texShape[0], o.texData.texShape[1]), e.program.customUniforms && n)
for (let p = 0; p < e.program.customUniforms.length; ++p) {
let u = e.program.customUniforms[p], c = e.customUniformLocations[p], l = n[p];
if (u.type === "float")
r.gl.uniform1fv(c, l);
else if (u.type === "vec2")
r.gl.uniform2fv(c, l);
else if (u.type === "vec3")
r.gl.uniform3fv(c, l);
else if (u.type === "vec4")
r.gl.uniform4fv(c, l);
else if (u.type === "int")
r.gl.uniform1iv(c, l);
else if (u.type === "ivec2")
r.gl.uniform2iv(c, l);
else if (u.type === "ivec3")
r.gl.uniform3iv(c, l);
else if (u.type === "ivec4")
r.gl.uniform4iv(c, l);
else
throw Error(`uniform type ${u.type} is not supported yet.`);
}
r.executeProgram();
}
function pR(r, e, t10) {
let o = "";
e.concat(t10).forEach((a) => {
let i = a.texData != null && a.texData.slice != null && a.texData.slice.flatOffset > 0;
if (r.enableShapeUniforms && !a.isUniform) {
let p = a.texData.texShape, { useSqueezeShape: u, uniformShape: c, keptDims: l } = Xf(r.packedInputs, a.shape, p), m = "", d = "", f = "";
if (c.length === 1 && r.packedInputs) {
let k = [Math.ceil(p[0] / 2), Math.ceil(p[1] / 2)];
m = `${k[0] > 1}_${k[1] > 1}`;
} else if (c.length === 2 && !r.packedInputs)
d = `${c[0] > 1}_${c[1] > 1}`;
else if (c.length > 2 && !r.packedInputs) {
let k = y.computeStrides(c);
f = `${k[0] === p[1]}_${k[k.length - 1] === p[1]}`;
}
let h = a.shape.length, g = c.length === 2 && y.arraysEqual(a.shape, p), x = y.sizeFromShape(a.shape) === 1, b = C.getBroadcastDims(a.shape, t10.shape), w = !r.packedInputs && h === t10.shape.length && y.arraysEqual(p, t10.texData.texShape), S = r.packedInputs || c.length > 2 ? "" : `${p[0] > 1}_${p[1] > 1}`;
o += `${h}_${w}_${u ? l : ""}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`;
} else {
let p = a.isUniform ? "uniform" : a.texData.texShape;
o += `${a.shape}_${p}_${i}`;
}
});
let n = r.userCode, s = r.constructor.name;
return s += "_" + o + "_" + n + `${P().getNumber("WEBGL_VERSION")}`, s;
}
function ut(r) {
return P().getBool("WEBGL_USE_SHAPES_UNIFORMS") && r <= 4;
}
var Yf = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = du.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
let t10 = It();
this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = `
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms ? hp(["r", "c", "d"], e) : Os(["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);
}
${t10.output} = result;
}
`;
}
};
var Qf = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = du.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
let t10 = It();
this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = `
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms ? hp(["r", "c", "d"], e) : Os(["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));
}
${t10.output} = result;
}
`;
}
};
var Zf = class {
constructor(e) {
this.variableNames = ["A"], this.outTexUsage = mr.DOWNLOAD;
let t10 = It();
this.outputShape = e, this.userCode = `
${jf}
void main() {
float x = getAAtOutCoords();
${t10.output} = encode_float(x);
}
`;
}
};
var Jf = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = mr.DOWNLOAD;
let t10 = It();
this.outputShape = e, this.userCode = `
${jf}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t10.output} = encode_float(x);
}
`;
}
};
var rZ = { R: 0, G: 1, B: 2, A: 3 };
var Xl = class {
constructor(e, t10 = false, o = "RGBA") {
this.variableNames = ["A"], this.customUniforms = [{ name: "texShape", type: "ivec2" }];
let n = It();
this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length);
let s = "result";
t10 && (s = "floor(result * 255. + 0.5)");
let a = "";
for (let i = 0; i < o.length; i++) {
let p = o[i];
a += `
if(offset == ${i}) {
result = values[${rZ[p]}];
}`;
}
this.userCode = `
${this.enableShapeUniforms ? Ec() : $c(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
float result = 0.;
int offset = imod(flatIndex, ${o.length});
flatIndex = idiv(flatIndex, ${o.length}, 1.);
int r = flatIndex / texShape[1];
if (r < texShape[0]) {
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
${a}
}
${n.output} = vec4(${s}, 0., 0., 0.);
}
`;
}
};
var eh = class {
constructor(e, t10 = false) {
this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
let o = It();
this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length);
let n = "", s = "result";
t10 && (s = "floor(result * 255. + 0.5)");
for (let a = 0; a <= 1; a++)
for (let i = 0; i <= 1; i++) {
let p = a * 2 + i;
n += `
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 = ${o.texture2D}(A, uv);
if (offset == 0) {
result[${p}] = values[0];
} else if (offset == 1) {
result[${p}] = values[1];
} else if (offset == 2) {
result[${p}] = values[2];
} else {
result[${p}] = values[3];
}
}
}
`;
}
this.userCode = `
${this.enableShapeUniforms ? Ec() : $c(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${n}
${o.output} = ${s};
}
`;
}
};
var qI = {};
He(qI, { bindVertexProgramAttributeStreams: () => BI, createBufferFromOutputTexture: () => WI, createFloat16MatrixTexture: () => PI, createFloat16PackedMatrixTexture: () => LI, createFloat32MatrixTexture: () => FI, createIndexBuffer: () => AI, createPackedMatrixTexture: () => MI, createUnsignedBytesMatrixTexture: () => OI, createVertexBuffer: () => DI, createVertexShader: () => RI, downloadByteEncodedFloatMatrixFromOutputTexture: () => GI, downloadFloat32MatrixFromBuffer: () => UI, downloadMatrixFromPackedOutputTexture: () => KI, downloadPackedMatrixFromBuffer: () => HI, getInternalFormatForFloat16MatrixTexture: () => rh, getInternalFormatForFloat16PackedMatrixTexture: () => sh, getInternalFormatForFloat32MatrixTexture: () => th, getInternalFormatForPackedMatrixTexture: () => nh, getInternalFormatForUnsignedBytesMatrixTexture: () => oh, uploadDenseMatrixToTexture: () => zI, uploadPixelDataToTexture: () => VI });
function RI(r) {
let e = It(), t10 = `${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 lI(r, t10);
}
function DI(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 hI(r, e);
}
function AI(r) {
let e = new Uint16Array([0, 1, 2, 2, 1, 3]);
return gI(r, e);
}
function Yl(r, e, t10, o, n, s) {
yI(e, t10);
let a = xI(r), i = r.TEXTURE_2D;
return ce(r, () => r.bindTexture(i, a)), ce(r, () => r.texParameteri(i, r.TEXTURE_WRAP_S, r.CLAMP_TO_EDGE)), ce(r, () => r.texParameteri(i, r.TEXTURE_WRAP_T, r.CLAMP_TO_EDGE)), ce(r, () => r.texParameteri(i, r.TEXTURE_MIN_FILTER, r.NEAREST)), ce(r, () => r.texParameteri(i, r.TEXTURE_MAG_FILTER, r.NEAREST)), P().getNumber("WEBGL_VERSION") === 1 ? ce(r, () => r.texImage2D(i, 0, o, e, t10, 0, n, s, null)) : ce(r, () => r.texStorage2D(i, 1, o, e, t10)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null)), { texture: a, texShape: [t10, e] };
}
function th(r) {
return r.internalFormatFloat;
}
function FI(r, e, t10, o) {
let [n, s] = fp(e, t10);
return Yl(r, n, s, th(o), o.textureFormatFloat, r.FLOAT);
}
function rh(r) {
return r.internalFormatHalfFloat;
}
function PI(r, e, t10, o) {
let [n, s] = fp(e, t10);
return Yl(r, n, s, rh(o), o.textureFormatFloat, o.textureTypeHalfFloat);
}
function oh(r) {
return r.downloadTextureFormat;
}
function OI(r, e, t10, o) {
let [n, s] = fp(e, t10);
return Yl(r, n, s, oh(o), r.RGBA, r.UNSIGNED_BYTE);
}
function nh(r) {
return r.internalFormatPackedFloat;
}
function MI(r, e, t10, o) {
let [n, s] = Ea(e, t10);
return Yl(r, n, s, nh(o), r.RGBA, r.FLOAT);
}
function sh(r) {
return r.internalFormatPackedHalfFloat;
}
function LI(r, e, t10, o) {
let [n, s] = Ea(e, t10);
return Yl(r, n, s, sh(o), r.RGBA, o.textureTypeHalfFloat);
}
function BI(r, e, t10) {
return ce(r, () => r.bindBuffer(r.ARRAY_BUFFER, t10)), Hf(r, e, "clipSpacePos", t10, 3, 20, 0) && Hf(r, e, "uv", t10, 2, 20, 12);
}
function zI(r, e, t10, o, n, s) {
ce(r, () => r.bindTexture(r.TEXTURE_2D, e));
let a, i, p;
n instanceof Uint8Array ? (a = new Uint8Array(t10 * o * 4), i = r.UNSIGNED_BYTE, p = r.RGBA) : (a = new Float32Array(t10 * o * 4), i = r.FLOAT, p = s.internalFormatPackedFloat), a.set(n), P().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t10, o, r.RGBA, i, a)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, p, t10, o, 0, r.RGBA, i, a)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function VI(r, e, t10) {
ce(r, () => r.bindTexture(r.TEXTURE_2D, e)), t10.data instanceof Uint8Array ? P().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t10.width, t10.height, r.RGBA, r.UNSIGNED_BYTE, t10.data)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, t10.width, t10.height, 0, r.RGBA, r.UNSIGNED_BYTE, t10.data)) : P().getNumber("WEBGL_VERSION") === 2 ? ce(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, r.RGBA, r.UNSIGNED_BYTE, t10)) : ce(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, r.RGBA, r.UNSIGNED_BYTE, t10)), ce(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function WI(r, e, t10, o) {
let n = r.createBuffer();
ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, n));
let i = 4 * 4 * e * t10;
return ce(r, () => r.bufferData(r.PIXEL_PACK_BUFFER, i, r.STREAM_READ)), ce(r, () => r.readPixels(0, 0, t10, e, r.RGBA, r.FLOAT, 0)), ce(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, null)), n;
}
function UI(r, e, t10) {
let o = r, n = new Float32Array(t10);
return o.bindBuffer(o.PIXEL_PACK_BUFFER, e), o.getBufferSubData(o.PIXEL_PACK_BUFFER, 0, n), o.bindBuffer(o.PIXEL_PACK_BUFFER, null), n;
}
function GI(r, e, t10, o) {
let [n, s] = fp(e, t10), a = 4, i = new Uint8Array(XE(e * t10, a));
return ce(r, () => r.readPixels(0, 0, n, s, o.downloadTextureFormat, r.UNSIGNED_BYTE, i)), new Float32Array(i.buffer);
}
function HI(r, e, t10, o, n, s, a, i) {
let p = r, u = new Float32Array(YE(s, a));
return p.bindBuffer(p.PIXEL_PACK_BUFFER, e), p.getBufferSubData(p.PIXEL_PACK_BUFFER, 0, u), p.bindBuffer(p.PIXEL_PACK_BUFFER, null), u;
}
function KI(r, e, t10) {
let o = new Float32Array(e * t10 * 4);
return ce(r, () => r.readPixels(0, 0, t10, e, r.RGBA, r.FLOAT, o)), o;
}
var xp = class {
constructor(e) {
this.outputTexture = null, this.program = null, this.disposed = false, this.itemsToPoll = [];
let t10 = P().getNumber("WEBGL_VERSION");
if (e != null ? (this.gl = e, iI(t10, e)) : this.gl = Gr(t10), e = this.gl, P().getNumber("WEBGL_VERSION") === 2) {
let s = e;
this.createVertexArray = () => ce(s, () => s.createVertexArray()), this.bindVertexArray = (a) => ce(s, () => s.bindVertexArray(a)), this.deleteVertexArray = (a) => ce(s, () => s.deleteVertexArray(a)), this.getVertexArray = () => ce(s, () => s.getParameter(s.VERTEX_ARRAY_BINDING));
} else if (e != null) {
let s = e.getExtension("OES_vertex_array_object");
if (s == null)
throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");
this.createVertexArray = () => ce(e, () => s.createVertexArrayOES()), this.bindVertexArray = (a) => ce(e, () => s.bindVertexArrayOES(a)), this.deleteVertexArray = (a) => ce(e, () => s.deleteVertexArrayOES(a)), this.getVertexArray = () => ce(e, () => e.getParameter(s.VERTEX_ARRAY_BINDING_OES));
}
let o = "WEBGL_color_buffer_float", n = "EXT_color_buffer_half_float";
if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), P().getNumber("WEBGL_VERSION") === 1) {
let s = "OES_texture_float", a = "OES_texture_half_float";
if (this.textureFloatExtension = kc(this.gl, s), Hr(this.gl, a))
this.textureHalfFloatExtension = kc(this.gl, a);
else if (P().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(o), Hr(this.gl, n))
this.colorBufferHalfFloatExtension = kc(this.gl, n);
else if (P().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 (o = "EXT_color_buffer_float", Hr(this.gl, o))
this.colorBufferFloatExtension = this.gl.getExtension(o);
else if (Hr(this.gl, n))
this.colorBufferHalfFloatExtension = this.gl.getExtension(n);
else
throw new Error("GL context does not support color renderable floats");
this.vertexBuffer = DI(this.gl), this.indexBuffer = AI(this.gl), this.framebuffer = bI(this.gl), this.textureConfig = Kl(this.gl, this.textureHalfFloatExtension);
}
get debug() {
return P().getBool("DEBUG");
}
dispose() {
if (this.disposed)
return;
this.program != null && console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."), this.outputTexture != null && console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");
let e = this.gl;
ce(e, () => e.finish()), ce(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), ce(e, () => e.deleteFramebuffer(this.framebuffer)), ce(e, () => e.bindBuffer(e.ARRAY_BUFFER, null)), ce(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, null)), ce(e, () => e.deleteBuffer(this.indexBuffer)), this.disposed = true;
}
createFloat32MatrixTexture(e, t10) {
return this.throwIfDisposed(), FI(this.gl, e, t10, this.textureConfig);
}
createFloat16MatrixTexture(e, t10) {
return this.throwIfDisposed(), PI(this.gl, e, t10, this.textureConfig);
}
createUnsignedBytesMatrixTexture(e, t10) {
return this.throwIfDisposed(), OI(this.gl, e, t10, this.textureConfig);
}
uploadPixelDataToTexture(e, t10) {
this.throwIfDisposed(), VI(this.gl, e, t10);
}
uploadDenseMatrixToTexture(e, t10, o, n) {
this.throwIfDisposed(), zI(this.gl, e, t10, o, n, this.textureConfig);
}
createFloat16PackedMatrixTexture(e, t10) {
return this.throwIfDisposed(), LI(this.gl, e, t10, this.textureConfig);
}
createPackedMatrixTexture(e, t10) {
return this.throwIfDisposed(), MI(this.gl, e, t10, this.textureConfig);
}
deleteMatrixTexture(e) {
this.throwIfDisposed(), this.outputTexture === e && (Kf(this.gl, this.framebuffer), this.outputTexture = null), ce(this.gl, () => this.gl.deleteTexture(e));
}
downloadByteEncodedFloatMatrixFromOutputTexture(e, t10, o) {
return this.downloadMatrixDriver(e, () => GI(this.gl, t10, o, this.textureConfig));
}
downloadPackedMatrixFromBuffer(e, t10, o, n, s, a) {
return HI(this.gl, e, t10, o, n, s, a, this.textureConfig);
}
downloadFloat32MatrixFromBuffer(e, t10) {
return UI(this.gl, e, t10);
}
createBufferFromTexture(e, t10, o) {
this.bindTextureToFrameBuffer(e);
let n = WI(this.gl, t10, o, this.textureConfig);
return this.unbindTextureToFrameBuffer(), n;
}
createAndWaitForFence() {
let e = this.createFence(this.gl);
return this.pollFence(e);
}
createFence(e) {
let t10, o;
if (P().getBool("WEBGL_FENCE_API_ENABLED")) {
let n = e, s = n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE, 0);
e.flush(), o = () => {
let a = n.clientWaitSync(s, 0, 0);
return a === n.ALREADY_SIGNALED || a === n.CONDITION_SATISFIED;
}, t10 = s;
} else
P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? (t10 = this.beginQuery(), this.endQuery(), o = () => this.isQueryAvailable(t10, P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))) : o = () => true;
return { query: t10, isFencePassed: o };
}
downloadMatrixFromPackedTexture(e, t10, o) {
return this.downloadMatrixDriver(e, () => KI(this.gl, t10, o));
}
createProgram(e) {
this.throwIfDisposed();
let t10 = this.gl;
this.vertexShader == null && (this.vertexShader = RI(t10));
let o = dI(t10);
ce(t10, () => t10.attachShader(o, this.vertexShader)), ce(t10, () => t10.attachShader(o, e)), fI(t10, o);
let n = Object.assign(o, { vao: this.createVertexArray() });
return this.debug && ql(t10, n), n;
}
buildVao(e) {
this.setProgram(e), this.bindVertexArray(e.vao);
let t10 = this.gl;
ce(t10, () => t10.bindBuffer(t10.ELEMENT_ARRAY_BUFFER, this.indexBuffer)), BI(t10, e, this.vertexBuffer);
}
deleteProgram(e) {
this.throwIfDisposed(), e === this.program && (this.program = null), e != null && (ce(this.gl, () => this.gl.deleteProgram(e)), this.deleteVertexArray(e.vao));
}
setProgram(e) {
this.throwIfDisposed(), this.program = e, this.program != null && this.debug && ql(this.gl, this.program), ce(this.gl, () => this.gl.useProgram(e));
}
getUniformLocation(e, t10, o = true) {
return this.throwIfDisposed(), o ? CI(this.gl, e, t10) : wI(this.gl, e, t10);
}
getAttributeLocation(e, t10) {
return this.throwIfDisposed(), ce(this.gl, () => this.gl.getAttribLocation(e, t10));
}
getUniformLocationNoThrow(e, t10) {
return this.throwIfDisposed(), this.gl.getUniformLocation(e, t10);
}
setInputMatrixTexture(e, t10, o) {
this.throwIfDisposed(), this.throwIfNoProgram(), SI(this.gl, e, t10, o);
}
setOutputMatrixTexture(e, t10, o) {
this.setOutputMatrixTextureDriver(e, o, t10);
}
setOutputPackedMatrixTexture(e, t10, o) {
this.throwIfDisposed();
let [n, s] = Ea(t10, o);
this.setOutputMatrixTextureDriver(e, n, s);
}
setOutputMatrixWriteRegion(e, t10, o, n) {
this.setOutputMatrixWriteRegionDriver(o, e, n, t10);
}
setOutputPackedMatrixWriteRegion(e, t10, o, n) {
throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
}
debugValidate() {
this.program != null && ql(this.gl, this.program), Nc(this.gl);
}
executeProgram() {
this.throwIfDisposed(), this.throwIfNoProgram();
let e = this.gl;
if (this.debug) {
let t10 = this.getVertexArray();
console.assert(t10 === this.program.vao, "VAO changed between setProgram and executeProgram!"), this.debugValidate();
}
ce(e, () => e.drawElements(e.TRIANGLES, 6, e.UNSIGNED_SHORT, 0));
}
blockUntilAllProgramsCompleted() {
this.throwIfDisposed(), ce(this.gl, () => this.gl.finish());
}
getQueryTimerExtension() {
return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = kc(this.gl, P().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 (P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
let o = this.gl, n = this.getQueryTimerExtensionWebGL2(), s = o.createQuery();
return o.beginQuery(n.TIME_ELAPSED_EXT, s), s;
}
let e = this.getQueryTimerExtensionWebGL1(), t10 = e.createQueryEXT();
return e.beginQueryEXT(e.TIME_ELAPSED_EXT, t10), t10;
}
endQuery() {
if (P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
let t10 = this.gl, o = this.getQueryTimerExtensionWebGL2();
t10.endQuery(o.TIME_ELAPSED_EXT);
return;
}
let e = this.getQueryTimerExtensionWebGL1();
e.endQueryEXT(e.TIME_ELAPSED_EXT);
}
async waitForQueryAndGetTime(e) {
return await y.repeatedTry(() => this.disposed || this.isQueryAvailable(e, P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))), this.getQueryTime(e, P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
}
getQueryTime(e, t10) {
if (t10 === 0)
return null;
if (t10 === 2) {
let o = this.gl;
return o.getQueryParameter(e, o.QUERY_RESULT) / 1e6;
} else {
let o = this.getQueryTimerExtensionWebGL1();
return o.getQueryObjectEXT(e, o.QUERY_RESULT_EXT) / 1e6;
}
}
isQueryAvailable(e, t10) {
if (t10 === 0)
return true;
if (t10 === 2) {
let o = this.gl, n = this.getQueryTimerExtensionWebGL2(), s = o.getQueryParameter(e, o.QUERY_RESULT_AVAILABLE);
return this.disjoint == null && (this.disjoint = this.gl.getParameter(n.GPU_DISJOINT_EXT)), s && !this.disjoint;
} else {
let o = this.getQueryTimerExtensionWebGL1(), n = o.getQueryObjectEXT(e, o.QUERY_RESULT_AVAILABLE_EXT);
return this.disjoint == null && (this.disjoint = this.gl.getParameter(o.GPU_DISJOINT_EXT)), n && !this.disjoint;
}
}
pollFence(e) {
return new Promise((t10) => {
this.addItemToPoll(() => e.isFencePassed(), () => t10());
});
}
pollItems() {
let e = oZ(this.itemsToPoll.map((t10) => t10.isDoneFn));
for (let t10 = 0; t10 <= e; ++t10) {
let { resolveFn: o } = this.itemsToPoll[t10];
o();
}
this.itemsToPoll = this.itemsToPoll.slice(e + 1);
}
addItemToPoll(e, t10) {
if (this.itemsToPoll.push({ isDoneFn: e, resolveFn: t10 }), this.itemsToPoll.length > 1)
return;
let o;
"setTimeoutCustom" in P().platform && (o = P().platform.setTimeoutCustom.bind(P().platform)), y.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0), () => 0, null, o);
}
bindTextureToFrameBuffer(e) {
this.throwIfDisposed(), jl(this.gl, e, this.framebuffer), this.debug && Nc(this.gl);
}
unbindTextureToFrameBuffer() {
this.outputTexture != null ? (jl(this.gl, this.outputTexture, this.framebuffer), this.debug && Nc(this.gl)) : Kf(this.gl, this.framebuffer);
}
downloadMatrixDriver(e, t10) {
this.bindTextureToFrameBuffer(e);
let o = t10();
return this.unbindTextureToFrameBuffer(), o;
}
setOutputMatrixTextureDriver(e, t10, o) {
this.throwIfDisposed();
let n = this.gl;
jl(n, e, this.framebuffer), this.debug && Nc(n), this.outputTexture = e, ce(n, () => n.viewport(0, 0, t10, o)), ce(n, () => n.scissor(0, 0, t10, o));
}
setOutputMatrixWriteRegionDriver(e, t10, o, n) {
this.throwIfDisposed(), ce(this.gl, () => this.gl.scissor(e, t10, o, n));
}
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 oZ(r) {
let e = 0;
for (; e < r.length && r[e](); ++e)
;
return e - 1;
}
var { addImpl: cR, bincountImpl: ah, bincountReduceImpl: lR, castImpl: mR, ceilImpl: dR, concatImpl: fR, equalImpl: hR, expImpl: gR, expm1Impl: xR, floorImpl: yR, gatherNdImpl: bR, gatherV2Impl: CR, greaterImpl: wR, greaterEqualImpl: SR, lessImpl: IR, lessEqualImpl: vR, linSpaceImpl: kR, logImpl: NR, maxImpl: TR, maximumImpl: _R, minimumImpl: $R, multiplyImpl: ER, negImpl: RR, notEqualImpl: DR, prodImpl: AR, raggedGatherImpl: FR, raggedRangeImpl: PR, raggedTensorToTensorImpl: OR, rangeImpl: MR, rsqrtImpl: LR, scatterImpl: BR, sigmoidImpl: zR, simpleAbsImpl: ih, sliceImpl: VR, sparseFillEmptyRowsImpl: WR, sparseReshapeImpl: UR, sparseSegmentReductionImpl: uh, sqrtImpl: GR, staticRegexReplaceImpl: HR, stridedSliceImpl: KR, stringNGramsImpl: qR, stringSplitImpl: jR, stringToHashBucketFastImpl: XR, subImpl: YR, tileImpl: QR, topKImpl: ZR, transposeImpl: yp, uniqueImpl: JR } = Sc;
function jI(r, e) {
return ["x", "y", "z", "w", "u", "v"].slice(0, e).map((t10) => `${r}.${t10}`);
}
function Rt(r, e) {
return e === 1 ? [r] : jI(r, e);
}
function eD(r, e) {
if (r === 1)
return "rc";
let t10 = "";
for (let o = 0; o < r; o++)
t10 += e[o], o < r - 1 && (t10 += ",");
return t10;
}
var ph = class {
constructor(e) {
if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = ut(this.outputShape.length), this.rank === 0)
this.userCode = `
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;
else {
let t10 = Rt("rc", this.rank), o = Re(this.rank), n = this.getOutOfBoundsCondition(t10), s = this.getSetup(t10), a = this.getOutput(t10);
this.userCode = `
void main() {
${o} rc = getOutputCoords();
if(${n}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${a}));
}
}
`;
}
}
getSourceCoordsArr(e) {
let t10 = [];
for (let o = 0; o <= 1; o++)
for (let n = 0; n <= 1; n++) {
let s = `${o === 0 ? "r" : "rp1"}, ${n === 0 ? "c" : "cp1"}`;
for (let a = 2; a < this.rank; a++)
s = `${e[e.length - 1 - a]},` + s;
t10.push(s);
}
return t10;
}
getOutOfBoundsCondition(e) {
if (this.rank === 1)
return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`;
let t10 = "";
for (let o = this.rank - 2; o < this.rank; o++)
t10 += `${e[o]} >= ${this.enableShapeUniforms ? `outShape[${o}]` : this.outputShape[o]}`, o < this.rank - 1 && (t10 += "||");
return t10;
}
getSetup(e) {
if (this.rank === 1)
return "";
let t10 = e.slice(-2), o = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1], n = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2];
return `
int r = ${t10[0]};
int c = ${t10[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${o};
bool rEdge = rp1 >= ${n};
`;
}
getOutput(e) {
let t10 = this.getSourceCoordsArr(e);
return this.rank === 1 ? `getA(rc), (rc + 1 >= ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0` : `getA(${t10[0]}),
cEdge ? 0. : getA(${t10[1]}),
rEdge ? 0. : getA(${t10[2]}),
rEdge || cEdge ? 0. : getA(${t10[3]})`;
}
};
var Oc = class {
constructor(e, t10) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length);
let o = "";
for (let n = 0; n < 4; n++) {
let s = "thisRC = rc;";
n % 2 === 1 && (s += "thisRC.z += 1;"), n > 1 && (s += "thisRC.y += 1;"), o += `
${s}
${n > 0 ? "if(thisRC.y < rows && thisRC.z < cols){" : ""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${n}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${n > 0 ? "}" : ""}
`;
}
this.userCode = `
${nZ(t10, this.enableShapeUniforms)}
${this.enableShapeUniforms ? Ec() : $c(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]};
${o}
setOutput(result);
}
`;
}
};
function nZ(r, e) {
return `
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${e ? tR(["r", "c", "d"], "inputShape") : Os(["r", "c", "d"], r)}
return ivec3(r, c, d);
}
`;
}
var ch = class {
constructor(e) {
this.gpgpu = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0, this.freeTextures = {}, this.usedTextures = {}, this.logEnabled = false;
}
acquireTexture(e, t10, o) {
let n = rD(t10, o), s = oD(e, n, o);
s in this.freeTextures || (this.freeTextures[s] = []), s in this.usedTextures || (this.usedTextures[s] = []);
let a = tD(e, n, this.gpgpu.gl, this.gpgpu.textureConfig, o);
if (this.freeTextures[s].length > 0) {
this.numFreeTextures--, this.numUsedTextures++, this._numBytesFree -= a, this.log();
let p = this.freeTextures[s].pop();
return this.usedTextures[s].push(p), p;
}
let i;
return n === er.PACKED_2X2_FLOAT32 ? i = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : n === er.PACKED_2X2_FLOAT16 ? i = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : n === er.UNPACKED_FLOAT32 ? i = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : n === er.UNPACKED_FLOAT16 ? i = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : n === er.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, t10, o, n) {
if (this.freeTextures == null)
return;
let s = rD(o, n), a = oD(t10, s, n);
a in this.freeTextures || (this.freeTextures[a] = []);
let i = tD(t10, s, this.gpgpu.gl, this.gpgpu.textureConfig, n), p = P().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
p !== -1 && this._numBytesAllocated > p ? (this.gpgpu.deleteMatrixTexture(e.texture), this._numBytesAllocated -= i) : (this.freeTextures[a].push(e), this.numFreeTextures++, this._numBytesFree += i), this.numUsedTextures--;
let u = this.usedTextures[a], c = u && u.indexOf(e);
if (c == null || c < 0)
throw new Error("Cannot release a texture that was never provided by this texture manager");
u[c] = u[u.length - 1], u.pop(), this.log();
}
log() {
if (!this.logEnabled)
return;
let e = this.numFreeTextures + this.numUsedTextures;
console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${e})`);
let t10 = this._numBytesFree / this._numBytesAllocated;
console.log(`Bytes allocated: ${this._numBytesAllocated}`), console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * t10)}%)`);
}
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((t10) => {
this.gpgpu.deleteMatrixTexture(t10.texture);
});
for (let e in this.usedTextures)
this.usedTextures[e].forEach((t10) => {
this.gpgpu.deleteMatrixTexture(t10.texture);
});
this.freeTextures = null, this.usedTextures = null, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0;
}
}
};
function sZ(r, e) {
let t10 = r;
if (e === t10.R32F)
return 4;
if (e === t10.R16F)
return 2;
if (e === t10.RGBA32F)
return 16;
if (e === r.RGBA)
return 16;
if (e === t10.RGBA16F)
return 8;
if (e === t10.RGBA8)
return 4;
throw new Error(`Unknown internal format ${e}`);
}
function tD(r, e, t10, o, n) {
let s = aZ(e, o), a;
if (n) {
let [p, u] = Ea(r[0], r[1]);
a = p * u;
} else {
let [p, u] = fp(r[0], r[1]);
a = p * u;
}
let i = sZ(t10, s);
return a * i;
}
function aZ(r, e) {
switch (r) {
case er.PACKED_2X2_FLOAT32:
return nh(e);
case er.PACKED_2X2_FLOAT16:
return sh(e);
case er.UNPACKED_FLOAT32:
return th(e);
case er.UNPACKED_FLOAT16:
return rh(e);
case er.PACKED_4X1_UNSIGNED_BYTE:
return oh(e);
default:
throw new Error(`Unknown physical texture type ${r}`);
}
}
function iZ(r) {
return P().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? r ? er.PACKED_2X2_FLOAT32 : er.UNPACKED_FLOAT32 : r ? er.PACKED_2X2_FLOAT16 : er.UNPACKED_FLOAT16;
}
function rD(r, e) {
if (r === mr.UPLOAD)
return er.PACKED_2X2_FLOAT32;
if (r === mr.RENDER || r == null)
return iZ(e);
if (r === mr.DOWNLOAD || r === mr.PIXELS)
return er.PACKED_4X1_UNSIGNED_BYTE;
throw new Error(`Unknown logical texture type ${r}`);
}
function oD(r, e, t10) {
return `${r[0]}_${r[1]}_${e}_${t10}`;
}
var tr = class {
constructor(e, t10) {
this.variableNames = ["A"], this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = `
float unaryOperation(float x) {
${t10}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`;
}
};
var Ut = "if (isnan(x)) return x;";
var nD = "return x;";
var XI = "return abs(x);";
var sD = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var aD = Ut + `
return (x < 0.0) ? 0.0 : x;
`;
var iD = Ut + `
return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var Ra = "return x;";
var uD = "return 1.0 / (1.0 + exp(-1.0 * x));";
var cD = "return x;";
var lD = `
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;
`;
var mD = `
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;
`;
var dD = `
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;
`;
var fD = "return 1.0 / (1.0 + exp(-1.0 * x));";
var Ar = class {
constructor(e, t10) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length), this.userCode = `
vec4 unaryOperation(vec4 x) {
${t10}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`;
}
};
var lh = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length);
let t10 = e.length, o = Rt("rc", t10), n = Re(t10), s = eD(t10, o), a = o.slice(-2), i = t10 <= 1 ? "rc" : `vec2(${a.join(",")})`;
this.userCode = `
void main() {
${n} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${i}));
}
`;
}
};
var pZ = Wt.whereImpl;
var cZ = 1e-7;
var lZ = 1e-4;
var mh = {};
function mZ(r) {
return r in mh || (mh[r] = {}), mh[r];
}
var dZ = P().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
var fZ = 600;
function hZ() {
return P().global.screen == null ? 1024 : P().global.screen.height * P().global.screen.width * window.devicePixelRatio * fZ / 1024 / 1024;
}
var hu = class extends ro {
nextDataId() {
return hu.nextDataId++;
}
constructor(e) {
if (super(), this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !P().getBool("HAS_WEBGL"))
throw new Error("WebGL is not supported on this device");
let t10;
if (e != null) {
if (e instanceof xp)
t10 = e;
else {
let o = Gr(P().getNumber("WEBGL_VERSION"), e);
t10 = new xp(o);
}
this.binaryCache = {}, this.gpgpuCreatedLocally = false;
} else {
let o = Gr(P().getNumber("WEBGL_VERSION"));
t10 = new xp(o), this.binaryCache = mZ(P().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
}
this.gpgpu = t10, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new ch(this.gpgpu), this.numMBBeforeWarning = hZ(), this.texData = new Lo(this, ur());
}
numDataIds() {
return this.texData.numDataIds() - this.pendingDeletes;
}
writeTexture(e, t10, o, n, s, a) {
let i = this.makeTensorInfo(t10, o), p = this.texData.get(i.dataId);
p.isPacked = false, p.texture = { texture: e, texShape: [n, s] }, p.texShape = [n, s];
let u = Tc(t10), c = new Xl(u, false, a), l = this.runWebGLProgram(c, [i], o, [[n, s]]);
return l.shape = t10, p.texture = null, this.disposeIntermediateTensorInfo(i), l.dataId;
}
write(e, t10, o) {
if ((P().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || P().getBool("DEBUG")) && this.checkNumericalProblems(e), o === "complex64" && e != null)
throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
let n = { id: this.nextDataId() };
return this.texData.set(n, { shape: t10, dtype: o, values: e, usage: mr.UPLOAD, refCount: 1 }), n;
}
refCount(e) {
return this.texData.has(e) ? this.texData.get(e).refCount : 0;
}
incRef(e) {
let t10 = this.texData.get(e);
t10.refCount++;
}
decRef(e) {
if (this.texData.has(e)) {
let t10 = this.texData.get(e);
t10.refCount--;
}
}
move(e, t10, o, n, s) {
if (P().getBool("DEBUG") && this.checkNumericalProblems(t10), n === "complex64")
throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
this.texData.set(e, { shape: o, dtype: n, values: t10, usage: mr.UPLOAD, refCount: s });
}
disposeIntermediateTensorInfo(e) {
this.disposeData(e.dataId);
}
readSync(e) {
let t10 = this.texData.get(e), { values: o, dtype: n, complexTensorInfos: s, slice: a, shape: i, isPacked: p } = t10;
if (a != null) {
let m;
p ? m = new Ar(i, Ra) : m = new tr(i, Ra);
let d = this.runWebGLProgram(m, [{ dataId: e, shape: i, dtype: n }], n), f = this.readSync(d.dataId);
return this.disposeIntermediateTensorInfo(d), f;
}
if (o != null)
return this.convertAndCacheOnCPU(e);
if (n === "string")
return o;
let u = this.activeTimers != null, c;
u && (c = y.now());
let l;
if (n === "complex64") {
let m = this.readSync(s.real.dataId), d = this.readSync(s.imag.dataId);
l = C.mergeRealAndImagArrays(m, d);
} else
l = this.getValuesFromTexture(e);
return u && (this.downloadWaitMs += y.now() - c), this.convertAndCacheOnCPU(e, l);
}
async read(e) {
if (this.pendingRead.has(e)) {
let f = this.pendingRead.get(e);
return new Promise((h) => f.push(h));
}
let t10 = this.texData.get(e), { values: o, shape: n, slice: s, dtype: a, complexTensorInfos: i, isPacked: p } = t10;
if (s != null) {
let f;
p ? f = new Ar(n, Ra) : f = new tr(n, Ra);
let h = this.runWebGLProgram(f, [{ dataId: e, shape: n, dtype: a }], a), g = this.read(h.dataId);
return this.disposeIntermediateTensorInfo(h), g;
}
if (o != null)
return this.convertAndCacheOnCPU(e);
if (P().getBool("DEBUG") && !P().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && P().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" && P().get("WEBGL_BUFFER_SUPPORTED")) {
c = this.decode(e);
let f = this.texData.get(c.dataId);
u = this.gpgpu.createBufferFromTexture(f.texture.texture, ...Hl(n));
}
this.pendingRead.set(e, []), a !== "complex64" && await this.gpgpu.createAndWaitForFence();
let l;
if (a === "complex64") {
let f = await Promise.all([this.read(i.real.dataId), this.read(i.imag.dataId)]), h = f[0], g = f[1];
l = C.mergeRealAndImagArrays(h, g);
} else if (u == null)
l = this.getValuesFromTexture(e);
else {
let f = y.sizeFromShape(n);
l = this.gpgpu.downloadFloat32MatrixFromBuffer(u, f);
}
if (c != null && this.disposeIntermediateTensorInfo(c), u != null) {
let f = this.gpgpu.gl;
ce(f, () => f.deleteBuffer(u));
}
let m = this.convertAndCacheOnCPU(e, l), d = this.pendingRead.get(e);
return this.pendingRead.delete(e), d.forEach((f) => f(m)), this.pendingDisposal.has(e) && (this.pendingDisposal.delete(e), this.disposeData(e) && ur().removeDataId(e, this), this.pendingDeletes--), m;
}
readToGPU(e, t10 = {}) {
let o = this.texData.get(e), { values: n, shape: s, slice: a, dtype: i, isPacked: p, texture: u } = o;
if (i === "complex64")
throw new Error("Does not support reading texture for complex64 dtype.");
if (a != null) {
let d;
p ? d = new Ar(s, Ra) : d = new tr(s, Ra);
let f = this.runWebGLProgram(d, [{ dataId: e, shape: s, dtype: i }], i), h = this.readToGPU(f, t10);
return this.disposeIntermediateTensorInfo(f), h;
}
if (u == null)
throw n != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU.");
let c = this.decode(e, t10.customTexShape), l = ur().makeTensorFromTensorInfo(c), m = this.texData.get(c.dataId);
return Object.assign({ tensorRef: l }, m.texture);
}
bufferSync(e) {
let t10 = this.readSync(e.dataId);
if (e.dtype === "string")
try {
let o = t10.map((n) => y.decodeString(n));
return me(e.shape, e.dtype, o);
} catch (o) {
throw new Error("Failed to decode encoded string bytes into utf-8");
}
return me(e.shape, e.dtype, t10);
}
checkNumericalProblems(e) {
if (e != null)
for (let t10 = 0; t10 < e.length; t10++) {
let o = e[t10];
if (!cI(o))
throw P().getBool("WEBGL_RENDER_FLOAT32_CAPABLE") ? Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`) : Error(`The value ${o} cannot be represented on this device.`);
}
}
getValuesFromTexture(e) {
let { shape: t10, dtype: o, isPacked: n } = this.texData.get(e), s = y.sizeFromShape(t10);
if (P().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
let m = this.decode(e), d = this.texData.get(m.dataId), f = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Hl(t10)).subarray(0, s);
return this.disposeIntermediateTensorInfo(m), f;
}
let a = P().getBool("WEBGL_PACK") && n === true, i = a ? Tc(t10) : t10, p = a ? new Jf(i) : new Zf(i), u = this.runWebGLProgram(p, [{ shape: i, dtype: o, dataId: e }], "float32"), c = this.texData.get(u.dataId), l = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture, c.texShape[0], c.texShape[1]).subarray(0, s);
return this.disposeIntermediateTensorInfo(u), l;
}
timerAvailable() {
return P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
}
time(e) {
let t10 = this.activeTimers, o = [], n = false;
this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, e();
let s = y.flatten(this.activeTimers.map((p) => p.query)).filter((p) => p != null), a = y.flatten(this.activeTimers.map((p) => p.name)).filter((p) => p != null);
this.activeTimers = t10, n && (this.programTimersStack = null);
let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null };
return (async () => {
if (P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
let p = await Promise.all(s);
i.kernelMs = y.sum(p), i.getExtraProfileInfo = () => p.map((u, c) => ({ name: a[c], ms: u })).map((u) => `${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: false, numBytesInGPU: this.numBytesInGPU, numBytesInGPUAllocated: this.textureManager.numBytesAllocated, numBytesInGPUFree: this.textureManager.numBytesFree };
}
startTimer() {
return P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? this.gpgpu.beginQuery() : { startMs: y.now(), endMs: null };
}
endTimer(e) {
return P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? (this.gpgpu.endQuery(), e) : (e.endMs = y.now(), e);
}
async getQueryTime(e) {
if (P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0)
return this.gpgpu.waitForQueryAndGetTime(e);
let t10 = e;
return t10.endMs - t10.startMs;
}
disposeData(e, t10 = false) {
if (this.pendingDisposal.has(e))
return false;
if (!this.texData.has(e))
return true;
if (t10 ? this.texData.get(e).refCount = 0 : this.texData.get(e).refCount--, !t10 && this.texData.get(e).refCount > 0)
return false;
if (this.pendingRead.has(e))
return this.pendingDisposal.add(e), this.pendingDeletes++, false;
this.releaseGPUData(e);
let { complexTensorInfos: o } = this.texData.get(e);
return o != null && (this.disposeData(o.real.dataId, t10), this.disposeData(o.imag.dataId, t10)), this.texData.delete(e), true;
}
releaseGPUData(e) {
let { texture: t10, dtype: o, texShape: n, usage: s, isPacked: a, slice: i } = this.texData.get(e), p = i && i.origDataId || e, u = this.dataRefCount.get(p);
u > 1 ? this.dataRefCount.set(p, u - 1) : (this.dataRefCount.delete(p), t10 != null && (this.numBytesInGPU -= this.computeBytes(n, o), this.textureManager.releaseTexture(t10, n, s, a)));
let c = this.texData.get(e);
c.texture = null, c.texShape = null, c.isPacked = false, c.slice = null;
}
getTexture(e) {
return this.uploadToGPU(e), this.texData.get(e).texture.texture;
}
getDataInfo(e) {
return this.texData.get(e);
}
shouldExecuteOnCPU(e, t10 = dZ) {
return P().getBool("WEBGL_CPU_FORWARD") && e.every((o) => this.texData.get(o.dataId).texture == null && y.sizeFromShape(o.shape) < t10);
}
getGPGPUContext() {
return this.gpgpu;
}
where(e) {
C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
let t10 = e.dataSync();
return pZ(e.shape, t10);
}
packedUnaryOp(e, t10, o) {
let n = new Ar(e.shape, t10), s = this.compileAndRun(n, [e], o);
return ur().makeTensorFromTensorInfo(s);
}
abs(e) {
if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") {
let n = ih(this.texData.get(e.dataId).values);
return this.makeOutput(e.shape, e.dtype, n);
}
if (P().getBool("WEBGL_PACK_UNARY_OPERATIONS"))
return this.packedUnaryOp(e, XI, e.dtype);
let t10 = new tr(e.shape, XI), o = this.compileAndRun(t10, [e]);
return ur().makeTensorFromTensorInfo(o);
}
makeTensorInfo(e, t10, o) {
let n;
if (t10 === "string" && o != null && o.length > 0 && y.isString(o[0])) {
let s = o.map((a) => y.encodeString(a));
n = this.write(s, e, t10);
} else
n = this.write(o, e, t10);
return this.texData.get(n).usage = null, { dataId: n, shape: e, dtype: t10 };
}
makeOutput(e, t10, o) {
return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e, t10, o), this);
}
unpackTensor(e) {
let t10 = new lh(e.shape);
return this.runWebGLProgram(t10, [e], e.dtype);
}
packTensor(e) {
let t10 = new ph(e.shape), o = true;
return this.runWebGLProgram(t10, [e], e.dtype, null, o);
}
packedReshape(e, t10) {
let o = [di(e.shape), ...fi(e.shape)], n = { dtype: e.dtype, shape: o, dataId: e.dataId }, s = [di(t10), ...fi(t10)], a = new Oc(s, o), i = true, p = [o], u = this.runWebGLProgram(a, [n], e.dtype, p, i);
return { dataId: u.dataId, shape: t10, dtype: u.dtype };
}
decode(e, t10) {
let o = this.texData.get(e), { isPacked: n, shape: s, dtype: a } = o;
if (t10 != null) {
let m = y.sizeFromShape(s), d = t10[0] * t10[1] * 4;
y.assert(m <= d, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
}
let i = Tc(s), p;
n ? p = new Qf(i) : p = new Yf(i);
let u = true, c = [t10 != null ? t10 : Hl(i)], l = this.runWebGLProgram(p, [{ shape: i, dtype: a, dataId: e }], a, c, u, t10);
return { dtype: a, shape: s, dataId: l.dataId };
}
runWebGLProgram(e, t10, o, n, s = false, a) {
let i = this.makeTensorInfo(e.outputShape, o), p = this.texData.get(i.dataId);
if (e.packedOutput && (p.isPacked = true), e.outPackingScheme === du.DENSE) {
let x = a != null ? a : Hl(e.outputShape);
p.texShape = x.map((b) => b * 2);
}
if (e.outTexUsage != null && (p.usage = e.outTexUsage), y.sizeFromShape(i.shape) === 0)
return p.values = y.getTypedArrayFromDType(i.dtype, 0), i;
let u = [], c = t10.map((x) => {
if (x.dtype === "complex64")
throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");
let b = this.texData.get(x.dataId);
if (b.texture == null) {
if (!e.packedInputs && y.sizeFromShape(x.shape) <= P().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))
return { shape: x.shape, texData: null, isUniform: true, uniformValues: b.values };
e.packedInputs && (b.isPacked = true, b.shape = x.shape);
}
if (this.uploadToGPU(x.dataId), !!b.isPacked != !!e.packedInputs)
x = b.isPacked ? this.unpackTensor(x) : this.packTensor(x), u.push(x), b = this.texData.get(x.dataId);
else if (b.isPacked && !fu(b.shape, x.shape)) {
let w = x, S = x.shape;
x.shape = b.shape, x = this.packedReshape(x, S), u.push(x), b = this.texData.get(x.dataId), w.shape = S;
}
return { shape: x.shape, texData: b, isUniform: false };
});
this.uploadToGPU(i.dataId);
let l = { shape: i.shape, texData: p, isUniform: false }, m = pR(e, c, l), d = this.getAndSaveBinary(m, () => iR(this.gpgpu, e, c, l)), f = this.activeTimers != null, h;
f && (h = this.startTimer()), P().get("ENGINE_COMPILE_ONLY") || uR(this.gpgpu, d, c, l, n), u.forEach((x) => this.disposeIntermediateTensorInfo(x)), f && (h = this.endTimer(h), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(h) }));
let g = P().get("WEBGL_FLUSH_THRESHOLD");
if (g > 0) {
let x = y.now();
x - this.lastGlFlushTime > g && (this.gpgpu.gl.flush(), this.lastGlFlushTime = x);
}
if (!P().getBool("WEBGL_LAZILY_UNPACK") && p.isPacked && s === false) {
let x = this.unpackTensor(i);
return this.disposeIntermediateTensorInfo(i), x;
}
return i;
}
compileAndRun(e, t10, o, n, s = false) {
return o = o || t10[0].dtype, this.runWebGLProgram(e, t10, o, n, s);
}
getAndSaveBinary(e, t10) {
return e in this.binaryCache || (this.binaryCache[e] = t10()), this.binaryCache[e];
}
getTextureManager() {
return this.textureManager;
}
dispose() {
this.disposed || (P().getBool("IS_TEST") || Object.keys(this.binaryCache).forEach((t10) => {
this.gpgpu.deleteProgram(this.binaryCache[t10].webGLProgram), delete this.binaryCache[t10];
}), 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 = true);
}
floatPrecision() {
return this.floatPrecisionValue == null && (this.floatPrecisionValue = De(() => {
if (!P().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
let e = P().getBool("DEBUG");
P().set("DEBUG", false);
let t10 = this.abs(ke(1e-8)).dataSync()[0];
if (P().set("DEBUG", e), t10 > 0)
return 32;
}
return 16;
})), this.floatPrecisionValue;
}
epsilon() {
return this.floatPrecision() === 32 ? cZ : lZ;
}
uploadToGPU(e) {
let t10 = this.texData.get(e), { shape: o, dtype: n, values: s, texture: a, usage: i, isPacked: p } = t10;
if (a != null)
return;
let u = this.activeTimers != null, c;
u && (c = y.now());
let l = t10.texShape;
if (l == null && (l = II(o, p), t10.texShape = l), s != null) {
let m = Tc(o), d, f = l[1], h = l[0], g = s instanceof Uint8Array || s instanceof Uint8ClampedArray;
(p || !g) && ([f, h] = Ea(l[0], l[1])), p ? d = new eh(m, g) : d = new Xl(m, g);
let x = g ? [h, f] : l, b = this.makeTensorInfo(x, n), w = this.texData.get(b.dataId);
g ? w.usage = mr.PIXELS : w.usage = mr.UPLOAD, w.texShape = x, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), f, h, s);
let S = [[h, f]], k = true, _ = this.runWebGLProgram(d, [b], n, S, k), E = this.texData.get(_.dataId);
t10.texShape = E.texShape, t10.isPacked = E.isPacked, t10.usage = E.usage, P().get("ENGINE_COMPILE_ONLY") ? this.disposeData(_.dataId) : (t10.texture = E.texture, t10.values = null, this.texData.delete(_.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += y.now() - c);
} else {
let m = this.acquireTexture(l, i, n, p);
t10.texture = m;
}
}
convertAndCacheOnCPU(e, t10) {
let o = this.texData.get(e), { dtype: n } = o;
return t10 != null && (o.values = gZ(t10, n)), o.values;
}
acquireTexture(e, t10, o, n) {
if (this.numBytesInGPU += this.computeBytes(e, o), !this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
let s = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
this.warnedAboutMemory = true, console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`);
}
return this.textureManager.acquireTexture(e, t10, n);
}
computeBytes(e, t10) {
return e[0] * e[1] * y.bytesPerElement(t10);
}
checkCompileCompletion() {
for (let [, e] of Object.entries(this.binaryCache))
this.checkCompletion_(e);
}
async checkCompileCompletionAsync() {
let e = [];
if (this.gpgpu.parallelCompilationExtension) {
for (let [, t10] of Object.entries(this.binaryCache))
e.push(this.checkCompletionAsync_(t10));
return Promise.all(e);
} else {
for (let [, t10] of Object.entries(this.binaryCache)) {
let o = new Promise((n) => {
try {
this.checkCompletion_(t10), n(true);
} catch (s) {
throw s;
}
});
e.push(o);
}
return Promise.all(e);
}
}
async checkCompletionAsync_(e) {
return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await Kw(), this.checkCompletionAsync_(e));
}
checkCompletion_(e) {
if (this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false)
throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)), this.gpgpu.gl.getShaderParameter(e.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (Gf(e.source, this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders.");
return true;
}
getUniformLocations() {
for (let e of Object.values(this.binaryCache)) {
this.gpgpu.buildVao(e.webGLProgram);
let { variablesLocations: t10, customUniformLocations: o, infLoc: n, nanLoc: s, outShapeLocation: a, outShapeStridesLocation: i, outTexShapeLocation: p } = EI(this.gpgpu, e.program, e.webGLProgram);
e.variablesLocations = t10, e.customUniformLocations = o, e.infLoc = n, e.nanLoc = s, e.outShapeLocation = a, e.outShapeStridesLocation = i, e.outTexShapeLocation = p;
}
}
createTensorFromGPUData(e, t10, o) {
e.channels = e.channels || "RGBA";
let { texture: n, height: s, width: a, channels: i } = e, p = ur().backend;
if (!p.gpgpu.gl.isTexture(n))
throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");
let u = p.writeTexture(n, t10, o, s, a, i);
return ur().makeTensorFromDataId(u, t10, o, p);
}
};
hu.nextDataId = 0;
function gZ(r, e) {
if (e === "float32" || e === "complex64")
return r;
if (e === "int32" || e === "bool") {
let t10 = e === "int32" ? new Int32Array(r.length) : new Uint8Array(r.length);
for (let o = 0; o < t10.length; ++o)
t10[o] = Math.round(r[o]);
return t10;
} else
throw new Error(`Unknown dtype ${e}`);
}
var xZ = "4.5.0";
function hD() {
P().set("WEBGL_FORCE_F16_TEXTURES", true);
}
Zi.isBrowser() && eu("webgl", () => new hu(), 2);
var cst = { forceHalfFloat: hD };
var Mc = `
if (isnan(a)) return a;
if (isnan(b)) return b;
`;
var lo = class {
constructor(e, t10, o) {
this.variableNames = ["A", "B"], this.outputShape = C.assertAndGetBroadcastShape(t10, o), this.enableShapeUniforms = ut(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 Kr = `
result.r = isNaN.r ? NAN : result.r;
result.g = isNaN.g ? NAN : result.g;
result.b = isNaN.b ? NAN : result.b;
result.a = isNaN.a ? NAN : result.a;
`;
var Ro = class {
constructor(e, t10, o, n = false) {
this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = C.assertAndGetBroadcastShape(t10, o);
let s = this.outputShape.length;
this.enableShapeUniforms = ut(s);
let a = "";
if (n)
if (s === 0 || y.sizeFromShape(this.outputShape) === 1)
a = `
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;
else if (a = `
${Re(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) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;
else {
let p = Rt("coords", s);
this.enableShapeUniforms ? a += `
bool nextRowOutOfBounds =
(${p[s - 2]} + 1) >= outShape[${s} - 2];
bool nextColOutOfBounds =
(${p[s - 1]} + 1) >= outShape[${s} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
` : a += `
bool nextRowOutOfBounds =
(${p[s - 2]} + 1) >= ${this.outputShape[s - 2]};
bool nextColOutOfBounds =
(${p[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 Dt(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
return t10.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
var gD = { kernelName: xo, backendName: "webgl", kernelFunc: Dt };
function Fr(r) {
let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.makeTensorInfo(o.shape, "complex64"), a = t10.texData.get(s.dataId), i = Dt({ inputs: { x: o }, backend: t10 }), p = Dt({ inputs: { x: n }, backend: t10 });
return a.complexTensorInfos = { real: i, imag: p }, s;
}
var xD = { kernelName: Ti, backendName: "webgl", kernelFunc: Fr };
var YI = "return (a < 0.) ? b * a : a;";
var QI = `
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function yZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = t10.makeTensorInfo([], "float32", y.createScalarValue(s, "float32")), i = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ro(QI, n.shape, a.shape) : new lo(YI, n.shape, a.shape), p = t10.runWebGLProgram(i, [n, a], "float32");
return t10.disposeIntermediateTensorInfo(a), p;
}
var yD = { kernelName: _n, backendName: "webgl", kernelFunc: yZ };
var ZI = "return (a < 0.) ? b * a : a;";
var JI = `
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function bZ(r) {
let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ro(JI, o.shape, n.shape) : new lo(ZI, o.shape, n.shape);
return t10.runWebGLProgram(s, [o, n], "float32");
}
var bD = { kernelName: Jn, backendName: "webgl", kernelFunc: bZ };
var Do = "if (isnan(x)) return x;";
function ge({ opSnippet: r, packedOpSnippet: e, cpuKernelImpl: t10, dtype: o }) {
return ({ inputs: n, backend: s }) => {
let { x: a } = n, i = s, p = o || a.dtype;
if (i.shouldExecuteOnCPU([a]) && t10 != null) {
let l = i.texData.get(a.dataId), m = t10(l.values, p);
return i.makeTensorInfo(a.shape, p, m);
}
let u = P().getBool("WEBGL_PACK_UNARY_OPERATIONS") && e != null, c;
return u ? c = new Ar(a.shape, e) : c = new tr(a.shape, r), i.runWebGLProgram(c, [a], p);
};
}
function nt({ opSnippet: r, packedOpSnippet: e, checkOutOfBounds: t10 = false, supportsComplex: o = false, cpuKernelImpl: n, dtype: s }) {
return ({ inputs: a, backend: i }) => {
let { a: p, b: u } = a, c = i;
if (o && p.dtype === "complex64") {
let f = c.texData.get(p.dataId), h = c.texData.get(u.dataId), [g, x] = [[f.complexTensorInfos.real, h.complexTensorInfos.real], [f.complexTensorInfos.imag, h.complexTensorInfos.imag]].map((w) => {
let [S, k] = w, _ = { dataId: S.dataId, dtype: S.dtype, shape: p.shape }, E = { dataId: k.dataId, dtype: k.dtype, shape: u.shape }, R = new lo(r, p.shape, u.shape);
return c.runWebGLProgram(R, [_, E], dt(S.dtype, k.dtype));
}), b = Fr({ inputs: { real: g, imag: x }, backend: c });
return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(x), b;
}
let l = s || dt(p.dtype, u.dtype);
if ((p.dtype === "string" || u.dtype === "string" || c.shouldExecuteOnCPU([p, u])) && n != null) {
let f = c.texData.get(p.dataId).values, h = c.texData.get(u.dataId).values, g = p.dtype === "string" ? C.fromUint8ToStringArray(f) : f, x = p.dtype === "string" ? C.fromUint8ToStringArray(h) : h, [b, w] = n(p.shape, u.shape, g, x, l), S = c.makeTensorInfo(w, l), k = c.texData.get(S.dataId);
return k.values = b, S;
}
let m = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") && e != null, d;
return m ? d = new Ro(e, p.shape, u.shape, t10) : d = new lo(r, p.shape, u.shape), c.runWebGLProgram(d, [p, u], l);
};
}
function hi(r, e = false) {
if (r === "linear")
return e ? cD : nD;
if (r === "relu")
return e ? mD : aD;
if (r === "elu")
return e ? lD : sD;
if (r === "relu6")
return e ? dD : iD;
if (r === "prelu")
return e ? JI : ZI;
if (r === "leakyrelu")
return e ? QI : YI;
if (r === "sigmoid")
return e ? fD : uD;
throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`);
}
var Lc = class {
constructor(e, t10, o, n = false, s = false, a = false, i = null, p = false, u = false) {
this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = o, this.enableShapeUniforms = ut(this.outputShape.length);
let c = n ? e[1] : e[2], l = Math.ceil(c / 2), m = n ? "i * 2, rc.y" : "rc.y, i * 2", d = s ? "rc.z, i * 2" : "i * 2, rc.z", f = n ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"], h = s ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"], g = "", x = "";
i && (p ? 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 b = a ? "result += getBiasAtOutCoords();" : "";
a && this.variableNames.push("bias"), p && this.variableNames.push("preluActivationWeights"), u && this.variableNames.push("leakyreluAlpha");
let w = "rc.x", S = "rc.x";
e[0] < t10[0] ? w = `imod(rc.x, ${e[0]})` : t10[0] < e[0] && (S = `imod(rc.x, ${t10[0]})`), this.userCode = `
${g}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${l}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
int batchA = ${w};
int batchB = ${S};
for (int i = 0; i < ${l}; i++) {
vec4 a = getMatrixA(batchA, ${m});
vec4 b = getMatrixB(batchB, ${d});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${f[0]} * ${h[0]});
result += (${f[1]} * ${h[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${b}
${x}
setOutput(result);
}
`;
}
};
var ev = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
var Ql = class {
constructor(e, t10, o) {
this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = C.assertAndGetBroadcastShape(t10, o), 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 CD = "return a * b;";
function Zl(r) {
let { inputs: e, backend: t10 } = r, { a: o, b: n } = e, s = C.upcastType(o.dtype, n.dtype);
if (o.dtype === "complex64") {
let i = t10.texData.get(o.dataId), p = t10.texData.get(n.dataId), u = new Ql(ev.REAL, o.shape, n.shape), c = new Ql(ev.IMAG, o.shape, n.shape), l = [{ dataId: i.complexTensorInfos.real.dataId, dtype: i.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: i.complexTensorInfos.imag.dataId, dtype: i.complexTensorInfos.imag.dtype, shape: o.shape }, { dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: n.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: n.shape }], m = t10.runWebGLProgram(u, l, "float32"), d = t10.runWebGLProgram(c, l, "float32"), f = Fr({ inputs: { real: m, imag: d }, backend: t10 });
return t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), f;
}
if (t10.shouldExecuteOnCPU([o, n])) {
let i = t10.texData.get(o.dataId), p = t10.texData.get(n.dataId), [u, c] = ER(o.shape, n.shape, i.values, p.values, s), l = t10.makeTensorInfo(c, s), m = t10.texData.get(l.dataId);
return m.values = u, l;
}
let a;
return P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? a = new Ro(CD, o.shape, n.shape) : a = new lo(CD, o.shape, n.shape), t10.runWebGLProgram(a, [o, n], s);
}
var wD = { kernelName: Kn, backendName: "webgl", kernelFunc: Zl };
function SD(r, e, t10) {
let o = [di(r.shape), ...fi(r.shape)], n = { dtype: r.dtype, shape: o, dataId: r.dataId }, s = [di(e), ...fi(e)], a = new Oc(s, o), i = true, p = [o], u = t10.runWebGLProgram(a, [n], r.dtype, p, i);
return { dataId: u.dataId, shape: e, dtype: u.dtype };
}
function te(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { shape: s } = o, a = t10, i = y.sizeFromShape(n.shape), p = y.inferFromImplicitShape(s, i), u = y.sizeFromShape(p);
y.assert(i === u, () => `The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);
let c = a.texData.get(n.dataId);
return c.isPacked && !fu(n.shape, p) && !(c.texture !== null && fu(c.shape, p)) ? SD(n, p, a) : (a.incRef(n.dataId), { dataId: n.dataId, shape: p, dtype: n.dtype });
}
var ID = { kernelName: ia, backendName: "webgl", kernelFunc: te };
var Jl = class {
constructor(e, t10) {
this.variableNames = ["x"];
let { windowSize: o, batchSize: n, inSize: s, outSize: a } = e;
this.outputShape = [n, a];
let i = Math.floor(o / 4) * 4, p = o % 4, u = "sumValue += dot(values, ones);";
if (t10 != null) {
let l = 1 / t10;
u = `sumValue += dot(values * ${y.isInt(l) ? l.toPrecision(2) : l}, ones);`;
}
let c = "";
s % o > 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 * ${o};
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 + ${i};
if (${p === 1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${u}
} else if (${p === 2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${u}
} else if (${p === 3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${u}
}
setOutput(sumValue);
}
`;
}
};
var dh = class {
constructor(e, t10) {
this.variableNames = ["x"];
let { windowSize: o, batchSize: n, inSize: s, outSize: a } = e;
this.outputShape = [n, a];
let i = "0.0", p = "";
t10 === "prod" ? i = "1.0" : t10 === "min" ? (i = "1.0 / 1e-20", p = "min") : t10 === "max" && (i = "-1.0 / 1e-20", p = "max");
let u = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
t10 === "sum" ? u = "sumValue" : t10 === "prod" ? u = "prodValue" : t10 === "all" ? u = "allValue" : t10 === "any" && (u = "anyValue");
let c = Math.floor(o / 4) * 4, l = o % 4, m = `
if (${t10 === "sum"}) {
sumValue += dot(values, ones);
} else if (${t10 === "prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${p}(values, minMaxValue);
if (${t10 === "min"} || ${t10 === "max"}) {
minMaxValue = ${p}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`, d = "vec4";
t10 === "all" ? (i = "1.0", m = `
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`, d = "bvec4") : t10 === "any" && (i = "0.0", m = `
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`, d = "bvec4");
let f = "";
s % o > 0 && (f = `
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) {
${f}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${o};
vec4 minMaxValue = vec4(${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;
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${m}
}
int inIdx = inOffset + ${c};
if (${l === 1}) {
${d} values = ${d}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${m}
} else if (${l === 2}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${m}
} else if (${l === 3}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${m}
}
setOutput(${u});
}
`;
}
};
function wZ(r) {
let e = [];
for (; e.length === 0 || e[e.length - 1].outSize !== 1; ) {
let t10 = e.length ? e[e.length - 1].outSize : r[1], o = C.computeOptimalWindowSize(t10);
e.push({ inSize: t10, windowSize: o, outSize: Math.ceil(t10 / o) });
}
return e;
}
function qr(r, e, t10, o) {
let n = wZ(r.shape), s = r;
for (let a = 0; a < n.length; a++) {
let { inSize: i, windowSize: p, outSize: u } = n[a], c, l;
t10 === "mean" ? c = a === 0 ? new Jl({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, i) : new Jl({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }) : c = new dh({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, t10), l = s, s = o.runWebGLProgram(c, [s], e), l.dataId !== r.dataId && o.disposeIntermediateTensorInfo(l);
}
return s;
}
var fh = class {
constructor(e, t10) {
this.variableNames = ["A"];
let o = new Array(e.length);
for (let a = 0; a < o.length; a++)
o[a] = e[t10[a]];
this.outputShape = o, this.rank = o.length;
let n = Re(this.rank), s = SZ(t10);
this.userCode = `
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`;
}
};
function SZ(r) {
let e = r.length;
if (e > 6)
throw Error(`Transpose for rank ${e} is not yet supported`);
let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"], o = new Array(e);
for (let n = 0; n < r.length; n++)
o[r[n]] = t10[n];
return o.join();
}
var hh = class {
constructor(e, t10) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true;
let o = new Array(e.length);
for (let c = 0; c < o.length; c++)
o[c] = e[t10[c]];
if (this.outputShape = o, this.rank = o.length, this.rank > 6)
throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
let n = Re(this.rank), s = jI("rc", this.rank), a = new Array(this.rank);
for (let c = 0; c < t10.length; c++)
a[t10[c]] = s[c];
let i = `vec2(${a.slice(-2).join()})`, p = `++${s[this.rank - 1]} < ${o[this.rank - 1]}`, u = `getChannel(getA(${a.join()}), ${i})`;
this.userCode = `
void main() {
${n} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${p}) {
result[1] = ${u};
}
--${s[this.rank - 1]};
if(++${s[this.rank - 2]} < ${o[this.rank - 2]}) {
result[2] = ${u};
if(${p}) {
result[3] = ${u};
}
}
setOutput(result);
}
`;
}
};
function gu(r, e, t10) {
let o = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new hh(r.shape, e) : new fh(r.shape, e);
return t10.runWebGLProgram(o, [r], r.dtype);
}
function vD(r, e, t10, o) {
let n = e, s = r.shape.length, a = y.parseAxisParam(n, r.shape), i = a, p = C.getAxesPermutation(i, s), u = p != null, c = r;
u && (c = gu(r, p, o), i = C.getInnerMostAxes(i.length, s)), C.assertAxesAreInnerMostDims("sum", i, s);
let [l, m] = C.computeOutAndReduceShapes(c.shape, i), d = l;
t10 && (d = C.expandShapeToKeepDim(l, a));
let f = y.sizeFromShape(m), g = y.sizeFromShape(r.shape) / f, x = te({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: o }), b = Za(r.dtype), w = qr(x, b, "sum", o), S = te({ inputs: { x: w }, attrs: { shape: d }, backend: o });
return o.disposeIntermediateTensorInfo(x), o.disposeIntermediateTensorInfo(w), u && o.disposeIntermediateTensorInfo(c), S;
}
function bp(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
return vD(n, s, a, t10);
}
var kD = { kernelName: ys, backendName: "webgl", kernelFunc: bp };
function bt(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t10, i = n.shape.length, p = new Array(i);
for (let c = 0; c < p.length; c++)
p[c] = n.shape[s[c]];
let u;
if (a.shouldExecuteOnCPU([n])) {
let l = a.texData.get(n.dataId).values, m = yp(l, n.shape, n.dtype, s, p);
u = a.makeTensorInfo(p, n.dtype);
let d = a.texData.get(u.dataId);
d.values = m;
} else
u = gu(n, s, a);
return u;
}
var ND = { kernelName: ao, backendName: "webgl", kernelFunc: bt };
var tv = 1e3;
function Cp({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) {
let u = r.shape.length, c = e.shape.length, l = t10 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t10 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Sr.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]);
y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t10} and transposeB=${o} must match.`);
let k = t10 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = te({ inputs: { x: r }, backend: n, attrs: { shape: k } }), R = te({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), D = [E, R], F = Math.max(x, b), O = t10 ? E.shape[1] : E.shape[2], M = s != null, L = a != null, B = p === "leakyrelu", z = p != null ? hi(p, true) : null, U = M || L || B || z != null, j;
if ((d === 1 || f === 1) && O > tv && U === false) {
let X = E, J = R;
t10 && (X = bt({ inputs: { x: E }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(X)), o && (J = bt({ inputs: { x: R }, backend: n, attrs: { perm: [0, 2, 1] } }), D.push(J));
let re = f !== 1, ne = f === 1, ee = X;
re && (ee = te({ inputs: { x: X }, backend: n, attrs: { shape: [F, O, 1] } }), D.push(ee));
let oe = f === 1 ? 2 : 1, ie = J;
ne && (ie = te({ inputs: { x: J }, backend: n, attrs: { shape: [F, 1, O] } }), D.push(ie));
let le = Zl({ inputs: { a: ee, b: ie }, backend: n });
j = bp({ inputs: { x: le }, backend: n, attrs: { axis: oe, keepDims: true } }), D.push(le);
} else {
let X = dt(r.dtype, e.dtype), J = new Lc(k, _, [F, d, f], t10, o, M, z, L, B), re = [E, R];
if (s != null && re.push(s), L && re.push(a), B) {
let ne = n.makeTensorInfo([], "float32", y.createScalarValue(i, "float32"));
re.push(ne), D.push(ne);
}
j = n.runWebGLProgram(J, re, X);
}
let H = te({ inputs: { x: j }, backend: n, attrs: { shape: S } });
D.push(j);
for (let X of D)
n.disposeIntermediateTensorInfo(X);
return H;
}
function IZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o;
return Cp({ a: n, b: s, transposeA: p, transposeB: u, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c });
}
var TD = { kernelName: bo, backendName: "webgl", kernelFunc: IZ };
var _D = "return abs(x);";
function vZ(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
if (t10.shouldExecuteOnCPU([o]) && o.dtype !== "complex64") {
let s = t10.texData.get(o.dataId), a = ih(s.values);
return t10.makeTensorInfo(o.shape, o.dtype, a);
}
let n;
return P().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Ar(o.shape, _D) : n = new tr(o.shape, _D), t10.runWebGLProgram(n, [o], o.dtype);
}
var $D = { kernelName: Gs, backendName: "webgl", kernelFunc: vZ };
var kZ = Ut + `
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`;
var NZ = ge({ opSnippet: kZ });
var ED = { kernelName: zo, backendName: "webgl", kernelFunc: NZ };
var TZ = Ut + `
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`;
var _Z = ge({ opSnippet: TZ });
var RD = { kernelName: Vo, backendName: "webgl", kernelFunc: _Z };
var DD = "return a + b;";
var $Z = nt({ opSnippet: DD, packedOpSnippet: DD, supportsComplex: true, cpuKernelImpl: cR });
var AD = { kernelName: no, backendName: "webgl", kernelFunc: $Z };
var gh = class {
constructor(e, t10) {
this.outputShape = [], this.outputShape = e, this.variableNames = t10.map((s, a) => `T${a}`);
let o = [];
this.variableNames.forEach((s) => {
o.push(`float v${s} = get${s}AtOutCoords();`);
});
let n = this.variableNames.map((s) => `v${s}`).join(" + ");
this.userCode = `
void main() {
${o.join(`
`)}
float result = ${n};
setOutput(result);
}
`;
}
};
var xh = class {
constructor(e, t10) {
this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t10.map((s, a) => `T${a}`);
let o = [];
this.variableNames.forEach((s) => {
o.push(`vec4 v${s} = get${s}AtOutCoords();`);
});
let n = this.variableNames.map((s) => `v${s}`).join(" + ");
this.userCode = `
void main() {
${o.join(`
`)}
vec4 result = ${n};
setOutput(result);
}
`;
}
};
function yh(r) {
let { inputs: e, backend: t10 } = r, o = e;
if (o.length === 1)
return Dt({ inputs: { x: o[0] }, backend: t10 });
if (o.length > P().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
let p = Math.floor(o.length / 2), u = yh({ inputs: o.slice(0, p), backend: t10 }), c = yh({ inputs: o.slice(p), backend: t10 });
return yh({ inputs: [u, c], backend: t10 });
}
let n = o.map((p) => p.dtype).reduce((p, u) => dt(p, u)), s = o.map((p) => p.shape), i = P().getBool("WEBGL_PACK") ? new xh(o[0].shape, s) : new gh(o[0].shape, s);
return t10.runWebGLProgram(i, o, n);
}
var FD = { kernelName: Wo, backendName: "webgl", kernelFunc: yh };
function EZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = C.getAxesPermutation(u, i), l = n;
c != null && (l = bt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = C.getInnerMostAxes(u.length, i)), C.assertAxesAreInnerMostDims("all", u, i);
let [m, d] = C.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = qr(h, h.dtype, "all", t10), x;
if (a) {
let b = C.expandShapeToKeepDim(m, p);
x = te({ inputs: { x: g }, backend: t10, attrs: { shape: b } });
} else
x = te({ inputs: { x: g }, backend: t10, attrs: { shape: m } });
return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x;
}
var PD = { kernelName: Uo, backendName: "webgl", kernelFunc: EZ };
function RZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = C.getAxesPermutation(u, i), l = n;
c != null && (l = bt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = C.getInnerMostAxes(u.length, i)), C.assertAxesAreInnerMostDims("any", u, i);
let [m, d] = C.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = qr(h, h.dtype, "any", t10), x;
if (a) {
let b = C.expandShapeToKeepDim(m, p);
x = te({ inputs: { x: g }, backend: t10, attrs: { shape: b } });
} else
x = te({ inputs: { x: g }, backend: t10, attrs: { shape: m } });
return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x;
}
var OD = { kernelName: Go, backendName: "webgl", kernelFunc: RZ };
var bh = class {
constructor(e, t10, o) {
this.variableNames = ["A"];
let { windowSize: n, batchSize: s, outSize: a } = e;
o || this.variableNames.push("bestIndicesA"), this.outputShape = [s, a];
let i = t10 === "max" ? ">" : "<", p = o ? "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 * ${n};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${n}; i++) {
int inIdx = ${p};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`;
}
};
var Ch = class {
constructor(e, t10, o, n) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, y.assert(e.length > 2, () => `Packed arg${o.charAt(0).toUpperCase() + o.slice(1)} supports only inputs with rank above 2.`);
let s = e[e.length - 1], a = Math.ceil(s / t10);
this.outputShape = e.slice(0, -1), a > 1 && this.outputShape.push(a), n || this.variableNames.push("bestIndicesA");
let i = this.outputShape, p = i.length, u = Re(p), c = Rt("coords", p), l, m;
if (a === 1) {
m = p + 1;
let R = Re(m);
l = `
${R} sourceLocR = ${R}(${c.join()}, 0);
++${c[p - 1]};
${R} sourceLocG = ${R}(${c.join()}, 0);
++${c[p - 2]};
${R} sourceLocA = ${R}(${c.join()}, 0);
--${c[p - 1]};
${R} sourceLocB = ${R}(${c.join()}, 0);
--${c[p - 2]};`;
} else
m = p, l = `
${u} sourceLocR = coords;
++${c[p - 1]};
${u} sourceLocG = coords;
++${c[p - 2]};
${u} sourceLocA = coords;
--${c[p - 1]};
${u} sourceLocB = coords;
--${c[p - 2]};`;
let d = ["x", "y", "z", "w", "u", "v"].slice(0, m), f = "." + d[m - 1], h = d.map((R) => "int " + R), g = Rt("sourceLocR", m - 1).concat("inIdx.r"), x = Rt("sourceLocG", m - 1).concat("inIdx.g"), b = Rt("sourceLocB", m - 1).concat("inIdx.b"), w = Rt("sourceLocA", m - 1).concat("inIdx.a"), S = o === "max" ? "greaterThan" : "lessThan", k = n ? "" : `
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${x.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${w.join()})));`, _ = `vec4(
getAChannel(${g.join()}),
hasNextCol ? getAChannel(${x.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`, E = n ? "" : `
float getBestIndicesAChannel(${h.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;
this.userCode = `
float getAChannel(${h.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${E}
void main() {
${u} coords = getOutputCoords();
bool hasNextCol = ${c[p - 1]} < ${i[p - 1] - 1};
bool hasNextRow = ${c[p - 2]} < ${i[p - 2] - 1};
${l}
ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},
sourceLocB${f}, sourceLocA${f}) * ${t10};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${_};
for (int i = 0; i < ${t10}; i++) {
inIdx = srcIdx;
${k}
vec4 candidate = ${_};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${S}(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 MD(r, e, t10, o = null) {
let n = e.shape[0], s = e.shape[1];
o != null && (n = o.shape[0], s = o.shape[1]);
let a = C.computeOptimalWindowSize(s), i = { windowSize: a, inSize: s, batchSize: n, outSize: Math.ceil(s / a) }, p = new bh(i, t10, o == null), u = [e];
o != null && u.push(o);
let c = r.runWebGLProgram(p, u, "int32");
if (c.shape[1] === 1)
return c;
let l = MD(r, e, t10, c);
return r.disposeIntermediateTensorInfo(c), l;
}
function LD(r, e, t10, o = null) {
let n = o != null ? o.shape : e.shape, s = n[n.length - 1], a = C.computeOptimalWindowSize(s), i = new Ch(n, a, t10, o == null), p = o == null ? [e] : [e, o], u = r.runWebGLProgram(i, p, "int32");
if (u.shape.length === e.shape.length) {
let c = LD(r, e, t10, u);
return r.disposeIntermediateTensorInfo(u), c;
}
return u;
}
function wh(r, e, t10, o) {
let n = [t10];
if (C.assertAxesAreInnerMostDims("arg" + o.charAt(0).toUpperCase() + o.slice(1), n, e.shape.length), !P().getBool("WEBGL_PACK_REDUCE") || e.shape.length <= 2) {
let s = [], a = r.texData.get(e.dataId), i = a !== null && a.isPacked, p = e;
i && (p = r.unpackTensor(e), s.push(p));
let [u, c] = C.computeOutAndReduceShapes(p.shape, n), l = y.sizeFromShape(c), m = te({ inputs: { x: p }, backend: r, attrs: { shape: [-1, l] } });
s.push(m);
let d = MD(r, m, o);
s.push(d);
let f = te({ inputs: { x: d }, backend: r, attrs: { shape: u } });
return s.forEach((h) => r.disposeIntermediateTensorInfo(h)), f;
}
return LD(r, e, o);
}
function DZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = bt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), C.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length);
let c = wh(t10, p, a[0], "max");
return u.forEach((l) => t10.disposeIntermediateTensorInfo(l)), c;
}
var BD = { kernelName: Hs, backendName: "webgl", kernelFunc: DZ };
function AZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = bt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), C.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length);
let c = wh(t10, p, a[0], "min");
return u.forEach((l) => t10.disposeIntermediateTensorInfo(l)), c;
}
var zD = { kernelName: Ks, backendName: "webgl", kernelFunc: AZ };
var FZ = Ut + `
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`;
var PZ = ge({ opSnippet: FZ });
var VD = { kernelName: Ho, backendName: "webgl", kernelFunc: PZ };
var OZ = Ut + "return log(x + sqrt(x * x + 1.0));";
var MZ = ge({ opSnippet: OZ });
var WD = { kernelName: Ko, backendName: "webgl", kernelFunc: MZ };
var LZ = Ut + `
return atan(x);
`;
var BZ = ge({ opSnippet: LZ });
var UD = { kernelName: qo, backendName: "webgl", kernelFunc: BZ };
var zZ = Mc + `
return atan(a, b);
`;
var VZ = `
vec4 result = atan(a, b);
bvec4 isNaNA = isnan(a);
bvec4 isNaNB = isnan(b);
bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
` + Kr + `
return result;
`;
var WZ = nt({ opSnippet: zZ, packedOpSnippet: VZ });
var GD = { kernelName: Xo, backendName: "webgl", kernelFunc: WZ };
var UZ = Ut + `
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
var GZ = ge({ opSnippet: UZ });
var HD = { kernelName: jo, backendName: "webgl", kernelFunc: GZ };
var Ms = class {
constructor(e, t10, o, n = false, s = false) {
if (this.variableNames = ["x"], t10 === "avg" && o)
throw new Error("Cannot compute positions for average pool.");
let a = e.filterWidth, i = e.strideHeight, p = e.strideWidth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterHeight, m = e.effectiveFilterWidth, d = e.padInfo.top, f = e.padInfo.left;
this.outputShape = e.outShape;
let h = t10 === "avg", g = `((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`, x = `(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`, b = "0.0";
if (h || (b = "-1.0 / 1e-20"), o) {
let R = ">=";
this.userCode = `
const ivec2 strides = ivec2(${i}, ${p});
const ivec2 pads = ivec2(${d}, ${f});
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 < ${l};
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 = ${n ? s ? g : x : `wR * ${m} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;
return;
}
let w = "max", S = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
t10 === "avg" && (S = "avgValue / max(count, 1.0)");
let k = Math.floor(a / 4) * 4, _ = a % 4, E = `
if (${h}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${w}(values, minMaxValue);
}
`;
this.userCode = `
const ivec2 strides = ivec2(${i}, ${p});
const ivec2 pads = ivec2(${d}, ${f});
const float initializationValue = ${b};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${b});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${l};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${k}; 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)
);
${E}
}
int xC = xCCorner + ${k};
if (${_ === 1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${E}
} else if (${_ === 2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${E}
} else if (${_ === 3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${E}
}
}
setOutput(${S});
}
`;
}
};
var xu = class {
constructor(e, t10, o, n = false, s = false) {
if (this.variableNames = ["x"], t10 === "avg" && o)
throw new Error("Cannot compute positions for average pool.");
let a = e.filterWidth, i = e.strideDepth, p = e.strideHeight, u = e.strideWidth, c = e.dilationDepth, l = e.dilationHeight, m = e.dilationWidth, d = e.effectiveFilterDepth, f = e.effectiveFilterHeight, h = e.effectiveFilterWidth, g = e.padInfo.front, x = e.padInfo.top, b = e.padInfo.left;
this.outputShape = e.outShape;
let w = t10 === "avg", S = "0.0";
if (w || (S = "-1.0 / 1e-20"), o) {
let F = ">=";
this.userCode = `
const ivec3 strides =
ivec3(${i}, ${p}, ${u});
const ivec3 pads = ivec3(${g}, ${x}, ${b});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${f};
wR += ${l}) {
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 ${F} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${n ? 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 * ${f} * ${h} +
wR * ${h} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;
return;
}
let k = "max", _ = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
t10 === "avg" && (_ = "avgValue / max(count, 1.0)");
let E = Math.floor(a / 4) * 4, R = a % 4, D = `
if (${w}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${k}(values, minMaxValue);
}
`;
this.userCode = `
const ivec3 strides =
ivec3(${i}, ${p}, ${u});
const ivec3 pads = ivec3(${g}, ${x}, ${b});
const float initializationValue = ${S};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${S});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${f};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${E}; 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)
);
${D}
}
int xC = xCCorner + ${E};
if (${R === 1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${D}
} else if (${R === 2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
initializationValue,
initializationValue
);
${D}
} else if (${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
);
${D}
}
}
}
setOutput(${_});
}
`;
}
};
function HZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
Ps(n, "avgPool");
let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
y.assert(C.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
let c = C.computePool2DInfo(n.shape, s, a, u, i, p);
if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape))
return Dt({ inputs: { x: n }, backend: t10 });
let l = new Ms(c, "avg", false);
return t10.runWebGLProgram(l, [n], "float32");
}
var KD = { kernelName: Yo, backendName: "webgl", kernelFunc: HZ };
function KZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = [1, 1, 1], l = C.computePool3DInfo(n.shape, s, a, c, i, p, u), m = new xu(l, "avg", false);
return t10.runWebGLProgram(m, [n], "float32");
}
var qD = { kernelName: qs, backendName: "webgl", kernelFunc: KZ };
var Sh = class {
constructor(e) {
this.variableNames = ["dy"], this.outputShape = e.inShape;
let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterHeight, u = e.effectiveFilterWidth, c = p - 1 - e.padInfo.top, l = u - 1 - e.padInfo.left, m = 1 / (t10 * o);
this.userCode = `
const ivec2 pads = ivec2(${c}, ${l});
const float avgMultiplier = float(${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 < ${p};
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 < ${u};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`;
}
};
var Ih = class {
constructor(e) {
this.variableNames = ["dy"], this.outputShape = e.inShape;
let t10 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterDepth, m = e.effectiveFilterHeight, d = e.effectiveFilterWidth, f = l - 1 - e.padInfo.front, h = m - 1 - e.padInfo.top, g = d - 1 - e.padInfo.left, x = 1 / (t10 * o * n);
this.userCode = `
const ivec3 pads = ivec3(${f}, ${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 < ${l};
wD += ${p}) {
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 < ${d};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`;
}
};
function qZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = C.computePool3DInfo(a.shape, i, p, l, u, c), d = new Ih(m);
return t10.runWebGLProgram(d, [n], a.dtype);
}
var jD = { kernelName: Ni, backendName: "webgl", kernelFunc: qZ };
function jZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s;
Ps([n, s], "avgPoolGrad");
let { filterSize: i, strides: p, pad: u } = o, c = C.computePool2DInfo(a.shape, i, p, 1, u), l = new Sh(c);
return t10.runWebGLProgram(l, [n], a.dtype);
}
var XD = { kernelName: Gp, backendName: "webgl", kernelFunc: jZ };
function XZ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
return Cp({ a: n, b: s, transposeA: a, transposeB: i, backend: t10 });
}
var YD = { kernelName: Qo, backendName: "webgl", kernelFunc: XZ };
var vh = class {
constructor(e, t10, o, n, s, a) {
this.outputShape = [], this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t10), C.assertAndGetBroadcastShape(e, o);
let i = "0.0";
n != null && (C.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
let p = "1.0";
s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale"), p = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = `
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${p};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`;
}
};
var kh = class {
constructor(e, t10, o, n, s, a) {
this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t10), C.assertAndGetBroadcastShape(e, o);
let i = "vec4(0.0)";
n != null && (C.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
let p = "vec4(1.0)";
s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale"), p = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = `
void main() {
vec4 offset = ${i};
vec4 scale = ${p};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`;
}
};
var YZ = ({ inputs: r, backend: e, attrs: t10 }) => {
let { x: o, mean: n, variance: s, offset: a, scale: i } = r;
y.assert(n.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || n.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(i == null || n.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
let { varianceEpsilon: p } = t10;
p == null && (p = 1e-3);
let u = [o, n, s], c = null;
a != null && (c = a.shape, u.push(a));
let l = null;
i != null && (l = i.shape, u.push(i));
let m = P().getBool("WEBGL_PACK_NORMALIZATION") ? new kh(o.shape, n.shape, s.shape, c, l, p) : new vh(o.shape, n.shape, s.shape, c, l, p);
return e.runWebGLProgram(m, u, u[0].dtype);
};
var QD = { kernelName: wn, backendName: "webgl", kernelFunc: YZ };
var Nh = class {
constructor(e) {
this.variableNames = ["source"], this.outputShape = e, this.rank = e.length;
let t10 = Re(this.rank);
this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
let o = QZ(this.rank), n, s = e.map((a, i) => `sourceLoc.${rv[i]} = start[${i}] + coords.${rv[i]};`);
n = `
${t10} sourceLoc;
${t10} coords = getOutputCoords();
${s.join(`
`)}
`, this.userCode = `
void main() {
${n}
setOutput(getSource(${o}));
}
`;
}
};
var rv = ["x", "y", "z", "w", "u", "v"];
function QZ(r) {
if (r === 1)
return "sourceLoc";
if (r <= 6)
return rv.slice(0, r).map((e) => "sourceLoc." + e).join(",");
throw Error(`Slicing for rank ${r} is not yet supported`);
}
var Th = class {
constructor(e) {
this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
let t10 = Re(this.rank), o = Rt("coords", this.rank), n = Rt("sourceLoc", this.rank), s = this.rank === 1 ? "sourceLoc" : `vec2(${n.slice(-2).join()})`, a = `getChannel(getSource(${n.join()}), ${s})`, i = `
result.x = ${a};
if (++${o[this.rank - 1]} < ${e[this.rank - 1]}) {
++${n[this.rank - 1]};
result.y = ${a};
--${n[this.rank - 1]};
}
`, p = this.rank === 1 ? "" : `
--${o[this.rank - 1]};
if (++${o[this.rank - 2]} < ${e[this.rank - 2]}) {
++${n[this.rank - 2]};
result.z = ${a};
if (++${o[this.rank - 1]} < ${e[this.rank - 1]}) {
++${n[this.rank - 1]};
result.w = ${a};
}
}
`, u = this.rank <= 4 ? `sourceLoc = coords +
${t10}(${e.map((c, l) => `start[${l}]`).join()});` : e.map((c, l) => `${n[l]} = ${o[l]} + start[${l}];`).join(`
`);
this.userCode = `
void main() {
${t10} coords = getOutputCoords();
${t10} sourceLoc;
${u}
vec4 result = vec4(0.);
${i}
${p}
setOutput(result);
}
`;
}
};
function ZZ(r, e, t10, o) {
let n = o.texData.get(r.dataId), s = o.makeTensorInfo(t10, r.dtype), a = o.texData.get(s.dataId);
Object.assign(a, n), a.refCount = 1, a.shape = t10, a.dtype = r.dtype;
let i = ct.computeFlatOffset(e, y.computeStrides(r.shape));
n.slice && (i += n.slice.flatOffset), a.slice = { flatOffset: i, origDataId: n.slice && n.slice.origDataId || r.dataId };
let p = o.dataRefCount.get(a.slice.origDataId) || 1;
return o.dataRefCount.set(a.slice.origDataId, p + 1), s;
}
function Ls(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ct.parseSliceParams(n, s, a);
if (ct.assertParamsValid(n, i, p), y.sizeFromShape(p) === 0)
return t10.makeTensorInfo(p, n.dtype, []);
if (t10.shouldExecuteOnCPU([n]) || n.dtype === "string") {
let l = t10.texData.get(n.dataId), m = VR(l.values, i, p, n.shape, n.dtype);
return t10.makeTensorInfo(p, n.dtype, m);
}
let { isPacked: u } = t10.texData.get(n.dataId), c = ct.isSliceContinous(n.shape, i, p);
if (u || !c) {
let l = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new Th(p) : new Nh(p), m = [i];
return t10.runWebGLProgram(l, [n], n.dtype, m);
}
return t10.uploadToGPU(n.dataId), ZZ(n, i, p, t10);
}
var ZD = { kernelName: pa, backendName: "webgl", kernelFunc: Ls };
var JZ = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o;
y.assert(n.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
let i = s.reduce((b, w) => b * w), p = C.getReshaped(n.shape, s, i), u = C.getPermuted(p.length, s.length), c = C.getReshapedPermuted(n.shape, s, i), l = C.getSliceBeginCoords(a, s.length), m = C.getSliceSize(c, a, s.length), d = [], f = te({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), h = bt({ inputs: { x: f }, backend: t10, attrs: { perm: u } }), g = te({ inputs: { x: h }, backend: t10, attrs: { shape: c } }), x = Ls({ inputs: { x: g }, backend: t10, attrs: { begin: l, size: m } });
return d.push(f), d.push(h), d.push(g), d.forEach((b) => t10.disposeIntermediateTensorInfo(b)), x;
};
var JD = { kernelName: js, backendName: "webgl", kernelFunc: JZ };
function e9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), u = ah(i, p, s.dtype, s.shape, a);
return t10.makeTensorInfo([a], s.dtype, u);
}
var eA = { kernelName: Zo, backendName: "webgl", kernelFunc: e9 };
function t9(r) {
let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.readSync(o.dataId), a = t10.readSync(n.dataId), i = C.assertAndGetBroadcastShape(Array.from(s), Array.from(a));
return t10.makeTensorInfo([i.length], "int32", Int32Array.from(i));
}
var tA = { kernelName: Xs, backendName: "webgl", kernelFunc: t9 };
var r9 = "return float(a != b);";
var ov = nt({ opSnippet: r9, cpuKernelImpl: DR, dtype: "bool" });
var rA = { kernelName: qn, backendName: "webgl", kernelFunc: ov };
function gi(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.texData.get(o.dataId);
return Dt({ inputs: { x: n.complexTensorInfos.real }, backend: t10 });
}
var oA = { kernelName: zi, backendName: "webgl", kernelFunc: gi };
var o9 = "return float(int(x));";
function nA(r, e) {
let t10 = new tr(r.shape, o9), o = e.runWebGLProgram(t10, [r], "int32");
return { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
function nv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o;
if (s === "complex64") {
if (n.dtype === "complex64")
return Dt({ inputs: { x: n }, backend: t10 });
let a = Wr(n.shape), i = nv({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), p = Fr({ inputs: { real: i, imag: a }, backend: t10 });
return a.dispose(), t10.disposeIntermediateTensorInfo(i), p;
}
if (n.dtype === "complex64") {
let a = gi({ inputs: { input: n }, backend: t10 }), i = nv({ inputs: { x: a }, backend: t10, attrs: { dtype: s } });
return t10.disposeIntermediateTensorInfo(a), i;
}
if (!y.hasEncodingLoss(n.dtype, s)) {
let a = Dt({ inputs: { x: n }, backend: t10 });
return { dataId: a.dataId, shape: a.shape, dtype: s };
}
if (t10.shouldExecuteOnCPU([n])) {
let a = t10.texData.get(n.dataId).values, [i, p, u] = mR(a, n.shape, n.dtype, s);
return t10.makeTensorInfo(i, p, u);
}
if (s === "int32")
return nA(n, t10);
if (s === "bool") {
let a = t10.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = ov({ inputs: { a: n, b: a }, backend: t10 });
return t10.disposeIntermediateTensorInfo(a), p;
}
throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`);
}
var sA = { kernelName: ho, backendName: "webgl", kernelFunc: nv };
var aA = "return ceil(x);";
var n9 = ge({ opSnippet: aA, packedOpSnippet: aA, cpuKernelImpl: dR });
var iA = { kernelName: Jo, backendName: "webgl", kernelFunc: n9 };
var _h = 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 $h = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, 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 s9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i;
P().getBool("WEBGL_PACK_CLIP") ? i = new $h(n.shape) : i = new _h(n.shape);
let p = [[s], [a]];
return t10.runWebGLProgram(i, [n], n.dtype, p);
}
var uA = { kernelName: go, backendName: "webgl", kernelFunc: s9 };
var Eh = class {
constructor(e) {
this.variableNames = ["real", "imag"], this.outputShape = e, this.userCode = `
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`;
}
};
function pA(r, e) {
return { dataId: e.dataId, dtype: e.dtype, shape: r.shape };
}
function a9(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e, n = t10.texData.get(o.dataId), s = new Eh(o.shape), a = [pA(o, n.complexTensorInfos.real), pA(o, n.complexTensorInfos.imag)];
return t10.runWebGLProgram(s, a, a[0].dtype);
}
var cA = { kernelName: _i, backendName: "webgl", kernelFunc: a9 };
var Rh = class {
constructor(e) {
this.outputShape = [], this.outputShape = C.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${i}`);
let t10 = new Array(e.length - 1);
t10[0] = e[0][1];
for (let a = 1; a < t10.length; a++)
t10[a] = t10[a - 1] + e[a][1];
let o = [`if (yC < ${t10[0]}) setOutput(getT0(yR, yC));`];
for (let a = 1; a < t10.length; a++) {
let i = t10[a - 1];
o.push(`else if (yC < ${t10[a]}) setOutput(getT${a}(yR, yC-${i}));`);
}
let n = t10.length, s = t10[t10.length - 1];
o.push(`else setOutput(getT${n}(yR, yC-${s}));`), this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${o.join(`
`)}
}
`;
}
};
var Ah = class {
constructor(e, t10) {
this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = C.computeOutShape(e, t10);
let o = this.outputShape, n = o.length, s = Re(n), a = Rt("coords", n), i = ["x", "y", "z", "w", "u", "v"].slice(0, n);
this.variableNames = e.map((h, g) => `T${g}`);
let p = new Array(e.length - 1);
p[0] = e[0][t10];
for (let h = 1; h < p.length; h++)
p[h] = p[h - 1] + e[h][t10];
let u = i[t10], c = i.slice(-2), l = i.join(), m = `if (${u} < ${p[0]}) {
return getChannel(
getT0(${l}), vec2(${c.join()}));
}`;
for (let h = 1; h < p.length; h++) {
let g = p[h - 1];
m += `
if (${u} < ${p[h]} && ${u} >= ${p[h - 1]}) {
return getChannel(
getT${h}(${Dh(i, u, g)}),
vec2(${Dh(c, u, g)}));
}`;
}
let d = p.length, f = p[p.length - 1];
m += `
return getChannel(
getT${d}(${Dh(i, u, f)}),
vec2(${Dh(c, u, f)}));`, this.userCode = `
float getValue(${i.map((h) => "int " + h)}) {
${m}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[n - 1]} = ${a[n - 1]} + 1;
if (${a[n - 1]} < ${o[n - 1]}) {
result.g = getValue(${a});
}
${a[n - 2]} = ${a[n - 2]} + 1;
if (${a[n - 2]} < ${o[n - 2]}) {
result.a = getValue(${a});
}
${a[n - 1]} = ${a[n - 1]} - 1;
if (${a[n - 2]} < ${o[n - 2]} &&
${a[n - 1]} < ${o[n - 1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`;
}
};
function Dh(r, e, t10) {
let o = r.indexOf(e);
return r.map((s, a) => a === o ? `${s} - ${t10}` : s).join();
}
function wp(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.texData.get(o.dataId);
return Dt({ inputs: { x: n.complexTensorInfos.imag }, backend: t10 });
}
var lA = { kernelName: Mi, backendName: "webgl", kernelFunc: wp };
function Bc(r, e, t10) {
let o = r[0].dtype;
if (o === "complex64") {
let d = r.map((b) => gi({ inputs: { input: b }, backend: t10 })), f = r.map((b) => wp({ inputs: { input: b }, backend: t10 })), h = Bc(d, e, t10), g = Bc(f, e, t10), x = Fr({ inputs: { real: h, imag: g }, backend: t10 });
return d.forEach((b) => t10.disposeIntermediateTensorInfo(b)), f.forEach((b) => t10.disposeIntermediateTensorInfo(b)), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), x;
}
let n = t10.shouldExecuteOnCPU(r);
if (o === "string" && (n = true), n) {
let d = r.map((S) => {
let _ = [-1, y.sizeFromShape(S.shape.slice(e))];
return te({ inputs: { x: S }, backend: t10, attrs: { shape: _ } });
}), f = d.map((S) => ({ vals: t10.readSync(S.dataId), shape: S.shape })), h = C.computeOutShape(d.map((S) => S.shape), 1), g = d[0].shape[0] === 1, x = fR(f, h, o, g), b = C.computeOutShape(r.map((S) => S.shape), e), w = t10.makeTensorInfo(b, o, x);
return d.forEach((S) => t10.disposeIntermediateTensorInfo(S)), w;
}
let s = r.filter((d) => y.sizeFromShape(d.shape) > 0), a = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && s[0].shape.length > 1;
if (s.length === 1) {
let d = a ? new tr(r[0].shape, Ra) : new Ar(r[0].shape, Ra);
return t10.runWebGLProgram(d, r, o);
}
let i = P().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");
if (s.length > i) {
let d = [];
for (let h = 0; h < s.length; h += i) {
let g = s.slice(h, h + i);
d.push(Bc(g, e, t10));
}
let f = Bc(d, e, t10);
for (let h of d)
t10.disposeIntermediateTensorInfo(h);
return f;
}
if (a) {
let d = new Ah(s.map((f) => f.shape), e);
return t10.runWebGLProgram(d, s, o);
}
let { tensors2D: p, outShape: u } = i9(s, e, t10), c = new Rh(p.map((d) => d.shape)), l = t10.runWebGLProgram(c, p, o);
p.forEach((d) => t10.disposeIntermediateTensorInfo(d));
let m = te({ inputs: { x: l }, attrs: { shape: u }, backend: t10 });
return t10.disposeIntermediateTensorInfo(l), m;
}
function i9(r, e, t10) {
let o = C.computeOutShape(r.map((s) => s.shape), e);
return { tensors2D: r.map((s) => te({ inputs: { x: s }, attrs: { shape: [-1, y.sizeFromShape(s.shape.slice(e))] }, backend: t10 })), outShape: o };
}
function sv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape);
C.assertParamsConsistent(a, s);
let i = C.computeOutShape(e.map((u) => u.shape), s);
if (y.sizeFromShape(i) === 0)
return t10.makeTensorInfo(i, e[0].dtype, []);
let p = e.filter((u) => y.sizeFromShape(u.shape) > 0);
return p.length === 1 ? Dt({ inputs: { x: p[0] }, backend: t10 }) : Bc(p, s, t10);
}
var mA = { kernelName: Ys, backendName: "webgl", kernelFunc: sv };
var zc = class {
constructor(e, t10 = false, o = null, n = false, s = false) {
this.variableNames = ["x", "W"], this.outputShape = e.outShape;
let a = e.padInfo.top, i = e.padInfo.left, p = e.strideHeight, u = e.strideWidth, c = e.dilationHeight, l = e.dilationWidth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4, g = e.dataFormat === "channelsLast", x = g ? 1 : 2, b = g ? 2 : 3, w = g ? 3 : 1, S = "", k = "";
o && (n ? S = `float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${o}
}` : s ? S = `float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${o}
}` : S = `
float activation(float x) {
${o}
}
`, k = "result = activation(result);");
let _ = t10 ? "result += getBiasAtOutCoords();" : "";
t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = `
${S}
const ivec2 strides = ivec2(${p}, ${u});
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${w}];
ivec2 xRCCorner =
ivec2(coords[${x}], coords[${b}]) * 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 < ${d}; wC++) {
int xC = xCCorner + wC * ${l};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${f}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${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, ${f}) *
getW(wR, wC, ${f}, d2);
} else {
dotProd +=
getX(batch, ${f}, xR, xC) *
getW(wR, wC, ${f}, d2);
}
} else if (${h === 2}) {
vec2 wValues = vec2(
getW(wR, wC, ${f}, d2),
getW(wR, wC, ${f} + 1, d2)
);
if (${g}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${f}),
getX(batch, xR, xC, ${f} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${f}, xR, xC),
getX(batch, ${f} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${h === 3}) {
vec3 wValues = vec3(
getW(wR, wC, ${f}, d2),
getW(wR, wC, ${f} + 1, d2),
getW(wR, wC, ${f} + 2, d2)
);
if (${g}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${f}),
getX(batch, xR, xC, ${f} + 1),
getX(batch, xR, xC, ${f} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${f}, xR, xC),
getX(batch, ${f} + 1, xR, xC),
getX(batch, ${f} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${_}
${k}
setOutput(result);
}
`;
}
};
var Fh = class {
constructor(e) {
this.variableNames = ["x", "W"], this.outputShape = e.outShape;
let t10 = e.padInfo.front, o = e.padInfo.top, n = e.padInfo.left, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.filterDepth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4;
this.userCode = `
const ivec3 strides = ivec3(${s}, ${a}, ${i});
const ivec3 pads = ivec3(${t10}, ${o}, ${n});
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 < ${l}; wF++) {
int xF = xFCorner + wF * ${p};
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 < ${d}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${f}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${h === 1}) {
dotProd +=
getX(batch, xF, xR, xC, ${f}) *
getW(wF, wR, wC, ${f}, d2);
} else if (${h === 2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${f}),
getX(batch, xF, xR, xC, ${f} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${f}, d2),
getW(wF, wR, wC, ${f} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${h === 3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${f}),
getX(batch, xF, xR, xC, ${f} + 1),
getX(batch, xF, xR, xC, ${f} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${f}, d2),
getW(wF, wR, wC, ${f} + 1, d2),
getW(wF, wR, wC, ${f} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`;
}
};
var Vc = class {
constructor(e, t10 = false, o = null, n = false, s = false) {
this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = ut(this.outputShape.length);
let a = e.padInfo.left, i = e.strideWidth, p = e.dilationWidth, u = e.filterHeight, c = e.filterWidth, l = c, m = `
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;
for (let g = 0; g < c; g++)
m += `
vec4 xTexelC${g * 2};
int xTexelC${g * 2}Ready;
vec4 xTexelC${g * 2 + 1};
int xTexelC${g * 2 + 1}Ready;
vec4 xC${g};`;
m += `
for (int r = 0; r < ${u}; r++) {
for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {
`;
for (let g = 0; g < c; g++)
m += `
xTexelC${g * 2} = vec4(0.0);
xTexelC${g * 2}Ready = 0;
xTexelC${g * 2 + 1} = vec4(0.0);
xTexelC${g * 2 + 1}Ready = 0;
xC${g} = vec4(0.0);`;
m += `
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;
for (let g = 0; g < (l + 1) / 2; g++) {
let x = g * 2;
if (m += `
xC = xCCorner + ${x * p};
`, i === 1) {
if (x < c && (a % 2 === 1 ? (m += `
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
xTexelC${x} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${x}.zw = vec2(0.0);
}
xTexelC${x}Ready = 1;
}
`, p === 1 && x > 0 ? m += `
xC${x} = vec4(xTexelC${x - 2}.zw, xTexelC${x}.xy);
` : m += `
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${x} = vec4(previous.zw, xTexelC${x}.xy);
} else {
xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);
}
`) : m += `
if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {
xTexelC${x} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${x}.zw = vec2(0.0);
}
xTexelC${x}Ready = 1;
}
xC${x} = xTexelC${x};
`, x + 1 < c)) {
let b = a % 2 === 0 ? y.nearestLargerEven(p) : p;
p % 2 === 0 && a % 2 === 1 || p % 2 !== 0 && a % 2 !== 1 ? (m += `
xCOffset = xC + imod(pads[1], 2) + ${b};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x + 1}Ready == 0) {
xTexelC${x + 1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${x + 1}.zw = vec2(0.0);
}
xTexelC${x + 1}Ready = 1;
}
`, p > 1 ? m += `
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
xC${x + 1} = vec4(previous.zw, xTexelC${x + 1}.xy);
} else {
xC${x + 1} = vec4(0.0, 0.0, xTexelC${x + 1}.xy);
}
` : m += `
xC${x + 1} = vec4(xTexelC${x}.zw, xTexelC${x + 1}.xy);
`) : b === 1 ? m += `
xC${x + 1} = xTexelC${x};
` : m += `
xCOffset = xC + ${b};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x + 1}Ready == 0) {
xTexelC${x + 1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${x + 1}.zw = vec2(0.0);
}
xTexelC${x + 1}Ready = 1;
}
xC${x + 1} = xTexelC${x + 1};
`;
}
} else
x < c && (a % 2 === 1 ? (m += `
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
xTexelC${x} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${x}.zw = vec2(0.0);
}
xTexelC${x}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x + 1}Ready == 0) {
xTexelC${x + 1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${x + 1}.zw = vec2(0.0);
}
xTexelC${x + 1}Ready = 1;
}
xC${x} = vec4(xTexelC${x}.zw, xTexelC${x + 1}.zw);
`, x + 1 < c && (m += `
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${x + 1} = vec4(xTexelC${x + 1}.xy, final.xy);
`)) : (m += `
if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {
xTexelC${x} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${x}.zw = vec2(0.0);
}
xTexelC${x}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x + 1}Ready == 0) {
xTexelC${x + 1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${x + 1}.zw = vec2(0.);
}
xTexelC${x + 1}Ready = 1;
}
xC${x} = vec4(
xTexelC${x}.xy, xTexelC${x + 1}.xy);
`, x + 1 < c && (m += `
xC${x + 1} = vec4(xTexelC${x}.zw, xTexelC${x + 1}.zw);
`)));
x < c && (m += `
wTexel = getW(r, ${x}, d1, d2);
dotProd += xC${x}.xxzz * vec4(wTexel.xy, wTexel.xy);
if(d1 + 1 < ${e.inChannels}) {
dotProd += xC${x}.yyww * vec4(wTexel.zw, wTexel.zw);
}
`, x + 1 < c && (m += `
wTexel = getW(r, ${x + 1}, d1, d2);
dotProd += xC${x + 1}.xxzz * vec4(wTexel.xy, wTexel.xy);
if(d1 + 1 < ${e.inChannels}) {
dotProd += xC${x + 1}.yyww * vec4(wTexel.zw, wTexel.zw);
}
`));
}
m += `
}
`, m += `
}
`, m += `
}
`;
let d = "", f = "";
o && (n ? d = `vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}` : s ? d = `vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}` : d = `vec4 activation(vec4 x) {
${o}
}`, f = "result = activation(result);");
let h = t10 ? "result += getBiasAtOutCoords();" : "";
t10 && this.variableNames.push("bias"), n && 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 xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${m}
vec4 result = dotProd - vec4(0.000000000000001);
${h}
${f}
setOutput(result);
}
`;
}
};
var Ph = class {
constructor(e, t10) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec4" }, { name: "pad", type: "ivec2" }, { name: "stride", type: "ivec2" }, { name: "dilation", type: "ivec2" }, { name: "inChannels", type: "int" }, { name: "itemsPerBlockRow", type: "int" }, { name: "outWidth", type: "int" }], this.outputShape = e, this.enableShapeUniforms = ut(this.outputShape.length);
let { dataFormat: o } = t10, n = It(), s = o === "channelsLast", a = s ? 1 : 2, i = s ? 2 : 3, p = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${e[2]} && pos < ${e[1]}) {`, u = "";
for (let c = 0; c <= 1; c++)
for (let l = 0; l <= 1; l++)
u += `
blockIndex = rc.z + ${l};
pos = rc.y + ${c};
${p}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${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[${c * 2 + l}] = getChannel(
getA(rc.x, d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${c * 2 + l}] = getChannel(
getA(rc.x, ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;
this.userCode = `
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${u}
${n.output} = result;
}
`;
}
};
function Oh(r, e) {
let t10 = r.length;
return t10 >= 3 ? e ? [...r.slice(0, -3), r[t10 - 3] * r[t10 - 2], r[t10 - 1]] : [...r.slice(0, -3), r[t10 - 3], r[t10 - 2] * r[t10 - 1]] : !e && t10 === 1 && r[0] > 1 ? [r[0], 1] : null;
}
function Mh({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
let p = r.shape, u = o.texData.get(r.dataId), c = t10.inChannels, l = p[0] * p[1] * p[2], m = t10.outChannels, d = t10.dataFormat === "channelsLast", f = false, h = false, g, x = [];
if (s != null) {
let S = Oh(s.shape, d);
S != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: S } }), x.push(s));
}
if (n != null) {
let S = Oh(n.shape, d);
S != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: S } }), x.push(n));
}
if (!((l === 1 || m === 1) && c > tv) && u.isPacked && d && u.texture != null && p[2] % 2 !== 0 && y.arraysEqual(u.shape.slice(-3), p.slice(-3))) {
let S = p[0] * p[1] * (p[2] + 1), k = { dataId: r.dataId, shape: [1, S, t10.inChannels], dtype: r.dtype }, _ = u.shape;
u.shape = u.shape.slice(), u.shape[u.shape.length - 2]++, y.assert(fu(u.shape, k.shape), () => `packed reshape ${u.shape} to ${k.shape} isn't free`);
let E = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } });
x.push(E);
let R = Cp({ a: k, b: E, backend: o, transposeA: f, transposeB: h, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), D = o.texData.get(R.dataId);
y.assert(D.isPacked, () => "batchMatMul result is expected to be packed"), u.shape = _, D.shape = t10.outShape, g = Dt({ inputs: { x: R }, backend: o }), g.shape = t10.outShape, x.push(R);
} else {
let S = t10.outHeight * t10.outWidth, k = te({ inputs: { x: r }, backend: o, attrs: { shape: d ? [t10.batchSize, S, t10.inChannels] : [t10.batchSize, t10.inChannels, S] } }), _ = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } }), E = Cp({ a: d ? k : _, b: d ? _ : k, transposeA: !d, transposeB: h, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a });
g = te({ inputs: { x: E }, backend: o, attrs: { shape: t10.outShape } }), x.push(k), x.push(_), x.push(E);
}
for (let S of x)
o.disposeIntermediateTensorInfo(S);
return g;
}
function Lh({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
let { filterWidth: p, filterHeight: u, inChannels: c, outWidth: l, outHeight: m, dataFormat: d } = t10, f = d === "channelsLast", h = p * u * c, g = m * l, x = [t10.batchSize, h, g], b = true, w = false, S = [];
if (s != null) {
let H = Oh(s.shape, f);
H != null && (s = te({ inputs: { x: s }, backend: o, attrs: { shape: H } }), S.push(s));
}
if (n != null) {
let H = Oh(n.shape, f);
H != null && (n = te({ inputs: { x: n }, backend: o, attrs: { shape: H } }), S.push(n));
}
let k = te({ inputs: { x: e }, backend: o, attrs: { shape: [1, h, y.sizeFromShape(e.shape) / h] } });
S.push(k);
let _ = new Ph(x, t10), E = [r.shape, [t10.padInfo.top, t10.padInfo.left], [t10.strideHeight, t10.strideWidth], [t10.dilationHeight, t10.dilationWidth], [t10.inChannels], [t10.filterWidth * t10.inChannels], [t10.outWidth]], R = o.runWebGLProgram(_, [r], "float32", E), D = te({ inputs: { x: R }, backend: o, attrs: { shape: x } });
S.push(R), S.push(D);
let F = n != null, O = s != null, M = i === "leakyrelu", L = i ? hi(i, true) : null, B = new Lc(f ? D.shape : k.shape, f ? k.shape : D.shape, f ? [t10.batchSize, g, t10.outChannels] : [t10.batchSize, t10.outChannels, g], b, w, F, L, O, M), z = f ? [D, k] : [k, D];
if (n && z.push(n), O && z.push(s), M) {
let H = o.makeTensorInfo([], "float32", y.createScalarValue(a, "float32"));
z.push(H), S.push(H);
}
let U = o.runWebGLProgram(B, z, "float32"), j = te({ inputs: { x: U }, backend: o, attrs: { shape: t10.outShape } });
S.push(U);
for (let H of S)
o.disposeIntermediateTensorInfo(H);
return j;
}
function u9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d;
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"))
d = Mh({ x: n, filter: s, convInfo: m, backend: t10 });
else if (m.strideWidth <= 2 && l === "channelsLast" && P().getBool("WEBGL_EXP_CONV")) {
let h = new Vc(m), g = [[m.padInfo.top, m.padInfo.left], [m.strideHeight, m.strideWidth], [m.dilationHeight, m.dilationWidth], [m.inHeight, m.inWidth]];
d = t10.runWebGLProgram(h, [n, s], "float32", g);
} else if (P().getBool("WEBGL_CONV_IM2COL"))
d = Lh({ x: n, filter: s, convInfo: m, backend: t10 });
else {
let h = new zc(m);
d = t10.runWebGLProgram(h, [n, s], "float32");
}
let f = te({ inputs: { x: d }, backend: t10, attrs: { shape: m.outShape } });
return t10.disposeIntermediateTensorInfo(d), f;
}
var dA = { kernelName: en, backendName: "webgl", kernelFunc: u9 };
var Bh = class {
constructor(e) {
this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
let t10 = e.strideHeight, o = e.strideWidth, n = 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 * ${t10} - ${n};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
${a ? `float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);` : `float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);`}
}
}
}
setOutput(dotProd);
}
`;
}
};
var zh = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dataFormat === "channelsLast", i = t10 - 1 - e.padInfo.top, p = o - 1 - e.padInfo.left, u = a ? 1 : 2, c = a ? 2 : 3, l = a ? 3 : 1;
this.userCode = `
const ivec2 pads = ivec2(${i}, ${p});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${l}];
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 < ${t10}; wR++) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t10} - 1 - wR;
for (int wC = 0; wC < ${o}; 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 = ${o} - 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);
}
`;
}
};
var Vh = class {
constructor(e) {
this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
let t10 = e.strideDepth, o = e.strideHeight, n = 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 * ${t10} - ${s};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${o} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`;
}
};
var Wh = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
let t10 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = t10 - 1 - e.padInfo.front, u = o - 1 - e.padInfo.top, c = n - 1 - e.padInfo.left;
this.userCode = `
const ivec3 pads = ivec3(${p}, ${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 < ${t10}; 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 = ${t10} - 1 - wF;
for (int wR = 0; wR < ${o}; 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 = ${o} - 1 - wR;
for (int wC = 0; wC < ${n}; 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 = ${n} - 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 p9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new Bh(m);
return t10.runWebGLProgram(d, [n, s], "float32");
}
var fA = { kernelName: $i, backendName: "webgl", kernelFunc: p9 };
var Uh = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "strides", type: "vec2" }], this.outputShape = e.inShape, this.enableShapeUniforms = ut(this.outputShape.length);
let t10 = e.filterHeight, o = e.filterWidth, n = t10 - 1 - e.padInfo.top, s = o - 1 - e.padInfo.left;
this.userCode = `
const ivec2 pads = ivec2(${n}, ${s});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
vec4 result = vec4(0.);
for (int wR = 0; wR < ${t10}; wR++) {
float dyR = float(dyRCorner + wR) / strides[0];
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t10} - 1 - wR;
for (int wC = 0; wC < ${o}; wC++) {
int wCPerm = ${o} - 1 - wC;
float dyC = float(dyCCorner + wC) / strides[1];
bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)
&& (fract(dyC) == 0.0);
int idyC = int(dyC);
float dyC2 = float(dyCCorner + wC + 1) / strides[1];
bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)
&& (fract(dyC2) == 0.0);
int idyC2 = int(dyC2);
if (idyCVal && idyCVal2) {
for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {
vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
vec4 dySample = getDy(batch, idyR, idyC, d2);
vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?
dySample : getDy(batch, idyR, idyC2, d2);
vec2 dyValue = mod(float(idyC), 2.) == 0. ?
dySample.xy : dySample.zw;
result.xy += vec2(dot(dyValue, wValue.xy),
dot(dyValue, wValue.zw));
dyValue = mod(float(idyC2), 2.) == 0. ?
dySample2.xy : dySample2.zw;
result.zw += vec2(dot(dyValue, wValue.xy),
dot(dyValue, wValue.zw));
}
} else if (idyCVal) {
for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {
vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
vec4 dySample = getDy(batch, idyR, idyC, d2);
vec2 dyValue = mod(float(idyC), 2.) == 0. ?
dySample.xy : dySample.zw;
result.xy += vec2(dot(dyValue, wValue.xy),
dot(dyValue, wValue.zw));
}
} else if (idyCVal2) {
for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {
vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
vec4 dySample = getDy(batch, idyR, idyC2, d2);
vec2 dyValue = mod(float(idyC2), 2.) == 0. ?
dySample.xy : dySample.zw;
result.zw += vec2(dot(dyValue, wValue.xy),
dot(dyValue, wValue.zw));
}
}
}
}
setOutput(result);
}
`;
}
};
function c9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = C.convertConv2DDataFormat(u), m = C.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l);
if (P().getBool("WEBGL_PACK") && l === "channelsLast") {
let d = [[m.strideHeight, m.strideWidth]], f = new Uh(m);
return t10.runWebGLProgram(f, [n, s], "float32", d);
} else {
let d = new zh(m);
return t10.runWebGLProgram(d, [n, s], "float32");
}
}
var hA = { kernelName: tn, backendName: "webgl", kernelFunc: c9 };
function l9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = C.computeConv3DInfo(n.shape, s.shape, a, p, i), c = new Fh(u);
return t10.runWebGLProgram(c, [n, s], "float32");
}
var gA = { kernelName: rn, backendName: "webgl", kernelFunc: l9 };
function m9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = C.computeConv3DInfo(n.shape, p, a, 1, i), c = new Vh(u);
return t10.runWebGLProgram(c, [n, s], "float32");
}
var xA = { kernelName: za, backendName: "webgl", kernelFunc: m9 };
function d9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o, u = C.computeConv3DInfo(p, s.shape, i, 1, a), c = new Wh(u);
return t10.runWebGLProgram(c, [n, s], "float32");
}
var yA = { kernelName: on, backendName: "webgl", kernelFunc: d9 };
var f9 = Do + `
return cos(x);
`;
var h9 = `
vec4 result = cos(x);
bvec4 isNaN = isnan(x);
${Kr}
return result;
`;
var g9 = ge({ opSnippet: f9, packedOpSnippet: h9 });
var bA = { kernelName: nn, backendName: "webgl", kernelFunc: g9 };
var x9 = `
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`;
var y9 = ge({ opSnippet: x9 });
var CA = { kernelName: sn, backendName: "webgl", kernelFunc: y9 };
var Gh = class {
constructor(e, t10, o, n, s) {
this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = [];
let [a, i, p, u] = e, [c] = t10, [l, m] = o;
this.outputShape = [c, l, m, u];
let d = n === "bilinear" ? 1 : 0, [f, h] = [`${i - 1}.0`, `${p - 1}.0`], [g, x, b] = l > 1 ? [`${(i - 1) / (l - 1)}`, "(y2-y1) * height_ratio", `y1*${f} + float(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${f}`], [w, S, k] = m > 1 ? [`${(p - 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 = ${S};
float in_y = ${b};
if( in_y < 0.0 || in_y > ${f} ) {
setOutput(float(${s}));
return;
}
float in_x = ${k};
if( in_x < 0.0 || in_x > ${h} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`;
}
};
var b9 = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new Gh(n.shape, s.shape, i, p, u);
return t10.runWebGLProgram(c, [n, s, a], "float32");
};
var wA = { kernelName: pn, backendName: "webgl", kernelFunc: b9 };
var Sp;
(function(r) {
r.Prod = "*", r.Sum = "+";
})(Sp || (Sp = {}));
var em = class {
constructor(e, t10, o, n) {
this.op = e, this.outputShape = t10, this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }];
let s = this.outputShape.length, a = this.op === Sp.Prod ? "1.0" : "0.0", i = o ? a : `getX(${SA(s, "coords", this.op)})`, p = this.outputShape[this.outputShape.length - 1], u = "", c = "";
o ? (u = n ? `end != ${p - 1}` : "end != 0", c = n ? "end + 1" : "end - 1") : (u = n ? `end + pow2 < ${p}` : "end >= pow2", c = n ? "end + pow2" : "end - pow2"), this.userCode = `
void main() {
${Re(s)} coords = getOutputCoords();
int end = ${IA(s, "coords", this.op)};
float val = ${i};
int pow2 = int(pow(2.0, index));
if (${u}) {
int idx = ${c};
${IA(s, "coords", this.op)} = idx;
val ${this.op}= getX(${SA(s, "coords", this.op)});
}
setOutput(val);
}
`;
}
};
function SA(r, e, t10) {
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 new Error(`Cumulative ${t10} for rank ${r} is not yet supported`);
}
function IA(r, e, t10) {
if (r === 1)
return `${e}`;
if (r === 2)
return `${e}.y`;
if (r === 3)
return `${e}.z`;
if (r === 4)
return `${e}.w`;
throw new Error(`Cumulative ${t10} for rank ${r} is not yet supported`);
}
function Hh(r, e, t10, o, n, s) {
let a = e.shape.length, i = C.getAxesPermutation([o], a), p = e;
i != null && (p = bt({ inputs: { x: e }, backend: t10, attrs: { perm: i } }));
let u = C.getInnerMostAxes(1, a)[0];
if (u !== a - 1)
throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`);
let c = p.shape[u], l = Dt({ inputs: { x: p }, backend: t10 });
for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) {
let d = new em(r, p.shape, false, s), f = [[m]], h = l;
l = t10.runWebGLProgram(d, [l], l.dtype, f), t10.disposeIntermediateTensorInfo(h);
}
if (n) {
let m = new em(r, p.shape, n, s), d = l;
l = t10.runWebGLProgram(m, [l], l.dtype), t10.disposeIntermediateTensorInfo(d);
}
if (i != null) {
let m = C.getUndoAxesPermutation(i), d = bt({ inputs: { x: l }, backend: t10, attrs: { perm: m } });
return t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(p), d;
}
return l;
}
function C9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
return Hh(Sp.Prod, n, t10, s, a, i);
}
var vA = { kernelName: an, backendName: "webgl", kernelFunc: C9 };
function w9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
return Hh(Sp.Sum, n, t10, s, a, i);
}
var kA = { kernelName: un, backendName: "webgl", kernelFunc: w9 };
function S9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o;
if (n.shape.length === 1) {
let p = t10.readSync(n.dataId), u = t10.readSync(s.dataId), c = ah(p, u, s.dtype, s.shape, a);
return t10.makeTensorInfo([a], s.dtype, c);
} else if (n.shape.length === 2) {
let p = t10.bufferSync(n), u = t10.bufferSync(s), c = lR(p, u, a, i);
return t10.makeTensorInfo(c.shape, s.dtype, c.values);
}
throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`);
}
var NA = { kernelName: Qs, backendName: "webgl", kernelFunc: S9 };
var Kh = class {
constructor(e, t10, o) {
this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t10, this.dataFormat = o, 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 / ${t10};
int offset_h = imod(h, ${t10});
int in_w = w / ${t10};
int offset_w = imod(w, ${t10});
int offset_d = (offset_h * ${t10} + 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 I9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = new Kh(f, s, a);
return t10.runWebGLProgram(h, [n], n.dtype);
}
var TA = { kernelName: cn, backendName: "webgl", kernelFunc: I9 };
var Wc = class {
constructor(e, t10 = false, o = null, n = false, s = false) {
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 = ut(this.outputShape.length);
let a = e.filterHeight, i = e.filterWidth, p = e.outChannels / e.inChannels, u = "", c = "";
o && (n ? u = `float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${o}
}` : s ? u = `float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${o}
}` : u = `
float activation(float x) {
${o}
}
`, c = "result = activation(result);");
let l = t10 ? "result += getBiasAtOutCoords();" : "";
t10 && this.variableNames.push("bias"), n && 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 / ${p};
int q = d2 - d1 * ${p};
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;
${l}
${c}
setOutput(result);
}
`;
}
};
var Uc = class {
constructor(e, t10 = false, o = null, n = false, s = false) {
this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = ut(this.outputShape.length);
let a = e.outChannels / e.inChannels, i = e.padInfo.left, p = e.strideWidth, u = e.dilationWidth, c = e.filterHeight, l = e.filterWidth, m = l, d = `
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;
for (let x = 0; x < l; x++)
d += `
vec4 xTexelC${x * 2};
int xTexelC${x * 2}Ready;
vec4 xTexelC${x * 2 + 1};
int xTexelC${x * 2 + 1}Ready;
vec4 xC${x};`;
d += `
for (int r = 0; r < ${c}; r++) {
`;
for (let x = 0; x < l; x++)
d += `
xTexelC${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);`;
d += `
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;
for (let x = 0; x < (m + 1) / 2; x++) {
let b = x * 2;
if (d += `
xC = xCCorner + ${b * u};
`, p === 1) {
if (b < l && (i % 2 === 1 ? (d += `
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
`, u === 1 && b > 0 ? d += `
xC${b} = vec4(xTexelC${b - 2}.zw, xTexelC${b}.xy);
` : d += `
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${b} = vec4(previous.zw, xTexelC${b}.xy);
} else {
xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);
}
`) : d += `
if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
xC${b} = xTexelC${b};
`, b + 1 < l)) {
let w = i % 2 === 0 ? y.nearestLargerEven(u) : u;
u % 2 === 0 && i % 2 === 1 || u % 2 !== 0 && i % 2 !== 1 ? (d += `
xCOffset = xC + imod(pads[1], 2) + ${w};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b + 1}Ready == 0) {
xTexelC${b + 1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b + 1}.zw = vec2(0.0);
}
xTexelC${b + 1}Ready = 1;
}
`, u > 1 ? d += `
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
xC${b + 1} = vec4(previous.zw, xTexelC${b + 1}.xy);
} else {
xC${b + 1} = vec4(0.0, 0.0, xTexelC${b + 1}.xy);
}
` : d += `
xC${b + 1} = vec4(xTexelC${b}.zw, xTexelC${b + 1}.xy);
`) : w === 1 ? d += `
xC${b + 1} = xTexelC${b};
` : d += `
xCOffset = xC + ${w};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b + 1}Ready == 0) {
xTexelC${b + 1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b + 1}.zw = vec2(0.0);
}
xTexelC${b + 1}Ready = 1;
}
xC${b + 1} = xTexelC${b + 1};
`;
}
} else
b < l && (i % 2 === 1 ? (d += `
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b + 1}Ready == 0) {
xTexelC${b + 1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${b + 1}.zw = vec2(0.0);
}
xTexelC${b + 1}Ready = 1;
}
xC${b} = vec4(xTexelC${b}.zw, xTexelC${b + 1}.zw);
`, b + 1 < l && (d += `
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${b + 1} = vec4(xTexelC${b + 1}.xy, final.xy);
`)) : (d += `
if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b + 1}Ready == 0) {
xTexelC${b + 1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b + 1}.zw = vec2(0.);
}
xTexelC${b + 1}Ready = 1;
}
xC${b} = vec4(
xTexelC${b}.xy, xTexelC${b + 1}.xy);
`, b + 1 < l && (d += `
xC${b + 1} = vec4(xTexelC${b}.zw, xTexelC${b + 1}.zw);
`)));
b < l && (d += `
wTexel = getW(r, ${b}, d1, q);
dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
`, b + 1 < l && (d += `
wTexel = getW(r, ${b + 1}, d1, q);
dotProd += xC${b + 1} * vec4(wTexel.xz, wTexel.xz);
`));
}
d += `
}
`, d += `
}
`;
let f = "", h = "";
o && (n ? f = `vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}` : s ? f = `vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}` : f = `vec4 activation(vec4 x) {
${o}
}`, h = "result = activation(result);");
let g = t10 ? "result += getBiasAtOutCoords();" : "";
t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = `
${f}
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);
${d}
vec4 result = dotProd - vec4(0.000000000000001);
${g}
${h}
setOutput(result);
}
`;
}
};
function v9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o, c = p;
c == null && (c = [1, 1]), y.assert(C.eitherStridesOrDilationsAreOne(a, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);
let l = C.computeConv2DInfo(n.shape, s.shape, a, c, i, u, true), m;
P().getBool("WEBGL_PACK_DEPTHWISECONV") && l.strideWidth <= 2 && l.outChannels / l.inChannels === 1 ? m = new Uc(l) : m = new Wc(l);
let d = [[l.padInfo.top, l.padInfo.left], [l.strideHeight, l.strideWidth], [l.dilationHeight, l.dilationWidth], [l.inHeight, l.inWidth]];
return t10.runWebGLProgram(m, [n, s], "float32", d);
}
var _A = { kernelName: ln, backendName: "webgl", kernelFunc: v9 };
var qh = class {
constructor(e) {
this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
let t10 = e.strideHeight, o = e.strideWidth, n = 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 * ${t10} - ${n};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`;
}
};
var jh = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = t10 - 1 - e.padInfo.top, i = o - 1 - e.padInfo.left, p = e.outChannels / e.inChannels;
this.userCode = `
const ivec2 pads = ivec2(${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 < ${t10}; wR++) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t10} - 1 - wR;
for (int wC = 0; wC < ${o}; 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 = ${o} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${p}; dm++) {
int d2 = d1 * ${p} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`;
}
};
function k9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = C.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new qh(l);
return t10.runWebGLProgram(m, [n, s], "float32");
}
var $A = { kernelName: Ei, backendName: "webgl", kernelFunc: k9 };
function N9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = C.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new jh(l);
return t10.runWebGLProgram(m, [n, s], "float32");
}
var EA = { kernelName: Ri, backendName: "webgl", kernelFunc: N9 };
var Xh = class {
constructor(e) {
this.variableNames = ["X"], this.outputShape = [e, e], this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`;
}
};
function T9(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = te({ inputs: { x: o }, backend: t10, attrs: { shape: [s] } }), i = new Xh(s), p = t10.runWebGLProgram(i, [a], a.dtype), u = te({ inputs: { x: p }, backend: t10, attrs: { shape: n } });
return t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(p), u;
}
var RA = { kernelName: Zs, backendName: "webgl", kernelFunc: T9 };
var Yh = class {
constructor(e) {
this.variableNames = ["x", "W"], this.outputShape = e.outShape;
let { inHeight: t10, inWidth: o, padInfo: n, strideHeight: s, strideWidth: a, filterHeight: i, filterWidth: p, dilationHeight: u, dilationWidth: c } = e, { top: l, left: m } = n;
this.userCode = `
const ivec2 strides = ivec2(${s}, ${a});
const ivec2 pads = ivec2(${l}, ${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 < ${t10}) {
for (int w = 0; w < ${p}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${o}) {
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 _9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = C.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c, l = new Yh(u);
c = t10.runWebGLProgram(l, [n, s], "float32");
let m = te({ inputs: { x: c }, backend: t10, attrs: { shape: u.outShape } });
return t10.disposeIntermediateTensorInfo(c), m;
}
var DA = { kernelName: mn, backendName: "webgl", kernelFunc: _9 };
function $9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = C.decodeEinsumEquation(n, s.length);
C.checkEinsumDimSizes(a.length, p, s);
let { path: u, steps: c } = C.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = [];
for (let h = 0; h < l; ++h) {
for (let g of c[h]) {
let { permutationIndices: x, expandDims: b } = C.getEinsumPermutation(d, p[g]), w;
C.isIdentityPermutation(x) ? w = s[g] : (w = bt({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(w));
let S = w.shape.slice();
for (let k = 0; k < b.length; ++k)
S.splice(b[k], 0, 1);
y.arraysEqual(w.shape, S) || (w = te({ inputs: { x: w }, backend: t10, attrs: { shape: S } }), f.push(w)), m === null ? m = w : (m = Zl({ inputs: { a: w, b: m }, backend: t10 }), f.push(m));
}
h < l - 1 && (u[h] >= 0 && (m = bp({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--);
}
for (let h of f)
h !== m && t10.disposeIntermediateTensorInfo(h);
return m;
}
var AA = { kernelName: Fi, backendName: "webgl", kernelFunc: $9 };
var E9 = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var R9 = `
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;
`;
var D9 = ge({ opSnippet: E9, packedOpSnippet: R9 });
var FA = { kernelName: fn, backendName: "webgl", kernelFunc: D9 };
var A9 = "return (b >= 0.0) ? a : a * (b + 1.0);";
var F9 = `
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
var P9 = (r) => {
let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e, s = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ro(F9, o.shape, n.shape) : new lo(A9, o.shape, n.shape);
return t10.runWebGLProgram(s, [o, n], o.dtype);
};
var PA = { kernelName: Va, backendName: "webgl", kernelFunc: P9 };
var O9 = `
return vec4(equal(a, b));
`;
var M9 = "return float(a == b);";
var L9 = nt({ opSnippet: M9, packedOpSnippet: O9, dtype: "bool", cpuKernelImpl: hR });
var OA = { kernelName: hn, backendName: "webgl", kernelFunc: L9 };
var B9 = `
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${C.ERF_P};
float a1 = ${C.ERF_A1};
float a2 = ${C.ERF_A2};
float a3 = ${C.ERF_A3};
float a4 = ${C.ERF_A4};
float a5 = ${C.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`;
var z9 = ge({ opSnippet: B9 });
var MA = { kernelName: Wa, backendName: "webgl", kernelFunc: z9 };
var V9 = Do + `
return exp(x);
`;
var W9 = `
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
var av = ge({ opSnippet: V9, packedOpSnippet: W9, cpuKernelImpl: gR, dtype: "float32" });
var LA = { kernelName: gn, backendName: "webgl", kernelFunc: av };
function Qh(r) {
let { inputs: e, attrs: t10, backend: o } = r, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n;
return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), te({ inputs: { x: s }, backend: o, attrs: { shape: i } });
}
var BA = { kernelName: Js, backendName: "webgl", kernelFunc: Qh };
var zA = "return exp(x) - 1.0;";
var U9 = ge({ opSnippet: zA, packedOpSnippet: zA, cpuKernelImpl: xR });
var VA = { kernelName: xn, backendName: "webgl", kernelFunc: U9 };
var tm = class {
constructor(e, t10, o) {
this.variableNames = ["real", "imag"];
let n = t10[1];
this.outputShape = t10;
let s = o ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`, a = o ? `${n}.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) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${n});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${n}; 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 Zh(r, e, t10) {
let o = t10.texData.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = te({ inputs: { x: r }, backend: t10, attrs: { shape: [a, s] } }), p = i.shape, u = new tm("real", p, e), c = new tm("imag", p, e), l = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: p }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: p }], m = t10.runWebGLProgram(u, l, "float32"), d = t10.runWebGLProgram(c, l, "float32"), f = Fr({ inputs: { real: m, imag: d }, backend: t10 });
t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d);
let h = te({ inputs: { x: f }, backend: t10, attrs: { shape: r.shape } });
return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(f), h;
}
function G9(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e;
return Zh(o, false, t10);
}
var WA = { kernelName: Pi, backendName: "webgl", kernelFunc: G9 };
var Jh = class {
constructor(e, t10) {
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 xi(r) {
let { backend: e, attrs: t10 } = r, { shape: o, value: n } = t10, { dtype: s } = t10;
if (s = s || y.inferDtype(n), s === "string") {
let a = y.getArrayFromDType(s, y.sizeFromShape(o));
return a.fill(n), e.makeTensorInfo(o, s, a);
} else {
let a = new Jh(o, n), i = [[n]];
return e.runWebGLProgram(a, [], s, i);
}
}
var UA = { kernelName: ea, backendName: "webgl", kernelFunc: xi };
var eg = class {
constructor(e) {
this.variableNames = ["Image"], this.outputShape = [];
let t10 = e[2];
this.outputShape = e, this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t10} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t10}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`;
}
};
var GA = { kernelName: yn, backendName: "webgl", kernelFunc: ({ inputs: r, backend: e }) => {
let { image: t10 } = r, o = e, n = new eg(t10.shape);
return o.runWebGLProgram(n, [t10], t10.dtype);
} };
var HA = "return floor(x);";
var H9 = ge({ opSnippet: HA, packedOpSnippet: HA, cpuKernelImpl: yR });
var KA = { kernelName: bn, backendName: "webgl", kernelFunc: H9 };
var K9 = `
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`;
var q9 = `
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`;
var j9 = nt({ opSnippet: K9, packedOpSnippet: q9, dtype: "int32" });
var qA = { kernelName: Cn, backendName: "webgl", kernelFunc: j9 };
var tg = class {
constructor(e) {
this.variableNames = ["A"];
let t10 = It(), [o, n] = e;
this.outputShape = e, this.userCode = `
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);
vec4 values = ${t10.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`;
}
};
var rg = class {
constructor(e) {
this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true;
let t10 = It(), [o, n] = e;
this.outputShape = e, this.userCode = `
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}.0, ${o}.0);
vec4 values = ${t10.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);
}
}
${t10.output} = result;
}
`;
}
};
var jA = { kernelName: $u, backendName: "webgl", kernelFunc: X9 };
var Gc;
var iv = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
function X9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o, a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, [p, u] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], c = [u, p], l = [u, p, s];
if (i || a) {
let h = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
(Gc == null || h !== iv) && (iv = h, Gc = document.createElement("canvas").getContext("2d", { willReadFrequently: iv })), Gc.canvas.width = p, Gc.canvas.height = u, Gc.drawImage(n, 0, 0, p, u), n = Gc.canvas;
}
let m = t10.makeTensorInfo(c, "int32");
t10.texData.get(m.dataId).usage = mr.PIXELS, t10.gpgpu.uploadPixelDataToTexture(t10.getTexture(m.dataId), n);
let d = P().getBool("WEBGL_PACK") ? new rg(l) : new tg(l), f = t10.runWebGLProgram(d, [m], "int32");
return t10.disposeData(m.dataId), f;
}
function Y9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h), x, b = [], w = a != null, S = i != null, k = d === "leakyrelu", _ = () => {
let R = [n, s], D = (F, O) => {
if (O === "NCHW" && F.shape.length === 1 && F.shape[0] !== 1) {
let M = te({ inputs: { x: F }, backend: t10, attrs: { shape: [F.shape[0], 1, 1] } });
return b.push(M), M;
}
return F;
};
if (w && R.push(D(a, c)), S && R.push(D(i, c)), k) {
let F = t10.makeTensorInfo([], "float32", y.createScalarValue(f, "float32"));
R.push(F), b.push(F);
}
return R;
};
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 = Mh({ x: n, filter: s, convInfo: g, backend: t10, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f });
else if (g.strideWidth <= 2 && h === "channelsLast" && P().getBool("WEBGL_EXP_CONV")) {
let R = d ? hi(d, true) : null, D = new Vc(g, w, R, S, k), F = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], O = _();
x = t10.runWebGLProgram(D, O, "float32", F);
} else if (P().getBool("WEBGL_CONV_IM2COL"))
x = Lh({ x: n, filter: s, convInfo: g, backend: t10, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f });
else {
let R = d ? hi(d, false) : null, D = new zc(g, w, R, S, k), F = _();
x = t10.runWebGLProgram(D, F, "float32");
}
let E = te({ inputs: { x }, backend: t10, attrs: { shape: g.outShape } });
return b.push(x), b.forEach((R) => t10.disposeIntermediateTensorInfo(R)), E;
}
var XA = { kernelName: Co, backendName: "webgl", kernelFunc: Y9 };
function Q9(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = [], h = c;
h == null && (h = [1, 1]), y.assert(C.eitherStridesOrDilationsAreOne(p, h), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`);
let g = C.computeConv2DInfo(n.shape, s.shape, p, h, u, l, true), x = P().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, b = m ? hi(m, x) : null, w = [n, s], S = a != null, k = i != null, _ = m === "leakyrelu";
if (S && w.push(a), k && w.push(i), _) {
let F = t10.makeTensorInfo([], "float32", y.createScalarValue(d, "float32"));
w.push(F), f.push(F);
}
let E;
x ? E = new Uc(g, S, b, k, _) : E = new Wc(g, S, b, k, _);
let R = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], D = t10.runWebGLProgram(E, w, "float32", R);
return f.forEach((F) => t10.disposeIntermediateTensorInfo(F)), D;
}
var YA = { kernelName: wo, backendName: "webgl", kernelFunc: Q9 };
var og = class {
constructor(e, t10, o, n) {
this.sliceDim = e, this.strides = t10, this.paramsShape = n, this.variableNames = ["x", "indices"], this.outputShape = o;
let s = Re(o.length), a = `
int index;`;
for (let i = 0; i < this.sliceDim; i++)
a += `
index = round(getIndices(coords[0], ${i}));
out_of_bounds = out_of_bounds || index < 0;
out_of_bounds = out_of_bounds || index >= ${this.paramsShape[i]};
flattenIndex += index * ${this.strides[i]};`;
this.userCode = `
void main() {
${s} coords = getOutputCoords();
int flattenIndex = 0;
bool out_of_bounds = false;
${a}
setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
}
`;
}
};
function Z9(r) {
let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = C.prepareAndValidate(o, n), m = te({ inputs: { x: n }, backend: t10, attrs: { shape: [u, a] } }), d = te({ inputs: { x: o }, backend: t10, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } });
if (t10.shouldExecuteOnCPU([o, n]) || o.dtype === "string") {
let x = t10.readSync(n.dataId), b = t10.bufferSync(o), w = bR(x, b, o.dtype, u, a, c, l, o.shape, i);
return t10.makeTensorInfo(p, o.dtype, w.values);
}
let f = new og(a, l, [u, c], o.shape), h = t10.runWebGLProgram(f, [d, m], d.dtype), g = te({ inputs: { x: h }, backend: t10, attrs: { shape: p } });
return t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(h), g;
}
var QA = { kernelName: Sn, backendName: "webgl", kernelFunc: Z9 };
var ng = class {
constructor(e, t10) {
this.variableNames = ["A", "indices"], this.outputShape = t10, this.rank = t10.length;
let o = Re(this.rank), n = J9(e, 2);
this.userCode = `
void main() {
${o} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${n}));
}
`;
}
};
function J9(r, e) {
let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = [];
for (let n = 0; n < r.length; n++)
n === 2 ? o.push("index") : o.push(`${t10[n]}`);
return o.join();
}
function uv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0];
if (P().get("DEBUG")) {
let b = t10.readSync(s.dataId), w = n.shape[p];
for (let S = 0; S < b.length; ++S) {
let k = b[S];
y.assert(k <= w - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${w - 1}]`);
}
}
let u = C.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = te({ inputs: { x: n }, backend: t10, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = te({ inputs: { x: s }, backend: t10, attrs: { shape: [u.batchSize, c / u.batchSize] } });
l.push(m), l.push(d);
let f = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize];
if (t10.shouldExecuteOnCPU([n, s]) || n.dtype === "string") {
let b = t10.bufferSync(d), w = t10.bufferSync(m), S = CR(w, b, f);
return l.forEach((k) => t10.disposeIntermediateTensorInfo(k)), t10.makeTensorInfo(u.outputShape, S.dtype, S.values);
}
let h = new ng(m.shape, f), g = t10.runWebGLProgram(h, [m, d], m.dtype);
l.push(g);
let x = te({ inputs: { x: g }, backend: t10, attrs: { shape: u.outputShape } });
return l.forEach((b) => t10.disposeIntermediateTensorInfo(b)), x;
}
var ZA = { kernelName: ta, backendName: "webgl", kernelFunc: uv };
var eJ = "return float(a > b);";
var tJ = `
return vec4(greaterThan(a, b));
`;
var rJ = nt({ opSnippet: eJ, packedOpSnippet: tJ, cpuKernelImpl: wR, dtype: "bool" });
var JA = { kernelName: In, backendName: "webgl", kernelFunc: rJ };
var oJ = "return float(a >= b);";
var nJ = `
return vec4(greaterThanEqual(a, b));
`;
var sJ = nt({ opSnippet: oJ, packedOpSnippet: nJ, dtype: "bool", cpuKernelImpl: SR });
var eF = { kernelName: vn, backendName: "webgl", kernelFunc: sJ };
function aJ(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e;
return Zh(o, true, t10);
}
var tF = { kernelName: Oi, backendName: "webgl", kernelFunc: aJ };
var iJ = "return float(!isnan(x) && !isinf(x));";
var uJ = ge({ opSnippet: iJ, dtype: "bool" });
var rF = { kernelName: kn, backendName: "webgl", kernelFunc: uJ };
var pJ = "return float(isinf(x));";
var cJ = ge({ opSnippet: pJ, dtype: "bool" });
var oF = { kernelName: Nn, backendName: "webgl", kernelFunc: cJ };
var lJ = "return float(isnan(x));";
var mJ = ge({ opSnippet: lJ, dtype: "bool" });
var nF = { kernelName: Tn, backendName: "webgl", kernelFunc: mJ };
var dJ = "return float(a < b);";
var fJ = `
return vec4(lessThan(a, b));
`;
var hJ = nt({ opSnippet: dJ, packedOpSnippet: fJ, cpuKernelImpl: IR, dtype: "bool" });
var sF = { kernelName: $n, backendName: "webgl", kernelFunc: hJ };
var gJ = "return float(a <= b);";
var xJ = `
return vec4(lessThanEqual(a, b));
`;
var yJ = nt({ opSnippet: gJ, packedOpSnippet: xJ, cpuKernelImpl: vR, dtype: "bool" });
var aF = { kernelName: En, backendName: "webgl", kernelFunc: yJ };
function bJ(r) {
let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = kR(o, n, s);
return e.makeTensorInfo([a.length], "float32", a);
}
var iF = { kernelName: Rn, backendName: "webgl", kernelFunc: bJ };
var CJ = Do + `
return x < 0.0 ? 0./0. : log(x);
`;
var wJ = `
vec4 result = log(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
return result;
`;
var SJ = ge({ opSnippet: CJ, packedOpSnippet: wJ, cpuKernelImpl: NR });
var uF = { kernelName: Dn, backendName: "webgl", kernelFunc: SJ };
var IJ = Do + `
return log(1.0 + x);
`;
var vJ = ge({ opSnippet: IJ });
var pF = { kernelName: An, backendName: "webgl", kernelFunc: vJ };
var kJ = "return float(a >= 1.0 && b >= 1.0);";
var NJ = `
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`;
var TJ = nt({ opSnippet: kJ, packedOpSnippet: NJ, dtype: "bool" });
var cF = { kernelName: Fn, backendName: "webgl", kernelFunc: TJ };
var _J = "return float(!(x >= 1.0));";
var $J = ge({ opSnippet: _J });
var lF = { kernelName: Pn, backendName: "webgl", kernelFunc: $J };
var EJ = "return float(a >= 1.0 || b >= 1.0);";
var RJ = `
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`;
var DJ = nt({ opSnippet: EJ, packedOpSnippet: RJ, dtype: "bool" });
var mF = { kernelName: On, backendName: "webgl", kernelFunc: DJ };
var sg = class {
constructor(e, t10, o, n, s) {
this.variableNames = ["x"], this.outputShape = [];
let a = t10, i = e[3] - 1;
this.outputShape = e;
let p, u = `float(${o}) + float(${n}) * sum`;
s === 0.5 ? p = `inversesqrt(${u})` : s === 1 ? p = `1.0/(${u})` : p = `exp(log(${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 * ${p};
setOutput(val);
}
`;
}
};
var ag = class {
constructor(e, t10, o, n, s) {
this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true;
let a = t10, i = e[3] - 1;
this.outputShape = e;
let p, u = `float(${o}) + float(${n}) * sum`;
s === 0.5 ? p = `inversesqrt(${u})` : s === 1 ? p = `1.0/(${u})` : p = `exp(log(${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 * ${p};
setOutput(result);
}
`;
}
};
var AJ = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = P().getBool("WEBGL_PACK_NORMALIZATION") ? new ag(n.shape, s, a, i, p) : new sg(n.shape, s, a, i, p);
return t10.runWebGLProgram(u, [n], n.dtype);
};
var dF = { kernelName: Mn, backendName: "webgl", kernelFunc: AJ };
var ig = class {
constructor(e, t10, o, n, s) {
this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t10, this.bias = o, this.alpha = n, 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 - ${t10})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t10} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${n}) * norm + float(${o});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${n})
* 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 FJ = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new ig(n.shape, i, p, u, c);
return t10.runWebGLProgram(l, [n, s, a], n.dtype);
};
var fF = { kernelName: Ua, backendName: "webgl", kernelFunc: FJ };
function hF(r, e, t10, o) {
let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = qr(i, r.dtype, "max", o), u = te({ inputs: { x: p }, attrs: { shape: t10 }, backend: o });
return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u;
}
function pv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = C.getAxesPermutation(u, i), l = c != null, m = t10.shouldExecuteOnCPU([n]), d = n;
if (l) {
if (m) {
let w = t10.texData.get(d.dataId).values, S = new Array(i);
for (let E = 0; E < S.length; E++)
S[E] = n.shape[c[E]];
let k = yp(w, n.shape, n.dtype, c, S);
d = t10.makeTensorInfo(S, n.dtype);
let _ = t10.texData.get(d.dataId);
_.values = k;
} else
d = gu(n, c, t10);
u = C.getInnerMostAxes(u.length, i);
}
C.assertAxesAreInnerMostDims("max", u, i);
let [f, h] = C.computeOutAndReduceShapes(d.shape, u), g = f;
a && (g = C.expandShapeToKeepDim(f, p));
let x;
if (m) {
let w = t10.texData.get(d.dataId).values, S = TR(w, y.sizeFromShape(h), g, n.dtype);
x = t10.makeTensorInfo(g, n.dtype);
let k = t10.texData.get(x.dataId);
k.values = S;
} else
x = hF(d, h, g, t10);
return l && t10.disposeIntermediateTensorInfo(d), x;
}
var gF = { kernelName: Ln, backendName: "webgl", kernelFunc: pv };
var PJ = Mc + `
return max(a, b);
`;
var OJ = `
vec4 result = vec4(max(a, b));
bvec4 isNaNA = isnan(a);
bvec4 isNaNB = isnan(b);
bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
` + Kr + `
return result;
`;
var MJ = nt({ opSnippet: PJ, packedOpSnippet: OJ, cpuKernelImpl: _R });
var xF = { kernelName: Bn, backendName: "webgl", kernelFunc: MJ };
function LJ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
Ps(n, "maxPool");
let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
y.assert(C.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
let c = C.computePool2DInfo(n.shape, s, a, u, i, p);
if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape))
return Dt({ inputs: { x: n }, backend: t10 });
let l = new Ms(c, "max", false);
return t10.runWebGLProgram(l, [n], n.dtype);
}
var yF = { kernelName: zn, backendName: "webgl", kernelFunc: LJ };
function BJ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = C.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new xu(l, "max", false);
return t10.runWebGLProgram(m, [n], n.dtype);
}
var bF = { kernelName: ra, backendName: "webgl", kernelFunc: BJ };
var ug = class {
constructor(e) {
this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
let t10 = e.strideHeight, o = e.strideWidth, n = e.dilationHeight, s = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = s - 1 - e.padInfo.top, p = a - 1 - e.padInfo.left, u = s * a - 1;
this.userCode = `
const ivec2 pads = ivec2(${i}, ${p});
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 += ${n}) {
float dyR = float(dyRCorner + wR) / ${t10}.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) / ${o}.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 * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`;
}
};
var pg = class {
constructor(e) {
this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
let t10 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterDepth, u = e.effectiveFilterHeight, c = e.effectiveFilterWidth, l = p - 1 - e.padInfo.front, m = u - 1 - e.padInfo.top, d = c - 1 - e.padInfo.left, f = p * u * c - 1;
this.userCode = `
const ivec3 pads = ivec3(${l}, ${m}, ${d});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${p};
wD += ${s}) {
float dyD = float(dyDCorner + wD) / ${t10}.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) / ${o}.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) / ${n}.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 = ${f} -
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 zJ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = C.computePool3DInfo(a.shape, i, p, l, u, c), d = new xu(m, "max", true), f = t10.runWebGLProgram(d, [a], a.dtype), h = new pg(m), g = t10.runWebGLProgram(h, [n, f], a.dtype);
return t10.disposeIntermediateTensorInfo(f), g;
}
var CF = { kernelName: Li, backendName: "webgl", kernelFunc: zJ };
function VJ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s;
Ps([s, a], "maxPoolGrad");
let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = C.computePool2DInfo(i.shape, p, u, 1, c, l), d = true, f = new Ms(m, "max", d), h = t10.runWebGLProgram(f, [i], i.dtype), g = new ug(m), x = t10.runWebGLProgram(g, [n, h], i.dtype);
return t10.disposeIntermediateTensorInfo(h), x;
}
var wF = { kernelName: Hp, backendName: "webgl", kernelFunc: VJ };
function SF(r, e, t10, o) {
let n = new Ms(t10, "max", false), s = o.runWebGLProgram(n, [r], "float32");
n = new Ms(t10, "max", true, true, e);
let a = o.runWebGLProgram(n, [r], "float32");
return [s, a];
}
var IF = { kernelName: Bi, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t10;
y.assert(o.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);
let u = [1, 1];
y.assert(C.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);
let c = C.computePool2DInfo(o.shape, n, s, u, a), [l, m] = SF(o, i, c, p);
return [l, m];
} };
function vF(r, e, t10, o) {
let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = te({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = qr(i, "float32", "mean", o), u = te({ inputs: { x: p }, attrs: { shape: t10 }, backend: o });
return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u;
}
var kF = { kernelName: Vn, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
let { x: o } = r, { keepDims: n, axis: s } = e, a = t10, i = o.shape.length, p = y.parseAxisParam(s, o.shape), u = p, c = C.getAxesPermutation(u, i), l = c != null, m = a.shouldExecuteOnCPU([o]), d = [], f = o;
if (l) {
if (m) {
let S = a.texData.get(f.dataId).values, k = new Array(i);
for (let R = 0; R < k.length; R++)
k[R] = o.shape[c[R]];
let _ = yp(S, o.shape, o.dtype, c, k);
f = a.makeTensorInfo(k, o.dtype);
let E = a.texData.get(f.dataId);
E.values = _;
} else
f = gu(o, c, a);
d.push(f), u = C.getInnerMostAxes(u.length, i);
}
C.assertAxesAreInnerMostDims("sum", u, i);
let [h, g] = C.computeOutAndReduceShapes(f.shape, u), x = h;
n && (x = C.expandShapeToKeepDim(h, p));
let b = vF(f, g, x, a);
for (let w of d)
a.disposeIntermediateTensorInfo(w);
return b;
} };
function WJ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = C.getAxesPermutation(u, i), l = n;
c != null && (l = bt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = C.getInnerMostAxes(u.length, n.shape.length)), C.assertAxesAreInnerMostDims("min", u, i);
let [m, d] = C.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = te({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = qr(h, h.dtype, "min", t10), x;
if (a) {
let b = C.expandShapeToKeepDim(m, p);
x = te({ inputs: { x: g }, backend: t10, attrs: { shape: b } });
} else
x = te({ inputs: { x: g }, backend: t10, attrs: { shape: m } });
return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x;
}
var NF = { kernelName: Wn, backendName: "webgl", kernelFunc: WJ };
var UJ = Mc + `
return min(a, b);
`;
var GJ = `
vec4 result = vec4(min(a, b));
bvec4 isNaNA = isnan(a);
bvec4 isNaNB = isnan(b);
bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
` + Kr + `
return result;
`;
var HJ = nt({ opSnippet: UJ, packedOpSnippet: GJ, cpuKernelImpl: $R });
var TF = { kernelName: Un, backendName: "webgl", kernelFunc: HJ };
var cg = class {
constructor(e, t10, o) {
this.variableNames = ["x"], this.outputShape = t10.map((c, l) => c[0] + e[l] + c[1]);
let n = e.length, s = Re(n), a = t10.map((c) => c[0]).join(","), i = t10.map((c, l) => c[0] + e[l]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n), u = o === "reflect" ? 0 : 1;
if (n === 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 < ${n}; 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};
}
}
${s} coords = outC - start;
setOutput(getX(${p}));
}
`;
}
};
var lg = class {
constructor(e, t10, o) {
this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t10.map((f, h) => f[0] + e[h] + f[1]);
let n = e.length, s = Re(n), a = t10.map((f) => f[0]).join(","), i = t10.map((f, h) => f[0] + e[h]).join(","), p = Rt("rc", n), u = Rt("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = o === "reflect" ? 0 : 1, d = "";
if (n === 1) {
let f = `
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${m};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${m};
}
source -= start;
`;
d = `
${s} rc = outputLoc;
${f}
result[0] = getChannel(getX(${u.join()}), ${l});
${p[n - 1]} += 1;
if(${c}) {
${f}
result[1] = getChannel(getX(${u.join()}), ${l});
}
`;
} else {
let f = `
${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;
`;
d = `
${s} rc = outputLoc;
${f}
result[0] = getChannel(getX(${u.join()}), ${l});
${p[n - 1]} += 1;
if(${c}) {
${f}
result[1] = getChannel(getX(${u.join()}), ${l});
}
rc = outputLoc;
${p[n - 2]} += 1;
if(${p[n - 2]} < ${this.outputShape[n - 2]}) {
${f}
result[2] = getChannel(getX(${u.join()}), ${l});
${p[n - 1]} += 1;
if(${c}) {
${f}
result[3] = getChannel(getX(${u.join()}), ${l});
}
}
`;
}
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 KJ = ({ inputs: r, backend: e, attrs: t10 }) => {
let { x: o } = r, { paddings: n, mode: s } = t10, a = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new lg(o.shape, n, s) : new cg(o.shape, n, s);
return e.runWebGLProgram(a, [o], o.dtype);
};
var _F = { kernelName: Gn, backendName: "webgl", kernelFunc: KJ };
var qJ = `if (b == 0.0) return NAN;
return mod(a, b);`;
var jJ = `
vec4 result = mod(a, b);
bvec4 isNaN = equal(b, vec4(0.0));
` + Kr + `
return result;
`;
var XJ = nt({ opSnippet: qJ, packedOpSnippet: jJ });
var $F = { kernelName: Ga, backendName: "webgl", kernelFunc: XJ };
var mg = class {
constructor(e, t10, o) {
this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [e, o], this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t10 - 1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t10 - 1}));
}
`;
}
};
var YJ = `
if (a == b) {
return 1.0;
};
return a / b;`;
var QJ = `
// 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;
`;
var cv = nt({ opSnippet: YJ, packedOpSnippet: QJ, checkOutOfBounds: true });
var EF = { kernelName: dn, backendName: "webgl", kernelFunc: cv };
var RF = "return a - b;";
var lv = nt({ opSnippet: RF, packedOpSnippet: RF, supportsComplex: true, cpuKernelImpl: YR });
var DF = { kernelName: Is, backendName: "webgl", kernelFunc: lv };
function mv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = pv({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: a, keepDims: false } }), p = C.expandShapeToKeepDim(i.shape, a), u = te({ inputs: { x: i }, backend: t10, attrs: { shape: p } }), c = lv({ inputs: { a: n, b: u }, backend: t10 }), l = av({ inputs: { x: c }, backend: t10 }), m = bp({ inputs: { x: l }, backend: t10, attrs: { axis: a, keepDims: false } }), d = te({ inputs: { x: m }, backend: t10, attrs: { shape: p } }), f = cv({ inputs: { a: l, b: d }, backend: t10 });
return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), f;
}
var AF = { kernelName: bs, backendName: "webgl", kernelFunc: mv };
function ZJ(r) {
let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : mv({ inputs: { logits: n }, backend: t10, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new mg(u, c, s), m = [[a]], d = t10.runWebGLProgram(l, [p], "int32", m);
return i || t10.disposeIntermediateTensorInfo(p), d;
}
var FF = { kernelName: Hn, backendName: "webgl", kernelFunc: ZJ };
var JJ = Ut + `
return -x;
`;
var eee = `
vec4 result = -x;
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
function tee(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
if (t10.shouldExecuteOnCPU([o])) {
let s = t10.texData.get(o.dataId), [a, i] = RR(s.values, o.shape, o.dtype);
return t10.makeTensorInfo(i, o.dtype, a);
}
let n;
return P().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Ar(o.shape, eee) : n = new tr(o.shape, JJ), t10.runWebGLProgram(n, [o], o.dtype);
}
var PF = { kernelName: oa, backendName: "webgl", kernelFunc: tee };
var ree = Wt.nonMaxSuppressionV3Impl;
function oee(r) {
C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), { selectedIndices: l } = ree(u, c, a, i, p);
return t10.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var OF = { kernelName: jn, backendName: "webgl", kernelFunc: oee };
var nee = Wt.nonMaxSuppressionV4Impl;
function see(r) {
C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), { selectedIndices: m, validOutputs: d } = nee(c, l, a, i, p, u);
return [t10.makeTensorInfo([m.length], "int32", new Int32Array(m)), t10.makeTensorInfo([], "int32", new Int32Array([d]))];
}
var MF = { kernelName: Ha, backendName: "webgl", kernelFunc: see };
var aee = Wt.nonMaxSuppressionV5Impl;
function iee(r) {
C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = aee(c, l, m, d, f, h);
return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))];
}
var LF = { kernelName: Xn, backendName: "webgl", kernelFunc: iee };
var dg = class {
constructor(e, t10, o, n) {
this.variableNames = ["indices"], this.outputShape = [e, t10], this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${n}), float(${o}),
float(index == coords.y)));
}
`;
}
};
var uee = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new dg(u, a, i, p), l = te({ inputs: { x: n }, backend: t10, attrs: { shape: [u] } }), m = t10.runWebGLProgram(c, [l], s);
t10.disposeIntermediateTensorInfo(l);
let d = [...n.shape, a], f = te({ inputs: { x: m }, backend: t10, attrs: { shape: d } });
return t10.disposeIntermediateTensorInfo(m), f;
};
var BF = { kernelName: Yn, backendName: "webgl", kernelFunc: uee };
function rm(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
if (o.dtype === "complex64") {
let n = gi({ inputs: { input: o }, backend: t10 }), s = rm({ inputs: { x: n }, backend: t10 }), a = wp({ inputs: { input: o }, backend: t10 }), i = rm({ inputs: { x: a }, backend: t10 }), p = Fr({ inputs: { real: s, imag: i }, backend: t10 });
return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p;
} else
return xi({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t10 });
}
var zF = { kernelName: fa, backendName: "webgl", kernelFunc: rm };
function VF(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
if (o.dtype === "string")
throw new Error("onesLike is not supported under string dtype");
if (o.dtype === "complex64") {
let n = gi({ inputs: { input: o }, backend: t10 }), s = VF({ inputs: { x: n }, backend: t10 }), a = wp({ inputs: { input: o }, backend: t10 }), i = rm({ inputs: { x: a }, backend: t10 }), p = Fr({ inputs: { real: s, imag: i }, backend: t10 });
return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p;
} else
return xi({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t10 });
}
var WF = { kernelName: na, backendName: "webgl", kernelFunc: VF };
function pee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
if (e.length === 1)
return Qh({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
let s = e[0].shape, a = e[0].dtype;
e.forEach((c) => {
y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
});
let i = [], p = e.map((c) => {
let l = Qh({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
return i.push(l), l;
}), u = sv({ inputs: p, backend: t10, attrs: { axis: n } });
return i.forEach((c) => t10.disposeIntermediateTensorInfo(c)), u;
}
var UF = { kernelName: sa, backendName: "webgl", kernelFunc: pee };
var fg = class {
constructor(e, t10, o) {
this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t10.map((u, c) => u[0] + e[c] + u[1]);
let n = e.length, s = Re(n), a = t10.map((u) => u[0]).join(","), i = t10.map((u, c) => u[0] + e[c]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n);
if (n === 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 {
${s} coords = outC - start;
setOutput(getX(${p}));
}
}
`;
}
};
var hg = class {
constructor(e, t10, o) {
this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t10.map((h, g) => h[0] + e[g] + h[1]);
let n = e.length, s = Re(n), a = t10.map((h) => h[0]).join(","), i = t10.map((h, g) => h[0] + e[g]).join(","), p = Rt("rc", n), u = Rt("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = [`${s} rc = outputLoc;`, `${p[n - 1]} += 1;
if(${c}) {
`, n === 1 ? "" : `}
rc = outputLoc;
${p[n - 2]} += 1;
if(${p[n - 2]} < ${this.outputShape[n - 2]}) {`, n === 1 ? "" : ` ${p[n - 1]} += 1;
if(${c}) {`], d = n === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))", f = "";
for (let h = 0, g = n === 1 ? 2 : 4; h < g; h++)
f += `
${m[h]}
if (${d}) {
result[${h}] = float(value);
} else {
${s} source = rc - start;
result[${h}] = getChannel(getX(${u.join()}), ${l});
}
`;
f += n === 1 ? "} " : "}}", 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 dv = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o;
if (y.sizeFromShape(n.shape) === 0) {
let u = s.map((c, l) => c[0] + n.shape[l] + c[1]);
return xi({ backend: t10, attrs: { shape: u, value: a, dtype: n.dtype } });
}
let i = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new hg(n.shape, s, a) : new fg(n.shape, s, a), p = [[a]];
return t10.runWebGLProgram(i, [n], n.dtype, p);
};
var GF = { kernelName: Qn, backendName: "webgl", kernelFunc: dv };
var cee = `
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);
`;
var lee = `
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
bvec4 isNaN1 = lessThan(a, vec4(0.0));
bvec4 isNaN2 = lessThan(floor(b), b);
bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);
` + Kr + `
return result;
`;
var mee = nt({ opSnippet: cee, packedOpSnippet: lee });
var HF = { kernelName: Zn, backendName: "webgl", kernelFunc: mee };
function dee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = [], u = y.parseAxisParam(s, n.shape), c = u, l = C.getAxesPermutation(c, i), m = n;
l != null && (m = bt({ inputs: { x: n }, backend: t10, attrs: { perm: l } }), c = C.getInnerMostAxes(c.length, i), p.push(m)), C.assertAxesAreInnerMostDims("prod", c, i);
let d;
if (t10.shouldExecuteOnCPU([m])) {
let f = t10.texData.get(m.dataId).values, { outVals: h, outShape: g, outDtype: x } = AR(m.shape, m.dtype, f, c);
d = t10.makeTensorInfo(g, x, h);
} else {
let [f, h] = C.computeOutAndReduceShapes(m.shape, c), g = y.sizeFromShape(h), x = te({ inputs: { x: m }, backend: t10, attrs: { shape: [-1, g] } }), b = Za(n.dtype), w = qr(x, b, "prod", t10);
d = te({ inputs: { x: w }, backend: t10, attrs: { shape: f } }), p.push(x), p.push(w);
}
if (a) {
p.push(d);
let f = C.expandShapeToKeepDim(d.shape, u);
d = te({ inputs: { x: d }, backend: t10, attrs: { shape: f } });
}
return p.forEach((f) => t10.disposeIntermediateTensorInfo(f)), d;
}
var KF = { kernelName: es, backendName: "webgl", kernelFunc: dee };
function fee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t10.readSync(x.dataId)), u = n.map((x) => x.shape), c = t10.readSync(s.dataId), l = t10.readSync(a.dataId), [m, d, f] = FR(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t10.makeTensorInfo([x.length], "int32", x)), g = t10.makeTensorInfo(f, s.dtype, d);
return h.concat([g]);
}
var qF = { kernelName: Kp, backendName: "webgl", kernelFunc: fee };
function hee(r) {
let { inputs: e, backend: t10 } = r, { starts: o, limits: n, deltas: s } = e, a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = PR(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t10.makeTensorInfo([u.length], "int32", u), m = t10.makeTensorInfo([c.length], o.dtype, c);
return [l, m];
}
var jF = { kernelName: qp, backendName: "webgl", kernelFunc: hee };
function gee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), l = t10.readSync(a.dataId), m = i.map((g) => t10.readSync(g.dataId)), d = i.map((g) => g.shape), [f, h] = OR(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p);
return t10.makeTensorInfo(f, s.dtype, h);
}
var XF = { kernelName: jp, backendName: "webgl", kernelFunc: gee };
var fv = (r) => {
let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = MR(o, n, s, a);
return e.makeTensorInfo([i.length], a, i);
};
var YF = { kernelName: aa, backendName: "webgl", kernelFunc: fv };
var xee = "return 1.0 / x;";
var yee = ge({ opSnippet: xee });
var QF = { kernelName: ts, backendName: "webgl", kernelFunc: yee };
var bee = Ut + `
return (x < 0.0) ? 0.0 : x;
`;
var Cee = `
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;
`;
var wee = ge({ opSnippet: bee, packedOpSnippet: Cee });
var ZF = { kernelName: rs, backendName: "webgl", kernelFunc: wee };
var See = Ut + `
return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var Iee = `
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;
`;
var vee = ge({ opSnippet: See, packedOpSnippet: Iee });
var JF = { kernelName: ss, backendName: "webgl", kernelFunc: vee };
var gg = class {
constructor(e, t10, o, n, s) {
this.variableNames = ["A"], this.outputShape = [];
let [a, i, p, u] = e;
this.outputShape = [a, t10, o, u];
let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m;
s ? m = "(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)" : m = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0] / l[0]},
${c[1] / l[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${p}.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 xg = class {
constructor(e, t10, o, n, s) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
let [a, i, p, u] = e;
this.outputShape = [a, t10, o, u];
let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m;
s ? m = "(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)" : m = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0] / l[0]},
${c[1] / l[1]},
${c[1] / l[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,
${p}.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 < ${o - 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 kee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = P().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new xg(n.shape, p, u, s, a) : new gg(n.shape, p, u, s, a);
return t10.runWebGLProgram(c, [n], "float32");
}
var e3 = { kernelName: ns, backendName: "webgl", kernelFunc: kee };
var yg = class {
constructor(e, t10, o) {
this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t10;
let [, n, s] = t10, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2;
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${l});
const float invHeightScale = float(${m});
const float invWidthScale = float(${d});
const int winHeight = int(${f});
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), ${n - 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 Nee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new yg(s.shape, n.shape, a);
return t10.runWebGLProgram(i, [s], s.dtype);
}
var t3 = { kernelName: qa, backendName: "webgl", kernelFunc: Nee };
var bg = class {
constructor(e, t10, o, n, s) {
this.variableNames = ["A"], this.outputShape = [];
let [a, i, p, u] = e;
this.outputShape = [a, t10, o, u];
let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d;
s ? d = "max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))" : d = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0] / l[0]},
${c[1] / l[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`;
}
};
var Cg = class {
constructor(e, t10, o, n, s) {
this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
let [a, i, p, u] = e;
this.outputShape = [a, t10, o, u];
let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d;
s ? d = "max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))" : d = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0] / l[0]},
${c[1] / l[1]},
${c[1] / l[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,
${p}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${d};
// Compute the 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 < ${o - 1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`;
}
};
function Tee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = P().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new Cg(n.shape, p, u, s, a) : new bg(n.shape, p, u, s, a);
return t10.runWebGLProgram(c, [n], n.dtype);
}
var r3 = { kernelName: os, backendName: "webgl", kernelFunc: Tee };
var wg = class {
constructor(e, t10, o) {
this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t10;
let [, n, s] = t10, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2;
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${l});
const float invHeightScale = float(${m});
const float invWidthScale = float(${d});
const int winHeight = int(${f});
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 >= ${i}) {
continue;
}
float sourceFracRow =
float(${p[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${p[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${n}) - 1),
${o} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${o} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`;
}
};
function _ee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new wg(s.shape, n.shape, a);
return t10.runWebGLProgram(i, [s], s.dtype);
}
var o3 = { kernelName: Ka, backendName: "webgl", kernelFunc: _ee };
var Sg = class {
constructor(e, t10) {
this.variableNames = ["x"];
let o = e.length;
if (o > 4)
throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);
if (this.outputShape = e, o === 1) {
this.userCode = `
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;
return;
}
let n = (i) => t10.indexOf(i) !== -1 && e[i] !== 1 ? `${e[i]} - coords[${i}] - 1` : `coords[${i}]`, s = e.map((i, p) => n(p)).join(","), a = Re(o);
this.userCode = `
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${s}));
}
`;
}
};
var Ig = class {
constructor(e, t10) {
this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true;
let o = e.length;
if (o > 4)
throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);
this.outputShape = e;
let n = Rt("rc", o), s = `${n[o - 1]} + 1 < ${this.outputShape[o - 1]}`, a = `${n[o - 2]} + 1 < ${this.outputShape[o - 2]}`, i = Re(o);
o === 1 ? this.userCode = `
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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 = ${p(n.slice())};
if(${s}){
result.g = ${u(n.slice())};
}
if(${a}) {
result.b = ${c(n.slice())};
if(${s}) {
result.a = ${l(n.slice())};
}
}
setOutput(result);
}
`;
function p(f) {
return m(f);
}
function u(f) {
return f[o - 1] = "(" + f[o - 1] + " + 1)", m(f);
}
function c(f) {
return f[o - 2] = "(" + f[o - 2] + " + 1)", m(f);
}
function l(f) {
return f[o - 1] = "(" + f[o - 1] + " + 1)", f[o - 2] = "(" + f[o - 2] + " + 1)", m(f);
}
function m(f) {
let h = e.map((b, w) => d(w, f)), g = h.join(","), x = h.slice(-2).join(",");
return `getChannel(getX(${g}), vec2(${x}))`;
}
function d(f, h) {
return t10.indexOf(f) !== -1 && e[f] !== 1 ? `${e[f]} - ${h[f]} - 1` : `${h[f]}`;
}
}
};
function $ee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length, i = y.parseAxisParam(s, n.shape);
if (a === 0)
return Dt({ inputs: { x: n }, backend: t10 });
let p = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new Ig(n.shape, i) : new Sg(n.shape, i);
return t10.runWebGLProgram(p, [n], n.dtype);
}
var n3 = { kernelName: as, backendName: "webgl", kernelFunc: $ee };
var vg = class {
constructor(e, t10) {
this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }];
let o = e[1], n = e[2];
this.outputShape = e;
let s = "";
typeof t10 == "number" ? s = `float outputValue = ${t10.toFixed(2)};` : s = `
vec3 fill = vec3(${t10.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 < ${n} && coordY >= 0 && coordY < ${o}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`;
}
};
var s3 = { kernelName: _s, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = new vg(o.shape, s), [u, c] = C.getImageCenter(a, o.shape[1], o.shape[2]), l = [[u, c, Math.sin(n), Math.cos(n)]];
return i.runWebGLProgram(p, [o], o.dtype, l);
} };
var Eee = `
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`;
var Ree = ge({ opSnippet: Eee });
var a3 = { kernelName: is, backendName: "webgl", kernelFunc: Ree };
var Dee = "return inversesqrt(x);";
var Aee = ge({ opSnippet: Dee, cpuKernelImpl: LR });
var i3 = { kernelName: us, backendName: "webgl", kernelFunc: Aee };
var yu = class {
constructor(e, t10, o, n, s, a, i = true, p = false) {
this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a;
let u = Re(s.length), c = Re(a.length), l = "";
o === 1 ? l = "i" : o === 2 && (l = "i, j");
let m = `getIndices(${l})`, d = "";
n === 1 ? d = "i" : n === 2 && (d = "i, coords[1]");
let f = `getUpdates(${d})`, h = "";
p && (h = "coords[0], coords[1]");
let g = `getDefaultValue(${h})`, x = t10 > 1 ? "strides[j]" : "strides";
this.userCode = `
${u} strides = ${u}(${s});
void main() {
${c} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t10}; j++) {
int index = round(${m});
flattenedIndex += index * ${x};
}
if (flattenedIndex == coords[0]) {
sum += ${f};
found = true;
}
}
setOutput(mix(${g}, sum, float(found)));
}
`;
}
};
var kg = class {
constructor(e, t10, o, n, s, a, i = true, p = false) {
this.variableNames = ["updates", "indices", "defaultValue"], this.packedInputs = true, this.packedOutput = true, this.outputShape = a;
let u = Re(s.length), c = Re(a.length), l = "";
o === 1 ? l = "i" : o === 2 && (l = "i, j");
let m = `getIndices(${l})`, d = "";
n === 1 ? d = "i" : n === 2 && (d = "i, coords[1]");
let f = `getUpdates(${d})`, h = "";
p && (h = "coords[0], coords[1]");
let g = `getDefaultValue(${h})`, x = t10 > 1 ? "strides[j]" : "strides", b = t10 > 1 ? "strides[j + 1]" : "strides";
this.userCode = `
${u} strides = ${u}(${s});
void main() {
${c} coords = getOutputCoords();
vec4 sum = vec4(0.);
vec4 found = vec4(0.);
for (int i = 0; i < ${e}; i+=2) {
ivec2 flattenedIndex = ivec2(0);
for (int j = 0; j < ${t10}; j+=2) {
ivec4 index = round(${m});
flattenedIndex += index.xz * ${x};
if (j + 1 < ${t10}) {
flattenedIndex += index.yw * ${b};
}
}
if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||
flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {
vec4 updVals = ${f};
if (flattenedIndex[0] == coords[0]) {
sum.xy += updVals.xy;
found.xy = vec2(1.);
} else if (flattenedIndex[0] == coords[0] + 1) {
sum.zw += updVals.xy;
found.zw = vec2(1.);
}
if (flattenedIndex[1] == coords[0]) {
sum.xy += updVals.zw;
found.xy = vec2(1.);
} else if (flattenedIndex[1] == coords[0] + 1) {
sum.zw += updVals.zw;
found.zw = vec2(1.);
}
}
}
setOutput(mix(${g}, sum, found));
}
`;
}
};
function Fee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = C.calculateShapes(s, n, a), m = [l / u, u];
if (l === 0)
return t10.makeTensorInfo(a, n.dtype);
let d = te({ inputs: { x: n }, backend: t10, attrs: { shape: [p, i] } }), f = te({ inputs: { x: s }, backend: t10, attrs: { shape: [p, u] } }), h = t10.makeTensorInfo([], "float32", new Float32Array([0])), g;
P().getBool("WEBGL_PACK") ? g = new kg(p, i, d.shape.length, f.shape.length, c, m) : g = new yu(p, i, d.shape.length, f.shape.length, c, m);
let x = t10.runWebGLProgram(g, [f, d, h], f.dtype), b = te({ inputs: { x }, backend: t10, attrs: { shape: a } });
return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(h), b;
}
var u3 = { kernelName: ps, backendName: "webgl", kernelFunc: Fee };
var Ng = class {
constructor(e, t10, o, n) {
this.variableNames = ["sortedSequence", "values"], this.customUniforms = [{ name: "numInputs", type: "int" }], this.outputShape = [e, o];
let s = "while (left < right) {", a = `for (int i = 0; i < ${Math.ceil(Math.log2(t10 + 1))}; ++i) { if (left >= right) break;`, i = P().getNumber("WEBGL_VERSION") === 2 ? s : a, p = n === "left" ? "<" : "<=";
this.userCode = `
int findBound(int batch, float value) {
int left = 0;
int right = numInputs;
int mid;
${i}
mid = (left + right) / 2;
if (getSortedSequence(batch, mid) ${p} value) {
left = mid + 1;
} else {
right = mid;
}
}
return right;
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int valueIndex = coords[1];
float value = getValues(batch, valueIndex);
setOutput(float(findBound(batch, value)));
}
`;
}
};
function Pee(r) {
let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new Ng(n.shape[0], n.shape[1], s.shape[1], a), p = [[n.shape[1]]];
return t10.runWebGLProgram(i, [n, s], "int32", p);
}
var p3 = { kernelName: ls, backendName: "webgl", kernelFunc: Pee };
var Tg = class {
constructor(e, t10, o) {
this.variableNames = ["c", "a", "b"], this.outputShape = t10;
let n, s;
if (o > 4)
throw Error(`Where for rank ${o} is not yet supported`);
if (o === 1)
s = "resRC", n = "resRC";
else {
let i = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], p = [], u = [];
for (let c = 0; c < t10.length; c++)
u.push(`${i[c]}`), c < e && p.push(`${i[c]}`);
n = p.join(), s = u.join();
}
let a = Re(o);
this.userCode = `
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${n});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`;
}
};
function Oee(r) {
let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = new Tg(o.shape.length, n.shape, n.shape.length);
return t10.runWebGLProgram(a, [o, n, s], dt(n.dtype, s.dtype));
}
var c3 = { kernelName: ua, backendName: "webgl", kernelFunc: Oee };
var Mee = `
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${C.SELU_SCALEALPHA};
float scale = ${C.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;
var Lee = ge({ opSnippet: Mee });
var l3 = { kernelName: ms, backendName: "webgl", kernelFunc: Lee };
var Bee = Do + `
return 1.0 / (1.0 + exp(-1.0 * x));
`;
var zee = `
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
var Vee = ge({ opSnippet: Bee, packedOpSnippet: zee, cpuKernelImpl: zR });
var m3 = { kernelName: hs, backendName: "webgl", kernelFunc: Vee };
var Wee = `
if (isnan(x)) { return 0.0; }
return sign(x);
`;
var Uee = ge({ opSnippet: Wee });
var d3 = { kernelName: fs, backendName: "webgl", kernelFunc: Uee };
var Gee = Do + `
return sin(x);
`;
var Hee = `
vec4 result = sin(x);
bvec4 isNaN = isnan(x);
${Kr}
return result;
`;
var Kee = ge({ opSnippet: Gee, packedOpSnippet: Hee });
var f3 = { kernelName: ds, backendName: "webgl", kernelFunc: Kee };
var qee = `
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`;
var jee = ge({ opSnippet: qee });
var h3 = { kernelName: ja, backendName: "webgl", kernelFunc: jee };
var Xee = `
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;
`;
var Yee = ge({ opSnippet: Xee });
var g3 = { kernelName: gs, backendName: "webgl", kernelFunc: Yee };
var Qee = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o;
y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
let i = s.reduce((x, b) => x * b), p = [[0, 0]];
p.push(...a);
for (let x = 1 + s.length; x < n.shape.length; ++x)
p.push([0, 0]);
let u = [], c = dv({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), l = C.getReshaped(c.shape, s, i, false), m = C.getPermuted(l.length, s.length, false), d = C.getReshapedPermuted(c.shape, s, i, false), f = te({ inputs: { x: c }, backend: t10, attrs: { shape: l } }), h = bt({ inputs: { x: f }, backend: t10, attrs: { perm: m } }), g = te({ inputs: { x: h }, backend: t10, attrs: { shape: d } });
return u.push(c), u.push(f), u.push(h), u.forEach((x) => t10.disposeIntermediateTensorInfo(x)), g;
};
var x3 = { kernelName: ca, backendName: "webgl", kernelFunc: Qee };
function Zee(r) {
let { inputs: e, backend: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e;
if (s.shape.length !== 1)
throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);
if (o.shape.length !== 2)
throw new Error(`Indices must be a matrix, saw:
${o.shape}`);
if (n.shape.length !== 1)
throw new Error(`Values must be a vector, saw:
${n.shape}`);
if (a.shape.length !== 0)
throw new Error(`Default value must be a scalar, saw:
${a.shape}`);
let i = t10.readSync(o.dataId), p = t10.readSync(n.dataId), u = t10.readSync(s.dataId), c = t10.readSync(a.dataId)[0], [l, m, d, f, h] = WR(i, o.shape, o.dtype, p, n.dtype, u, c);
return [t10.makeTensorInfo(m, o.dtype, l), t10.makeTensorInfo([m[0]], n.dtype, d), t10.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t10.makeTensorInfo([h.length], o.dtype, new Int32Array(h))];
}
var y3 = { kernelName: Vi, backendName: "webgl", kernelFunc: Zee };
function Jee(r) {
let { inputs: e, backend: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = e;
if (o.shape.length !== 2)
throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);
if (n.shape.length !== 1)
throw new Error(`Input shape should be a vector but received shape ${n.shape}`);
if (s.shape.length !== 1)
throw new Error(`Target shape should be a vector but received shape ${s.shape}`);
let a = Array.from(t10.readSync(n.dataId)), i = t10.readSync(o.dataId), p = Array.from(t10.readSync(s.dataId)), [u, c, l] = UR(i, o.shape, o.dtype, a, p);
return [t10.makeTensorInfo(c, o.dtype, u), t10.makeTensorInfo([l.length], s.dtype, new Int32Array(l))];
}
var b3 = { kernelName: Xa, backendName: "webgl", kernelFunc: Jee };
function ete(r) {
let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e;
if (o.shape.length < 1)
throw new Error("Data should be at least 1 dimensional but received scalar");
if (n.shape.length !== 1)
throw new Error(`Indices should be a vector but received shape
${n.shape}`);
if (s.shape.length !== 1)
throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);
let a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = uh(a, o.shape, o.dtype, i, p, true);
return t10.makeTensorInfo(c, o.dtype, u);
}
var C3 = { kernelName: Wi, backendName: "webgl", kernelFunc: ete };
function tte(r) {
let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e;
if (o.shape.length < 1)
throw new Error("Data should be at least 1 dimensional but received scalar");
if (n.shape.length !== 1)
throw new Error(`Indices should be a vector but received shape
${n.shape}`);
if (s.shape.length !== 1)
throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);
let a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = uh(a, o.shape, o.dtype, i, p);
return t10.makeTensorInfo(c, o.dtype, u);
}
var w3 = { kernelName: Ui, backendName: "webgl", kernelFunc: tte };
function rte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = C.calculateShapes(s, n, i), d = false;
if (s.dtype === "string") {
let x = t10.bufferSync(n), b = t10.bufferSync(s), w = y.decodeString(t10.readSync(a.dataId)[0]), S = BR(x, b, i, m, c, u, p, l, w, d);
return t10.makeTensorInfo(i, S.dtype, S.values);
}
let f = new yu(u, p, n.shape.length, s.shape.length, l, [m, 1], d), h = t10.runWebGLProgram(f, [s, n, a], s.dtype), g = te({ inputs: { x: h }, backend: t10, attrs: { shape: i } });
return t10.disposeIntermediateTensorInfo(h), g;
}
var S3 = { kernelName: Cs, backendName: "webgl", kernelFunc: rte };
function ote(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = C.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice();
return p.map((m) => {
let d = [...l];
d[i] = m;
let f = Ls({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: d } });
return c[i] += m, f;
});
}
var I3 = { kernelName: la, backendName: "webgl", kernelFunc: ote };
var v3 = "return sqrt(x);";
var nte = ge({ opSnippet: v3, packedOpSnippet: v3, cpuKernelImpl: GR });
var k3 = { kernelName: xs, backendName: "webgl", kernelFunc: nte };
var ste = "return x * x;";
var ate = ge({ opSnippet: ste });
var N3 = { kernelName: Gi, backendName: "webgl", kernelFunc: ate };
var T3 = "return (a - b) * (a - b);";
var ite = nt({ opSnippet: T3, packedOpSnippet: T3 });
var _3 = { kernelName: ws, backendName: "webgl", kernelFunc: ite };
function ute(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
if (n.dtype !== "string")
throw new Error("Input must be of datatype string");
let s = t10.readSync(n.dataId), a = C.fromUint8ToStringArray(s), i = HR(a, "string", o);
return t10.makeTensorInfo(n.shape, "string", i);
}
var $3 = { kernelName: _u, backendName: "webgl", kernelFunc: ute };
function pte({ inputs: r, attrs: e, backend: t10 }) {
let { x: o } = r, n = Ut + `
return x > 0.0 ? 1.0 : float(${e.alpha});
`, s = new tr(o.shape, n);
return t10.runWebGLProgram(s, [o], o.dtype);
}
var E3 = { kernelName: yo, backendName: "webgl", kernelFunc: pte };
var _g = class {
constructor(e, t10, o) {
this.variableNames = ["x"], this.outputShape = o;
let n = o.length, s = Re(o.length), a = Re(o.length), i = "";
if (n === 1)
i = "coords * strides + begin";
else {
let p = 0;
i = o.map((u, c) => (p++, o.length === 1 ? `coords * strides[${c}] + begin[${c}]` : `coords[${p - 1}] * strides[${c}] + begin[${c}]`)).join(",");
}
this.userCode = `
${s} begin = ${s}(${e});
${s} strides = ${s}(${t10});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${i}));
}
`;
}
};
function cte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: w, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
if (h)
k = te({ inputs: { x: n }, backend: t10, attrs: { shape: f } });
else if (g || x) {
y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
let E = ct.computeOutShape(b, w, S), R = Ls({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: E } });
k = te({ inputs: { x: R }, backend: t10, attrs: { shape: f } }), t10.disposeIntermediateTensorInfo(R);
} else if (t10.shouldExecuteOnCPU([n])) {
let R = t10.readSync(n.dataId), D = me(n.shape, n.dtype, R), F = KR(d, D, S, b);
k = t10.makeTensorInfo(f, n.dtype, F.values);
} else {
let R = new _g(b, S, d);
k = t10.runWebGLProgram(R, [n], n.dtype);
}
let _ = te({ inputs: { x: k }, backend: t10, attrs: { shape: f } });
return t10.disposeIntermediateTensorInfo(k), _;
}
var R3 = { kernelName: Ss, backendName: "webgl", kernelFunc: cte };
function lte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.readSync(c.dataId), d = t10.readSync(l.dataId), [f, h] = qR(m, d, n, s, a, i, p, u);
return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)];
}
var D3 = { kernelName: ma, backendName: "webgl", kernelFunc: lte };
function mte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { skipEmpty: n } = o, { 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 = t10.readSync(s.dataId), p = t10.readSync(a.dataId)[0], [u, c, l] = jR(i, p, n), m = c.length;
return [t10.makeTensorInfo([m, 2], "int32", u), t10.makeTensorInfo([m], "string", c), t10.makeTensorInfo([2], "int32", new Int32Array(l))];
}
var A3 = { kernelName: Hi, backendName: "webgl", kernelFunc: mte };
function dte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { numBuckets: n } = o, { input: s } = e;
if (s.dtype !== "string")
throw new Error("Input must be of datatype string");
if (n <= 0)
throw new Error("Number of buckets must be at least 1");
let a = t10.readSync(s.dataId), i = XR(a, n);
return t10.makeTensorInfo(s.shape, "int32", i);
}
var F3 = { kernelName: Ki, backendName: "webgl", kernelFunc: dte };
var fte = "return tan(x);";
var hte = ge({ opSnippet: fte });
var P3 = { kernelName: vs, backendName: "webgl", kernelFunc: hte };
var gte = `
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`;
var xte = ge({ opSnippet: gte });
var O3 = { kernelName: ks, backendName: "webgl", kernelFunc: xte };
function yte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { tensor: n, indices: s, updates: a } = e, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = C.calculateShapes(a, s, n.shape), m = [l / u, u];
if (l === 0)
return t10.makeTensorInfo(n.shape, s.dtype);
let d = te({ inputs: { x: s }, backend: t10, attrs: { shape: [p, i] } }), f = te({ inputs: { x: a }, backend: t10, attrs: { shape: [p, u] } }), h = te({ inputs: { x: n }, backend: t10, attrs: { shape: m } }), g = new yu(p, i, d.shape.length, f.shape.length, c, m, false, true), x = t10.runWebGLProgram(g, [f, d, h], h.dtype), b = te({ inputs: { x }, backend: t10, attrs: { shape: n.shape } });
return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(x), b;
}
var M3 = { kernelName: cs, backendName: "webgl", kernelFunc: yte };
var $g = class {
constructor(e, t10) {
this.variableNames = ["A"];
let o = new Array(e.length);
for (let a = 0; a < o.length; a++)
o[a] = e[a] * t10[a];
this.outputShape = o, this.rank = o.length;
let n = Re(this.rank), s = bte(e);
this.userCode = `
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`;
}
};
function bte(r) {
let e = r.length;
if (e > 5)
throw Error(`Tile for rank ${e} is not yet supported`);
if (e === 1)
return `imod(resRC, ${r[0]})`;
let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"], o = [];
for (let n = 0; n < r.length; n++)
o.push(`imod(${t10[n]}, ${r[n]})`);
return o.join();
}
function hv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o;
if (n.dtype === "string" || n.shape.length > 5) {
let p = t10.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = QR(c, s);
return t10.makeTensorInfo(l.shape, l.dtype, l.values);
}
let a = new $g(n.shape, s);
return t10.runWebGLProgram(a, [n], n.dtype);
}
var L3 = { kernelName: so, backendName: "webgl", kernelFunc: hv };
var Eg = 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));
}
}
`;
}
};
var Rg = 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 Ip(r, e) {
e !== null && r.disposeIntermediateTensorInfo(e);
}
function B3(r) {
let e = 1;
for (; e < r; )
e *= 2;
return e;
}
function Cte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = P().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), p = P().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), u = n.shape, c = u[u.length - 1];
if (t10.shouldExecuteOnCPU([n]) || c < i || s > p) {
let F = t10.readSync(n.dataId), [O, M] = ZR(F, u, n.dtype, s, a);
return [t10.makeTensorInfo(O.shape, O.dtype, O.values), t10.makeTensorInfo(M.shape, M.dtype, M.values)];
}
if (s === 0)
return u[u.length - 1] = 0, [t10.makeTensorInfo(u, n.dtype, []), t10.makeTensorInfo(u, "int32", [])];
if (c === 1)
return [n, xi({ attrs: { shape: u, dtype: "int32", value: 0 }, backend: t10 })];
let l = t10.texData.get(n.dataId), m = l !== null && l.isPacked, d = m ? t10.unpackTensor(n) : n, h = y.sizeFromShape(u) / c, g = te({ inputs: { x: d }, attrs: { shape: [h, c] }, backend: t10 });
m && Ip(t10, d);
let x = B3(s), b = B3(c), w = null, S = () => w === null ? [g, g] : [g, w], k = (F, O, M) => {
let L = S(), B = new Eg(M), U = [[c], [w === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [F], [O]], j = w;
w = t10.runWebGLProgram(B, L, "int32", U), Ip(t10, j);
};
for (let F = 1; F < x; F *= 2) {
let O = F * 2;
for (let M = F; M >= 1; M /= 2)
k(O, M, [h, b]);
}
for (let F = b; F > x; F /= 2) {
let O = S(), M = new Rg([h, F / 2]), B = [[c], [w === null ? 1 : 0], [x]], z = w;
w = t10.runWebGLProgram(M, O, "int32", B), Ip(t10, z);
let U = x / 2, j = U * 2;
for (let H = U; H >= 1; H /= 2)
k(j, H, w.shape);
}
let _ = w;
w = Ls({ inputs: { x: w }, backend: t10, attrs: { begin: 0, size: [h, s] } }), Ip(t10, _);
let E = uv({ inputs: { x: g, indices: w }, backend: t10, attrs: { axis: 1, batchDims: 1 } });
Ip(t10, g);
let R = u.slice(0, -1);
R.push(s), _ = w, w = te({ inputs: { x: w }, attrs: { shape: R }, backend: t10 }), Ip(t10, _);
let D = E;
return E = te({ inputs: { x: E }, attrs: { shape: R }, backend: t10 }), Ip(t10, D), [E, w];
}
var z3 = { kernelName: Ns, backendName: "webgl", kernelFunc: Cte };
var Dg = class {
constructor(e, t10, o, n, s, a) {
this.variableNames = ["Image", "Transforms"], this.outputShape = a;
let i = o === "nearest" ? 1 : 2, p;
switch (n) {
case "constant":
p = 1;
break;
case "reflect":
p = 2;
break;
case "wrap":
p = 3;
break;
case "nearest":
p = 4;
break;
default:
p = 1;
break;
}
this.userCode = `
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${p} == 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 (${p} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${p} == 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 < ${t10}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${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(${s});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t10}));
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 wte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Dg(l, m, a, i, p, g);
return t10.runWebGLProgram(x, [n, s], "float32");
}
var V3 = { kernelName: Ts, backendName: "webgl", kernelFunc: wte };
function Ste(r) {
let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e;
Ps(s, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
let a = o.readSync(s.dataId), { outputValues: i, outputShape: p, indices: u } = JR(a, n, s.shape, s.dtype);
return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)];
}
var W3 = { kernelName: qi, backendName: "webgl", kernelFunc: Ste };
function Ite(r) {
let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o;
s < 0 && (s += n.shape.length);
let a = n, i = a.shape.length, p = n.shape[s], u = new Array(i - 1), c = 0;
for (let h = 0; h < i; h++)
h !== s && (u[c++] = a.shape[h]);
let l = [], m = new Array(i).fill(0), d = a.shape.slice();
d[s] = 1;
let f = new Array(p);
for (let h = 0; h < f.length; h++) {
m[s] = h;
let g = Ls({ inputs: { x: a }, backend: t10, attrs: { begin: m, size: d } }), x = te({ inputs: { x: g }, backend: t10, attrs: { shape: u } });
f[h] = x, l.push(g);
}
return l.forEach((h) => t10.disposeIntermediateTensorInfo(h)), f;
}
var U3 = { kernelName: da, backendName: "webgl", kernelFunc: Ite };
var Ag = class {
constructor(e, t10) {
this.variableNames = ["x", "segmentIds"];
let o = e.windowSize, n = e.batchSize, s = e.inSize, a = e.numSegments, i = a * Math.ceil(s / o);
this.outputShape = [n, i];
let p = "0.0", u = "sumValue", c = Math.floor(o / 4) * 4, l = o % 4, m = `
sumValue += dot(values, segFilter);
`, d = "";
s % o > 0 && (d = `
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`);
let f = "";
s % o > 0 && (f = `
if (inIdx < 0 || inIdx >= ${s}) {
return -1.0;
}
`), this.userCode = `
const float initializationValue = ${p};
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${f}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${o}));
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 + ${c};
if (${l === 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
);
${m}
} else if (${l === 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
);
${m}
} else if (${l === 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 vte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = C.getAxesPermutation([u], i), l = n;
c != null && (l = bt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), p.push(l), u = C.getInnerMostAxes(1, i)[0]);
let m = C.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = te({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, d] } });
p.push(f);
let h = Za(n.dtype), g = (S, k, _, E, R) => {
let D = S.shape[0], F = S.shape[1], O = C.segment_util.segOpComputeOptimalWindowSize(F, R), M = { windowSize: O, inSize: F, batchSize: D, numSegments: R }, L = new Ag(M, k), B = t10.compileAndRun(L, [S, _], E);
if (p.push(B), B.shape[1] === R)
return B;
let z = fv({ backend: t10, attrs: { start: 0, stop: R, step: 1, dtype: "float32" } }), U = hv({ inputs: { x: z }, backend: t10, attrs: { reps: [F / O] } });
return p.push(z), p.push(U), g(B, k, U, E, R);
}, x = g(f, "unsortedSegmentSum", s, h, a), b = te({ inputs: { x }, backend: t10, attrs: { shape: m } }), w = b;
if (c != null) {
p.push(b);
let S = C.getUndoAxesPermutation(c);
w = bt({ inputs: { x: w }, backend: t10, attrs: { perm: S } });
}
return p.forEach((S) => t10.disposeIntermediateTensorInfo(S)), w;
}
var G3 = { kernelName: ji, backendName: "webgl", kernelFunc: vte };
var kte = [TD, $D, ED, RD, AD, FD, PD, OD, BD, zD, VD, WD, UD, GD, HD, KD, qD, jD, XD, YD, QD, JD, eA, tA, sA, iA, uA, xD, cA, mA, dA, fA, hA, gA, xA, yA, bA, CA, wA, vA, kA, NA, TA, _A, $A, EA, RA, DA, AA, FA, PA, OA, MA, LA, BA, VA, WA, UA, GA, KA, qA, jA, XA, YA, QA, ZA, JA, eF, gD, tF, lA, rF, oF, nF, yD, sF, aF, iF, uF, pF, cF, lF, mF, dF, fF, gF, xF, yF, bF, CF, wF, IF, kF, NF, TF, _F, $F, FF, wD, PF, OF, MF, LF, rA, BF, WF, UF, GF, HF, bD, KF, qF, jF, XF, YF, oA, EF, QF, ZF, JF, ID, e3, t3, r3, o3, n3, s3, a3, i3, u3, p3, c3, l3, m3, d3, f3, h3, ZD, AF, g3, x3, y3, b3, C3, w3, S3, I3, k3, N3, _3, $3, E3, R3, D3, A3, F3, DF, kD, P3, O3, M3, L3, z3, V3, ND, W3, U3, G3, zF];
for (let r of kte)
Ya(r);
var we;
(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";
})(we || (we = {}));
var bu;
(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";
})(bu || (bu = {}));
var H3;
function Nte(r) {
H3 = r.wasm.cwrap(bo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Tte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e;
if (n.dtype !== "float32" || s.dtype !== "float32")
throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");
let { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m = t10.dataIdMap.get(n.dataId).id, d = t10.dataIdMap.get(s.dataId).id, f = 0;
if (a != null) {
let R = t10.dataIdMap.get(a.dataId);
if (R.shape.length !== 1)
throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);
f = R.id;
}
let h = i == null ? 0 : t10.dataIdMap.get(i.dataId).id, g = bu[c];
if (g == null)
throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);
let x = p ? n.shape[2] : n.shape[1], b = u ? s.shape[1] : s.shape[2], w = Sr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)), S = t10.makeOutput([...w, x, b], n.dtype), k = t10.dataIdMap.get(S.dataId).id, _ = new Uint8Array(new Int32Array(n.shape).buffer), E = new Uint8Array(new Int32Array(s.shape).buffer);
return H3(m, _, n.shape.length, d, E, s.shape.length, p, u, g, f, h, l || 0, k), S;
}
var K3 = { kernelName: bo, backendName: "wasm", setupFunc: Nte, kernelFunc: Tte };
function Ce(r, e) {
let t10;
function o(s) {
t10 = s.wasm.cwrap(r, null, ["number", "number", "number"]);
}
function n(s) {
let { backend: a, inputs: { x: i } } = s, p = a.dataIdMap.get(i.dataId).id, u = a.makeOutput(i.shape, e || i.dtype), c = a.dataIdMap.get(u.dataId).id;
return y.sizeFromShape(u.shape) === 0 || t10(p, we[i.dtype], c), u;
}
return { kernelName: r, backendName: "wasm", setupFunc: o, kernelFunc: n };
}
var q3 = Ce(Gs);
var j3 = Ce(zo);
var X3 = Ce(Vo);
function Je(r, e, t10) {
let o;
function n(a) {
o = a.wasm.cwrap(r, null, ["number", "array", "number", "number", "array", "number", "number", "number"]);
}
function s(a) {
let { backend: i, inputs: p } = a, { a: u, b: c } = p, l = i.dataIdMap.get(u.dataId).id, m = i.dataIdMap.get(c.dataId).id, d = t10 != null ? t10 : u.dtype, f = C.assertAndGetBroadcastShape(u.shape, c.shape), h = i.makeOutput(f, d);
if (y.sizeFromShape(f) === 0)
return h;
let g = new Uint8Array(new Int32Array(u.shape).buffer), x = new Uint8Array(new Int32Array(c.shape).buffer), b = i.dataIdMap.get(h.dataId).id;
return (() => o(l, g, u.shape.length, m, x, c.shape.length, we[u.dtype], b))(), h;
}
return { kernelName: r, backendName: "wasm", setupFunc: n, kernelFunc: s };
}
var _te = true;
var Y3 = Je(no, _te);
var Q3;
function $te(r) {
Q3 = r.wasm.cwrap(Wo, null, ["array", "number", "number", "number"]);
}
function Ete(r) {
let { inputs: e, backend: t10 } = r, o = t10.makeOutput(e[0].shape, e[0].dtype);
if (y.sizeFromShape(o.shape) === 0)
return o;
let n = e.map((i) => t10.dataIdMap.get(i.dataId).id), s = new Uint8Array(new Int32Array(n).buffer), a = t10.dataIdMap.get(o.dataId).id;
return Q3(s, n.length, we[o.dtype], a), o;
}
var Z3 = { kernelName: Wo, backendName: "wasm", setupFunc: $te, kernelFunc: Ete };
function vp(r) {
let { inputs: { x: e }, backend: t10 } = r;
if (e.dtype === "string")
return ir(t10.readSync(e.dataId), e.shape, e.dtype);
let o = t10.makeOutput(e.shape, e.dtype), n = t10.typedArrayFromHeap(e);
return t10.typedArrayFromHeap(o).set(n), o;
}
var J3 = { kernelName: xo, backendName: "wasm", kernelFunc: vp };
var eP;
function Rte(r) {
eP = r.wasm.cwrap(ao, null, ["number", "array", "number", "number", "number", "array", "number"]);
}
function mo(r) {
let { inputs: e, backend: t10, attrs: o } = r, [n, s] = Ate(e.x.shape, o.perm), a = true;
for (let f = 0; f < s.length; f++)
s[f] !== f && (a = false);
let i = Dte(e.x.shape, o.perm), p = { dataId: e.x.dataId, shape: n, dtype: e.x.dtype };
if (a) {
let f = vp({ inputs: e, backend: t10 });
return f.shape = i, f;
}
let u = t10.makeOutput(i, p.dtype), c = t10.dataIdMap.get(p.dataId).id, l = t10.dataIdMap.get(u.dataId).id, m = new Uint8Array(new Int32Array(s).buffer), d = new Uint8Array(new Int32Array(p.shape).buffer);
return eP(c, d, p.shape.length, we[p.dtype], l, m, s.length), u;
}
function Dte(r, e) {
let t10 = new Array(r.length);
for (let o = 0; o < t10.length; o++)
t10[o] = r[e[o]];
return t10;
}
function Ate(r, e) {
let t10 = [], o = [];
for (let n = 0; n < r.length; ++n)
r[n] !== 1 && t10.push(r[n]), r[e[n]] !== 1 && o.push(e[n]);
for (let n = 0; n < o.length; ++n) {
let s = -1;
for (let a = 0; a < o.length; ++a)
o[a] >= n && (s === -1 || o[s] > o[a]) && (s = a);
o[s] = n;
}
return [t10, o];
}
var tP = { kernelName: ao, backendName: "wasm", kernelFunc: mo, setupFunc: Rte };
function Tr(r, e, t10) {
let o = r.shape, n = r.shape.length, s = y.parseAxisParam(e, o), a = s, i = C.getAxesPermutation(a, n), p = null, u = false;
if (i != null) {
let c = new Array(n);
for (let d = 0; d < c.length; d++)
c[d] = o[i[d]];
a = C.getInnerMostAxes(a.length, n), p = mo({ inputs: { x: r }, attrs: { perm: i }, backend: t10 });
let l = t10.dataIdMap.get(r.dataId).id;
t10.dataIdMap.get(p.dataId).id !== l && (u = true);
}
return { transposed: p, originalAxes: s, axes: a, inputWasTransposed: u };
}
var rP;
function Fte(r) {
rP = r.wasm.cwrap(Uo, null, ["number, number, number"]);
}
function Pte(r) {
let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e);
if (d) {
let w = e.dataIdMap.get(c.dataId).id;
u = c, p = w;
}
let f = u.shape.length;
C.assertAxesAreInnerMostDims("all", l, f);
let [h, g] = C.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let w = e.dataIdMap.get(b.dataId).id;
rP(p, x, w);
}
if (d && e.disposeData(c.dataId), s) {
let w = C.expandShapeToKeepDim(b.shape, m);
b.shape = w;
}
return b;
}
var oP = { kernelName: Uo, backendName: "wasm", setupFunc: Fte, kernelFunc: Pte };
var nP;
function Ote(r) {
nP = r.wasm.cwrap(Go, null, ["number, number, number"]);
}
function Mte(r) {
let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e);
if (d) {
let w = e.dataIdMap.get(c.dataId).id;
u = c, p = w;
}
let f = u.shape.length;
C.assertAxesAreInnerMostDims("any", l, f);
let [h, g] = C.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let w = e.dataIdMap.get(b.dataId).id;
nP(p, x, w);
}
if (d && e.disposeData(c.dataId), s) {
let w = C.expandShapeToKeepDim(b.shape, m);
b.shape = w;
}
return b;
}
var sP = { kernelName: Go, backendName: "wasm", setupFunc: Ote, kernelFunc: Mte };
function Fg(r) {
let e;
function t10(n) {
e = n.wasm.cwrap(r, null, ["number", "number", "number", "number", "number"]);
}
function o(n) {
let { backend: s, inputs: a, attrs: i } = n, { axis: p } = i, { x: u } = a, c = s.dataIdMap.get(u.dataId).id, l = c, m = u, { transposed: d, axes: f, inputWasTransposed: h } = Tr(u, p, s);
if (h) {
let k = s.dataIdMap.get(d.dataId).id;
k !== c && (m = d, l = k);
}
let g = m.shape.slice(0, -1), x = s.makeOutput(g, "int32"), b = s.dataIdMap.get(x.dataId).id, w = y.sizeFromShape(x.shape), S = m.shape[f[0]];
return e(l, we[m.dtype], w, S, b), h && s.disposeData(d.dataId), x;
}
return { kernelName: r, backendName: "wasm", setupFunc: t10, kernelFunc: o };
}
var aP = Fg(Hs);
var iP = Fg(Ks);
var uP = Ce(Ho);
var pP = Ce(Ko);
var cP = Ce(qo);
var lP = Je(Xo, false);
var mP = Ce(jo);
var dP;
function Lte(r) {
dP = r.wasm.cwrap(Yo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Bte(r) {
let { inputs: e, attrs: t10, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t10, c = C.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.strideHeight, b = 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 S = o.makeOutput(c.outShape, "float32"), k = o.dataIdMap.get(S.dataId).id;
return dP(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, w, k), S;
}
var fP = { kernelName: Yo, backendName: "wasm", setupFunc: Lte, kernelFunc: Bte };
var hP;
function zte(r) {
hP = r.wasm.cwrap("AvgPool3D", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Vte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = C.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.makeOutput(c.outShape, n.dtype);
return hP(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l;
}
var gP = { kernelName: qs, backendName: "wasm", setupFunc: zte, kernelFunc: Vte };
var xP;
function Wte(r) {
xP = r.wasm.cwrap("AvgPool3DGrad", 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"]);
}
function Ute(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = C.computePool3DInfo(s.shape, a, i, 1, p, u), l = t10.makeOutput(s.shape, s.dtype);
return xP(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left, c.filterDepth, c.filterHeight, c.filterWidth), l;
}
var yP = { kernelName: Ni, backendName: "wasm", setupFunc: Wte, kernelFunc: Ute };
function zt(r) {
let { inputs: e, attrs: t10 } = r, { x: o } = e, { shape: n } = t10, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s);
return y.assert(s === y.sizeFromShape(a), () => `new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype };
}
var bP = { kernelName: ia, backendName: "wasm", kernelFunc: zt };
var CP;
function Gte(r) {
CP = r.wasm.cwrap(Qo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
}
function Hte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
if (n.dtype !== "float32" || s.dtype !== "float32")
throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), w = Sr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]);
y.assert(c === l, () => `Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`);
let S = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = zt({ inputs: { x: n }, backend: t10, attrs: { shape: S } }), E = zt({ inputs: { x: s }, backend: t10, attrs: { shape: k } }), R = t10.dataIdMap.get(_.dataId).id, D = t10.dataIdMap.get(E.dataId).id, F = a ? _.shape[2] : _.shape[1], O = i ? E.shape[1] : E.shape[2], M = Math.max(g, x), L = t10.makeOutput([M, F, O], _.dtype), B = t10.dataIdMap.get(L.dataId).id, z = new Uint8Array(new Int32Array(_.shape).buffer), U = new Uint8Array(new Int32Array(E.shape).buffer);
return CP(R, z, _.shape.length, D, U, E.shape.length, a, i, B), t10.disposeData(_.dataId), t10.disposeData(E.dataId), L.shape = w, L;
}
var wP = { kernelName: Qo, backendName: "wasm", setupFunc: Gte, kernelFunc: Hte };
function Ao(r) {
let { inputs: { x: e }, attrs: { begin: t10, size: o }, backend: n } = r, [s, a] = ct.parseSliceParams(e, t10, o), i = ct.isSliceContinous(e.shape, s, a), p = n.readSync(e.dataId), u = n.makeOutput(a, e.dtype), c = y.computeStrides(e.shape), l = n.dataIdMap.get(u.dataId);
if (i) {
let f = ct.computeFlatOffset(s, c);
return e.dtype === "string" ? l.stringBytes = p.slice(f, f + y.sizeFromShape(a)) : n.typedArrayFromHeap(u).set(p.subarray(f, f + y.sizeFromShape(a))), u;
}
if (e.dtype === "string") {
let f = ip(p, s, a, e.shape, e.dtype);
return l.stringBytes = f, u;
}
let m = n.typedArrayFromHeap(u), d = e.shape.length;
if (d === 2)
Kte(p, c[0], m, s, a);
else if (d === 3)
qte(p, c[0], c[1], m, s, a);
else if (d === 4)
jte(p, c[0], c[1], c[2], m, s, a);
else {
let f = ip(p, s, a, e.shape, e.dtype);
m.set(f);
}
return u;
}
function Kte(r, e, t10, o, n) {
let s = 0, a = o[0], i = o[1], p = a + n[0];
for (let u = a; u < p; u++) {
let c = u * e + i;
t10.set(r.subarray(c, c + n[1]), s), s += n[1];
}
}
function qte(r, e, t10, o, n, s) {
let a = 0, i = n[0], p = n[1], u = n[2], c = i + s[0], l = p + s[1];
for (let m = i; m < c; m++)
for (let d = p; d < l; d++) {
let f = m * e + d * t10 + u;
o.set(r.subarray(f, f + s[2]), a), a += s[2];
}
}
function jte(r, e, t10, o, n, s, a) {
let i = 0, p = s[0], u = s[1], c = s[2], l = p + a[0], m = u + a[1], d = c + a[2], f = s[3];
for (let h = p; h < l; h++)
for (let g = u; g < m; g++)
for (let x = c; x < d; x++) {
let b = h * e + g * t10 + x * o + f;
n.set(r.subarray(b, b + a[3]), i), i += a[3];
}
}
var SP = { kernelName: pa, backendName: "wasm", kernelFunc: Ao };
function Xte(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o, i = s.reduce((x, b) => x * b), p = C.getReshaped(n.shape, s, i), u = C.getPermuted(p.length, s.length), c = C.getReshapedPermuted(n.shape, s, i), l = C.getSliceBeginCoords(a, s.length), m = C.getSliceSize(c, a, s.length), d = zt({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), f = mo({ inputs: { x: d }, backend: t10, attrs: { perm: u } }), h = zt({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = Ao({ inputs: { x: h }, backend: t10, attrs: { begin: l, size: m } });
return t10.disposeData(d.dataId), t10.disposeData(f.dataId), t10.disposeData(d.dataId), g;
}
var IP = { kernelName: js, backendName: "wasm", kernelFunc: Xte };
var vP;
function Yte(r) {
vP = r.wasm.cwrap(Zo, null, ["number", "number", "boolean", "number", "number", "number"]);
}
function Qte(r) {
let { backend: e, inputs: t10, attrs: o } = r, { x: n, weights: s } = t10, { size: a } = o, i = s.shape.reduce((l, m) => l * m, 1) !== 0, p = n.shape.length === 1 ? [a] : [n.shape[0], a], u = e.makeOutput(p, s.dtype);
function c(l) {
return e.dataIdMap.get(l.dataId).id;
}
return vP(c(n), a, i, c(s), we[s.dtype], c(u)), u;
}
var kP = { kernelName: Zo, backendName: "wasm", setupFunc: Yte, kernelFunc: Qte };
function Zte(r) {
let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.typedArrayFromHeap(o), a = t10.typedArrayFromHeap(n), i = C.assertAndGetBroadcastShape(Array.from(s), Array.from(a));
return t10.makeOutput([i.length], "int32", void 0, new Int32Array(i));
}
var NP = { kernelName: Xs, backendName: "wasm", kernelFunc: Zte };
function Pr(r) {
let { inputs: { x: e }, attrs: { dtype: t10 }, backend: o } = r, n = o.makeOutput(e.shape, t10), s = o.typedArrayFromHeap(e);
return o.typedArrayFromHeap(n).set(s), n;
}
var TP = { kernelName: ho, backendName: "wasm", kernelFunc: Pr };
var _P = Ce(Jo);
var $P;
function Jte(r) {
$P = r.wasm.cwrap(go, null, ["number", "number", "number", "number"]);
}
function ere(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i = t10.dataIdMap.get(n.dataId).id, p = t10.makeOutput(n.shape, n.dtype), u = t10.dataIdMap.get(p.dataId).id;
return $P(i, s, a, u), p;
}
var EP = { kernelName: go, backendName: "wasm", setupFunc: Jte, kernelFunc: ere };
function gv(r) {
let { inputs: e, backend: t10 } = r, o = y.parseAxisParam(r.attrs.axis, e[0].shape)[0], n = e.map((d) => d.shape);
C.assertParamsConsistent(n, o);
let s = C.computeOutShape(e.map((d) => d.shape), o), a = e.filter((d) => y.sizeFromShape(d.shape) > 0);
if (a.length === 1)
return vp({ inputs: { x: a[0] }, backend: t10 });
let i = t10.makeOutput(s, e[0].dtype);
if (y.sizeFromShape(s) === 0)
return i;
if (a[0].dtype === "string") {
let d = a.map((w) => {
let k = [-1, y.sizeFromShape(w.shape.slice(o))];
return zt({ inputs: { x: w }, backend: t10, attrs: { shape: k } });
}), f = d.map((w) => ({ vals: t10.readSync(w.dataId), shape: w.shape }));
s = C.computeOutShape(d.map((w) => w.shape), 1);
let h = d[0].shape[0] === 1, g = np(f, s, e[0].dtype, h), x = C.computeOutShape(a.map((w) => w.shape), o);
i.shape = x;
let b = t10.dataIdMap.get(i.dataId);
return b.stringBytes = C.fromStringArrayToUint8(g), d.forEach((w) => t10.disposeData(w.dataId)), i;
}
let p = y.sizeFromShape(a[0].shape.slice(0, o)), u = 0, c = a.map((d) => {
let f = y.sizeFromShape(d.shape.slice(o));
return u += f, f;
}), l = a.map((d) => t10.typedArrayFromHeap(d)), m = t10.typedArrayFromHeap(i);
for (let d = 0; d < p; d++) {
let f = d * u;
for (let h = 0; h < l.length; h++) {
let g = c[h], x = d * g, b = l[h].subarray(x, x + g);
m.set(b, f), f += g;
}
}
return i;
}
var RP = { kernelName: Ys, backendName: "wasm", kernelFunc: gv };
var DP;
function tre(r) {
DP = r.wasm.cwrap(en, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function rre(r) {
let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l, dataFormat: m } = t10, d = C.convertConv2DDataFormat(m), f = C.computeConv2DInfo(n.shape, s.shape, p, u, c, l, false, d), h = f.filterHeight, g = f.filterWidth, x = f.padInfo.top, b = f.padInfo.right, w = f.padInfo.bottom, S = f.padInfo.left, k = f.dilationHeight, _ = f.dilationWidth, E = f.strideHeight, R = f.strideWidth, D = f.inChannels, F = f.outChannels, O = f.padInfo.type === "SAME" ? 1 : 0;
if (f.dataFormat !== "channelsLast")
throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);
let M = o.makeOutput(f.outShape, "float32"), L = o.dataIdMap.get(M.dataId).id;
return DP(a, n.shape[0], n.shape[1], n.shape[2], i, h, g, x, b, w, S, O, k, _, E, R, D, F, L), M;
}
var AP = { kernelName: en, backendName: "wasm", setupFunc: tre, kernelFunc: rre };
var FP;
function ore(r) {
FP = r.wasm.cwrap(tn, 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 nre(r) {
let { backend: e, inputs: t10, attrs: o } = r, { dy: n, filter: s } = t10, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, inputShape: c } = o, l = 1, m = C.convertConv2DDataFormat(p), d = C.computeConv2DInfo(c, s.shape, a, l, i, u, false, m), { batchSize: f, filterHeight: h, filterWidth: g, inChannels: x, inHeight: b, inWidth: w, outChannels: S, outHeight: k, outWidth: _, strideHeight: E, strideWidth: R } = d, D = h - 1 - d.padInfo.top, F = g - 1 - d.padInfo.left, O = d.dataFormat === "channelsLast", M = y.computeStrides(d.inShape), L = y.computeStrides(n.shape), [B, z, U] = y.computeStrides(s.shape), j = M[0], H = O ? M[1] : M[2], X = O ? M[2] : 1, J = O ? 1 : M[1], re = L[0], ne = O ? L[1] : L[2], ee = O ? L[2] : 1, oe = O ? 1 : L[1], ie = e.makeOutput(d.inShape, "float32"), le = e.dataIdMap.get(ie.dataId).id, ye = e.dataIdMap.get(n.dataId).id, _e = e.dataIdMap.get(s.dataId).id;
return FP(ye, _e, f, h, g, b, w, x, k, _, S, E, R, D, F, B, z, U, j, H, X, J, re, ne, ee, oe, le), ie;
}
var PP = { kernelName: tn, backendName: "wasm", setupFunc: ore, kernelFunc: nre };
var OP;
function sre(r) {
OP = r.wasm.cwrap(rn, 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"]);
}
function are(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o;
if (n.dtype !== "float32")
throw new Error(`Tensor x must have dtype float32, got ${n.dtype}`);
if (s.dtype !== "float32")
throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);
let u = C.computeConv3DInfo(n.shape, s.shape, a, p, i), c = t10.makeOutput(u.outShape, n.dtype);
return OP(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c;
}
var MP = { kernelName: rn, backendName: "wasm", setupFunc: sre, kernelFunc: are };
var LP;
function ire(r) {
LP = r.wasm.cwrap(za, 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"]);
}
function ure(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o;
if (n.dtype !== "float32")
throw new Error(`Tensor dy must have dtype float32, got ${n.dtype}`);
if (s.dtype !== "float32")
throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);
let u = C.computeConv3DInfo(n.shape, p, a, 1, i), c = t10.makeOutput(u.filterShape, s.dtype);
return LP(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c;
}
var BP = { kernelName: za, backendName: "wasm", setupFunc: ire, kernelFunc: ure };
var zP;
function pre(r) {
zP = r.wasm.cwrap(on, 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"]);
}
function cre(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o;
if (n.dtype !== "float32")
throw new Error(`Tensor dy must have dtype float32, got ${n.dtype}`);
if (s.dtype !== "float32")
throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);
let u = C.computeConv3DInfo(p, s.shape, i, 1, a), c = t10.makeOutput(u.inShape, n.dtype);
return zP(t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c;
}
var VP = { kernelName: on, backendName: "wasm", setupFunc: pre, kernelFunc: cre };
var WP = Ce(nn);
var UP = Ce(sn);
var xv;
(function(r) {
r[r.bilinear = 0] = "bilinear", r[r.nearest = 1] = "nearest";
})(xv || (xv = {}));
var GP;
function lre(r) {
GP = r.wasm.cwrap(pn, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
}
function mre(r) {
let { backend: e, inputs: t10, attrs: o } = r, { method: n, extrapolationValue: s, cropSize: a } = o, { image: i, boxes: p, boxInd: u } = t10, c = p.shape[0], [l, m] = a, d = [c, l, m, i.shape[3]], f = e.dataIdMap.get(i.dataId), h;
i.dtype !== "float32" && (h = Pr({ backend: e, inputs: { x: i }, attrs: { dtype: "float32" } }), f = e.dataIdMap.get(h.dataId));
let g = f.id, x = e.dataIdMap.get(p.dataId).id, b = e.dataIdMap.get(u.dataId).id, w = e.makeOutput(d, "float32"), S = e.dataIdMap.get(w.dataId).id, k = new Uint8Array(new Int32Array(i.shape).buffer);
return GP(g, x, b, c, k, l, m, xv[n], s, S), h != null && e.disposeData(h.dataId), w;
}
var HP = { kernelName: pn, backendName: "wasm", setupFunc: lre, kernelFunc: mre };
var KP;
function dre(r) {
KP = r.wasm.cwrap(an, null, ["number", "number", "number", "number", "number", "number"]);
}
function fre(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length;
y.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumprod does not support ${n.dtype} tensors in the WASM backend`);
let u = C.getAxesPermutation([s], p), c = n;
u !== null && (c = mo({ inputs: { x: n }, attrs: { perm: u }, backend: t10 }));
let l = C.getInnerMostAxes(1, p)[0];
C.assertAxesAreInnerMostDims("cumprod", [l], p);
let m = t10.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t10.dataIdMap.get(c.dataId).id, h = t10.dataIdMap.get(m.dataId).id;
KP(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]);
let g = m;
if (u !== null) {
let x = C.getUndoAxesPermutation(u);
g = mo({ inputs: { x: m }, attrs: { perm: x }, backend: t10 }), t10.disposeData(c.dataId), t10.disposeData(m.dataId);
}
return g;
}
var qP = { kernelName: an, backendName: "wasm", setupFunc: dre, kernelFunc: fre };
var jP;
function hre(r) {
jP = r.wasm.cwrap(un, null, ["number", "number", "number", "number", "number", "number"]);
}
function gre(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length;
y.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumsum does not support ${n.dtype} tensors in the WASM backend`);
let u = C.getAxesPermutation([s], p), c = n;
u !== null && (c = mo({ inputs: { x: n }, attrs: { perm: u }, backend: t10 }));
let l = C.getInnerMostAxes(1, p)[0];
C.assertAxesAreInnerMostDims("cumsum", [l], p);
let m = t10.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t10.dataIdMap.get(c.dataId).id, h = t10.dataIdMap.get(m.dataId).id;
jP(f, a ? 1 : 0, i ? 1 : 0, d, h, we[n.dtype]);
let g = m;
if (u !== null) {
let x = C.getUndoAxesPermutation(u);
g = mo({ inputs: { x: m }, attrs: { perm: x }, backend: t10 }), t10.disposeData(c.dataId), t10.disposeData(m.dataId);
}
return g;
}
var XP = { kernelName: un, backendName: "wasm", setupFunc: hre, kernelFunc: gre };
var YP;
function xre(r) {
YP = r.wasm.cwrap("DenseBincount", null, ["number", "array", "number", "number", "boolean", "number", "number", "boolean", "number"]);
}
function yre(r) {
let { backend: e, inputs: t10, attrs: o } = r, { x: n, weights: s } = t10, { size: a, binaryOutput: i } = o, p = s.shape.reduce((m, d) => m * d, 1) !== 0, u = n.shape.length === 1 ? [a] : [n.shape[0], a], c = e.makeOutput(u, s.dtype);
function l(m) {
return e.dataIdMap.get(m.dataId).id;
}
return YP(l(n), new Uint8Array(new Int32Array(n.shape).buffer), n.shape.length, a, p, l(s), we[s.dtype], i, l(c)), c;
}
var QP = { kernelName: Qs, backendName: "wasm", setupFunc: xre, kernelFunc: yre };
var ZP;
function bre(r) {
ZP = r.wasm.cwrap(cn, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
}
function Cre(r) {
let { backend: e, inputs: t10, attrs: o } = r, { x: n } = t10, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = e.makeOutput(f, "float32"), x = e.dataIdMap.get(n.dataId).id, b = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), w = new Uint8Array(new Int32Array(f).buffer), S = new Uint8Array(new Int32Array(y.computeStrides(f)).buffer), k = e.dataIdMap.get(h.dataId).id;
return ZP(x, s, a === "NHWC" ? 1 : 0, b, n.shape.length - 1, w, S, f.length, k), h;
}
var JP = { kernelName: cn, backendName: "wasm", setupFunc: bre, kernelFunc: Cre };
var eO;
function wre(r) {
eO = r.wasm.cwrap(ln, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Sre(r) {
let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l } = t10, m = u == null ? [1, 1] : u, d = C.computeConv2DInfo(n.shape, s.shape, p, m, c, l, true), f = d.filterHeight, h = d.filterWidth, g = d.padInfo.top, x = d.padInfo.right, b = d.padInfo.bottom, w = d.padInfo.left, S = d.dilationHeight, k = d.dilationWidth, _ = d.strideHeight, E = d.strideWidth, R = d.inChannels, D = d.outChannels, F = d.padInfo.type === "SAME" ? 1 : 0;
if (d.dataFormat !== "channelsLast")
throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);
let O = o.makeOutput(d.outShape, "float32"), M = o.dataIdMap.get(O.dataId).id;
return eO(a, n.shape[0], n.shape[1], n.shape[2], i, f, h, g, x, b, w, F, S, k, _, E, R, D, M), O;
}
var tO = { kernelName: ln, backendName: "wasm", setupFunc: wre, kernelFunc: Sre };
var rO;
function Ire(r) {
rO = r.wasm.cwrap("Diag", null, ["number", "number", "number", "number"]);
}
function vre(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t10.makeOutput([...o.shape, ...o.shape], o.dtype);
return rO(t10.dataIdMap.get(o.dataId).id, we[o.dtype], n, t10.dataIdMap.get(s.dataId).id), s;
}
var oO = { kernelName: Zs, backendName: "wasm", setupFunc: Ire, kernelFunc: vre };
var nO;
function kre(r) {
nO = r.wasm.cwrap(mn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Nre(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o;
if (n.dtype !== s.dtype)
throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${n.dtype} and ${s.dtype}`);
let u = C.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = t10.makeOutput(u.outShape, n.dtype);
return nO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(c.dataId).id, we[n.dtype], u.batchSize, u.inChannels, u.inHeight, u.inWidth, u.outHeight, u.outWidth, u.strideHeight, u.strideWidth, u.dilationHeight, u.dilationWidth, u.filterHeight, u.filterWidth, u.padInfo.top, u.padInfo.left), c;
}
var sO = { kernelName: mn, backendName: "wasm", setupFunc: kre, kernelFunc: Nre };
var aO;
function Tre(r) {
aO = r.wasm.cwrap(Ai, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function _re(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o;
if (n.dtype !== s.dtype || n.dtype !== a.dtype)
throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);
let c = C.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = t10.makeOutput(s.shape, s.dtype);
return aO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(a.dataId).id, t10.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l;
}
var iO = { kernelName: Ai, backendName: "wasm", setupFunc: Tre, kernelFunc: _re };
var uO;
function $re(r) {
uO = r.wasm.cwrap(Di, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Ere(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o;
if (n.dtype !== s.dtype || n.dtype !== a.dtype)
throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);
let c = C.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = t10.makeOutput(n.shape, n.dtype);
return uO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(a.dataId).id, t10.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l;
}
var pO = { kernelName: Di, backendName: "wasm", setupFunc: $re, kernelFunc: Ere };
var cO = Ce(fn);
var lO;
function Rre(r) {
lO = r.wasm.cwrap(Va, null, ["number", "number", "number"]);
}
function Dre(r) {
let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e, s = t10.makeOutput(n.shape, "float32"), a = (i) => t10.dataIdMap.get(i.dataId).id;
return lO(a(n), a(o), a(s)), s;
}
var mO = { kernelName: Va, backendName: "wasm", setupFunc: Rre, kernelFunc: Dre };
var Are = false;
var dO = Je(hn, Are, "bool");
var fO = Ce(gn, "float32");
function Pg(r) {
let { inputs: e, attrs: t10, backend: o } = r, { input: n } = e, { dim: s } = t10, a = n.shape.length, i = n.shape.slice(), p = s;
return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), zt({ inputs: { x: n }, backend: o, attrs: { shape: i } });
}
var hO = { kernelName: Js, backendName: "wasm", kernelFunc: Pg };
var gO = Ce(xn, "float32");
function yv(r) {
let { attrs: { shape: e, value: t10, dtype: o }, backend: n } = r, s = n.makeOutput(e, o);
return n.typedArrayFromHeap(s).fill(t10), s;
}
var xO = { kernelName: ea, backendName: "wasm", kernelFunc: yv };
var yO;
function Fre(r) {
yO = r.wasm.cwrap(yn, null, ["number", "number", "number", "number", "number", "number"]);
}
function Pre(r) {
let { inputs: e, backend: t10 } = r, { image: o } = e, n = t10.makeOutput(o.shape, o.dtype), s = t10.dataIdMap.get(o.dataId).id, a = t10.dataIdMap.get(n.dataId).id, [i, p, u, c] = o.shape;
return yO(s, i, p, u, c, a), n;
}
var bO = { kernelName: yn, backendName: "wasm", kernelFunc: Pre, setupFunc: Fre };
var CO = Ce(bn);
var Ore = false;
var wO = Je(Cn, Ore);
var SO;
function Mre(r) {
SO = r.wasm.cwrap(wn, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function Lre(r) {
let { backend: e, inputs: t10, attrs: o } = r, { varianceEpsilon: n } = o, { x: s, mean: a, variance: i, offset: p, scale: u } = t10, c = e.dataIdMap.get(s.dataId).id, l = e.dataIdMap.get(a.dataId).id, m = e.dataIdMap.get(i.dataId).id, d = p != null ? e.dataIdMap.get(p.dataId).id : 0, f = u != null ? e.dataIdMap.get(u.dataId).id : 0, h = e.makeOutput(s.shape, s.dtype);
if (y.sizeFromShape(s.shape) === 0)
return h;
let g = e.dataIdMap.get(h.dataId).id;
return SO(c, l, m, d, f, n, g), h;
}
var IO = { kernelName: wn, backendName: "wasm", setupFunc: Mre, kernelFunc: Lre };
var vO;
function Bre(r) {
vO = r.wasm.cwrap(Co, 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 zre(r) {
let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t10, h = C.computeConv2DInfo(n.shape, s.shape, p, c, u, m), g = bu[d];
if (g == null)
throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);
let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, w = h.outChannels, S = 0;
if (a != null) {
let ee = o.dataIdMap.get(a.dataId);
if (ee.shape.length !== 1)
throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);
if (ee.shape[0] !== w)
throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${w})`);
S = ee.id;
}
let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, R = h.padInfo.right, D = h.padInfo.bottom, F = h.padInfo.left, O = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, B = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, j = h.batchSize, H = h.inHeight, X = h.inWidth;
if (l !== "NHWC")
throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);
let J = o.makeOutput(h.outShape, "float32"), re = o.dataIdMap.get(J.dataId).id, ne = i == null ? 0 : o.dataIdMap.get(i.dataId).id;
return vO(x, j, H, X, b, k, _, S, E, R, D, F, U, O, M, L, B, z, w, g, ne, f || 0, re), J;
}
var kO = { kernelName: Co, backendName: "wasm", setupFunc: Bre, kernelFunc: zre };
var NO;
function Vre(r) {
NO = r.wasm.cwrap(wo, 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 Wre(r) {
let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t10, h = C.computeConv2DInfo(n.shape, s.shape, p, c, u, m, true), g = bu[d];
if (g == null)
throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, w = h.outChannels, S = 0;
if (a != null) {
let ee = o.dataIdMap.get(a.dataId);
if (ee.shape.length !== 1)
throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);
if (ee.shape[0] !== w)
throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${w})`);
S = ee.id;
}
let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, R = h.padInfo.right, D = h.padInfo.bottom, F = h.padInfo.left, O = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, B = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, j = h.batchSize, H = h.inHeight, X = h.inWidth;
if (l !== "NHWC")
throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);
let J = o.makeOutput(h.outShape, "float32"), re = o.dataIdMap.get(J.dataId).id, ne = i == null ? 0 : o.dataIdMap.get(i.dataId).id;
return NO(x, j, H, X, b, k, _, S, E, R, D, F, U, O, M, L, B, z, w, g, ne, f || 0, re), J;
}
var TO = { kernelName: wo, backendName: "wasm", setupFunc: Vre, kernelFunc: Wre };
var _O;
function Ure(r) {
_O = r.wasm.cwrap(Sn, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
}
function Gre(r) {
let { backend: e, inputs: t10 } = r, { params: o, indices: n } = t10, [s, a, i, p] = of.prepareAndValidate(o, n), u = e.makeOutput(s, o.dtype);
if (a === 0)
return u;
let c = n.shape, l = c[c.length - 1], d = e.dataIdMap.get(o.dataId).id, h = e.dataIdMap.get(n.dataId).id, g = new Uint8Array(new Int32Array(p).buffer), x = e.dataIdMap.get(u.dataId).id;
return _O(d, we[o.dtype], h, a, l, i, g, x), u;
}
var $O = { kernelName: Sn, backendName: "wasm", setupFunc: Ure, kernelFunc: Gre };
var EO;
function Hre(r) {
EO = r.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
}
function Kre(r) {
let { backend: e, inputs: t10, attrs: o } = r, { x: n, indices: s } = t10, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = e.readSync(s.dataId), c = n.shape[p];
for (let D = 0; D < u.length; ++D) {
let F = u[D];
y.assert(F <= c - 1 && F >= 0, () => `GatherV2: the index value ${F} is not in [0, ${c - 1}]`);
}
let l = C.segment_util.collectGatherOpShapeInfo(n, s, p, i), m = zt({ inputs: { x: n }, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] }, backend: e }), d = y.sizeFromShape(s.shape), f = zt({ inputs: { x: s }, attrs: { shape: [l.batchSize, d / l.batchSize] }, backend: e }), h = [l.batchSize, l.outerSize, d / l.batchSize, l.sliceSize], g = e.makeOutput(h, n.dtype);
if (y.sizeFromShape(n.shape) === 0)
return g;
let x = m.shape.length - 1, w = e.dataIdMap.get(m.dataId).id, k = e.dataIdMap.get(f.dataId).id, _ = e.dataIdMap.get(g.dataId).id, E = new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer), R = new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);
return EO(w, we[n.dtype], E, x, k, l.batchSize, R, _), e.disposeData(m.dataId), e.disposeData(f.dataId), g.shape = l.outputShape, g;
}
var RO = { kernelName: ta, backendName: "wasm", setupFunc: Hre, kernelFunc: Kre };
var qre = false;
var DO = Je(In, qre, "bool");
var jre = false;
var AO = Je(vn, jre, "bool");
var FO = Ce(kn, "bool");
var PO = Ce(Nn, "bool");
var OO = Ce(Tn, "bool");
var MO;
function Xre(r) {
MO = r.wasm.cwrap(_n, null, ["number", "number", "number", "number"]);
}
function Yre(r) {
let { inputs: { x: e }, attrs: { alpha: t10 }, backend: o } = r, n = o.dataIdMap.get(e.dataId).id, s = o.makeOutput(e.shape, "float32");
if (y.sizeFromShape(e.shape) !== 0) {
let a = o.dataIdMap.get(s.dataId).id;
MO(n, we[e.dtype], t10, a);
}
return s;
}
var LO = { kernelName: _n, backendName: "wasm", setupFunc: Xre, kernelFunc: Yre };
var Qre = false;
var BO = Je($n, Qre, "bool");
var Zre = false;
var zO = Je(En, Zre, "bool");
var VO;
function Jre(r) {
VO = r.wasm.cwrap(Rn, null, ["number", "number", "number", "number"]);
}
function eoe(r) {
let { attrs: e, backend: t10 } = r, { start: o, stop: n, num: s } = e, a = Math.floor(s), i = t10.makeOutput([a], "float32");
return VO(t10.dataIdMap.get(i.dataId).id, o, n, a), i;
}
var WO = { kernelName: Rn, backendName: "wasm", setupFunc: Jre, kernelFunc: eoe };
var UO = Ce(Dn);
var GO = Ce(An);
var toe = false;
var HO = Je(Fn, toe, "bool");
var KO = Ce(Pn);
var roe = false;
var qO = Je(On, roe, "bool");
var ooe = false;
var jO = Je(m0, ooe, "bool");
var XO;
function noe(r) {
XO = r.wasm.cwrap(Mn, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function soe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o;
if (n.dtype !== "float32")
throw new Error("LRN error: x must have dtype float32");
let u = t10.makeOutput(n.shape, n.dtype);
return XO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(u.dataId).id, n.shape[3], s, a, i, p), u;
}
var YO = { kernelName: Mn, backendName: "wasm", setupFunc: noe, kernelFunc: soe };
var QO;
function aoe(r) {
QO = r.wasm.cwrap(Ua, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function ioe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o;
if (n.dtype !== "float32" || s.dtype !== "float32" || a.dtype !== "float32")
throw new Error("LRNGrad error: x, y, and dy must have dtype float32");
let l = t10.makeOutput(n.shape, n.dtype);
return QO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(a.dataId).id, t10.dataIdMap.get(l.dataId).id, a.shape[3], i, p, u, c), l;
}
var ZO = { kernelName: Ua, backendName: "wasm", setupFunc: aoe, kernelFunc: ioe };
var JO;
function uoe(r) {
JO = r.wasm.cwrap(Ln, null, ["number", "number", "number", "number"]);
}
function poe(r) {
let { backend: e, inputs: t10, attrs: o } = r, { reductionIndices: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e);
if (d) {
let w = e.dataIdMap.get(c.dataId).id;
u = c, p = w;
}
let f = u.shape.length;
C.assertAxesAreInnerMostDims("max", l, f);
let [h, g] = C.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let w = e.dataIdMap.get(b.dataId).id;
JO(p, we[a.dtype], x, w);
}
if (d && e.disposeData(c.dataId), s) {
let w = C.expandShapeToKeepDim(b.shape, m);
b.shape = w;
}
return b;
}
var eM = { kernelName: Ln, backendName: "wasm", setupFunc: uoe, kernelFunc: poe };
var coe = false;
var tM = Je(Bn, coe);
var rM;
function loe(r) {
rM = r.wasm.cwrap(zn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function moe(r) {
let { inputs: e, attrs: t10, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id;
y.assert(n.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);
let { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t10, c = C.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.dilationHeight, b = c.dilationWidth, w = c.strideHeight, S = c.strideWidth, k = c.inChannels, _ = c.outChannels;
if (c.dataFormat !== "channelsLast")
throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
let E = o.makeOutput(c.outShape, "float32"), R = o.dataIdMap.get(E.dataId).id;
return rM(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, w, S, k, _, R), E;
}
var oM = { kernelName: zn, backendName: "wasm", setupFunc: loe, kernelFunc: moe };
var nM;
function doe(r) {
nM = r.wasm.cwrap("MaxPool3D", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function foe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = C.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.makeOutput(c.outShape, n.dtype);
return nM(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l;
}
var sM = { kernelName: ra, backendName: "wasm", setupFunc: doe, kernelFunc: foe };
var aM;
function hoe(r) {
aM = r.wasm.cwrap("MaxPool3DGrad", 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 goe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o, c = C.computePool3DInfo(s.shape, a, i, 1, p, u), l = t10.makeOutput(s.shape, s.dtype);
return aM(t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(l.dataId).id, c.batchSize, c.inChannels, c.inDepth, c.inHeight, c.inWidth, c.outDepth, c.outHeight, c.outWidth, c.strideDepth, c.strideHeight, c.strideWidth, c.dilationDepth, c.dilationHeight, c.dilationWidth, c.effectiveFilterDepth, c.effectiveFilterHeight, c.effectiveFilterWidth, c.padInfo.front, c.padInfo.top, c.padInfo.left), l;
}
var iM = { kernelName: Li, backendName: "wasm", setupFunc: hoe, kernelFunc: goe };
var uM;
function xoe(r) {
uM = r.wasm.cwrap(Vn, null, ["number, number, number"]);
}
function yoe(r) {
let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e), f = l;
if (d) {
let S = e.dataIdMap.get(c.dataId).id;
S !== i && (u = c, p = S, f = C.getInnerMostAxes(f.length, u.shape.length));
}
C.assertAxesAreInnerMostDims("mean", f, u.shape.length);
let [h, g] = C.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = u;
u.dtype !== "float32" && (b = Pr({ backend: e, inputs: { x: u }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(b.dataId).id);
let w = e.makeOutput(h, "float32");
if (y.sizeFromShape(u.shape) !== 0) {
let S = e.dataIdMap.get(w.dataId).id;
uM(p, x, S);
}
if (d && e.disposeData(c.dataId), s) {
let S = C.expandShapeToKeepDim(w.shape, m);
w.shape = S;
}
return u.dtype !== "float32" && e.disposeData(b.dataId), w;
}
var pM = { kernelName: Vn, backendName: "wasm", setupFunc: xoe, kernelFunc: yoe };
var cM;
function boe(r) {
cM = r.wasm.cwrap(Wn, null, ["number", "number", "number", "number"]);
}
function Coe(r) {
let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e);
if (d) {
let w = e.dataIdMap.get(c.dataId).id;
w !== i && (u = c, p = w);
}
let f = u.shape.length;
C.assertAxesAreInnerMostDims("min", l, f);
let [h, g] = C.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let w = e.dataIdMap.get(b.dataId).id;
cM(p, we[a.dtype], x, w);
}
if (d && e.disposeData(c.dataId), s) {
let w = C.expandShapeToKeepDim(b.shape, m);
b.shape = w;
}
return b;
}
var lM = { kernelName: Wn, backendName: "wasm", setupFunc: boe, kernelFunc: Coe };
var woe = false;
var mM = Je(Un, woe);
var bv;
(function(r) {
r[r.reflect = 0] = "reflect", r[r.symmetric = 1] = "symmetric";
})(bv || (bv = {}));
var dM;
function Soe(r) {
dM = r.wasm.cwrap(Gn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function Ioe(r) {
let { inputs: { x: e }, backend: t10, attrs: { paddings: o, mode: n } } = r, s = o.map((f, h) => f[0] + e.shape[h] + f[1]), a = t10.dataIdMap.get(e.dataId).id, i = t10.makeOutput(s, e.dtype), p = t10.dataIdMap.get(i.dataId).id, u = new Uint8Array(new Int32Array(e.shape).buffer), c = o.map((f) => f[0]), l = o.map((f) => f[1]), m = new Uint8Array(new Int32Array(c).buffer), d = new Uint8Array(new Int32Array(l).buffer);
return dM(a, u, e.shape.length, we[e.dtype], m, d, bv[n], p), i;
}
var fM = { kernelName: Gn, backendName: "wasm", kernelFunc: Ioe, setupFunc: Soe };
var hM;
function voe(r) {
hM = r.wasm.cwrap(bs, null, ["number", "number", "number", "number"]);
}
function Cv(r) {
let { backend: e, inputs: { logits: t10 }, attrs: { dim: o } } = r, n = e.dataIdMap.get(t10.dataId).id, s = e.makeOutput(t10.shape, t10.dtype), a = e.dataIdMap.get(s.dataId).id, i = t10.shape[o], p = y.sizeFromShape(t10.shape) / i;
return y.sizeFromShape(s.shape) === 0 || hM(n, a, i, p), s;
}
var gM = { kernelName: bs, backendName: "wasm", setupFunc: voe, kernelFunc: Cv };
var xM;
function koe(r) {
xM = r.wasm.cwrap(Hn, null, ["number", "number", "number", "number", "number", "number"]);
}
function Noe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o;
if (n.dtype !== "float32")
throw new Error(`Tensor logits must have dtype float32, got ${n.dtype}`);
let p = i ? n : Cv({ inputs: { logits: n }, backend: t10, attrs: { dim: n.shape.length - 1 } }), [u, c] = p.shape, l = t10.makeOutput([u, s], "int32");
return xM(t10.dataIdMap.get(p.dataId).id, u, c, s, a, t10.dataIdMap.get(l.dataId).id), i || t10.disposeData(p.dataId), l;
}
var yM = { kernelName: Hn, backendName: "wasm", setupFunc: koe, kernelFunc: Noe };
var Toe = true;
var bM = Je(Kn, Toe);
var CM = Ce(oa);
function Hc(r, e) {
let t10 = new Int32Array(r.wasm.HEAPU8.buffer, e, 4), o = t10[0], n = t10[1], s = t10[2], a = t10[3];
return r.wasm._free(e), { pSelectedIndices: o, selectedSize: n, pSelectedScores: s, pValidOutputs: a };
}
var wM;
function _oe(r) {
wM = r.wasm.cwrap(jn, "number", ["number", "number", "number", "number", "number"]);
}
function $oe(r) {
let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a } = o, { boxes: i, scores: p } = t10, u = e.dataIdMap.get(i.dataId).id, c = e.dataIdMap.get(p.dataId).id, l = wM(u, c, s, n, a), { pSelectedIndices: m, selectedSize: d, pSelectedScores: f, pValidOutputs: h } = Hc(e, l);
return e.wasm._free(f), e.wasm._free(h), e.makeOutput([d], "int32", m);
}
var SM = { kernelName: jn, backendName: "wasm", setupFunc: _oe, kernelFunc: $oe };
var IM;
function Eoe(r) {
IM = r.wasm.cwrap(Ha, "number", ["number", "number", "number", "number", "number", "bool"]);
}
function Roe(r) {
let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, padToMaxOutputSize: i } = o, { boxes: p, scores: u } = t10, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = IM(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Hc(e, m);
e.wasm._free(h);
let x = e.makeOutput([f], "int32", d), b = e.makeOutput([], "int32", g);
return [x, b];
}
var vM = { kernelName: Ha, backendName: "wasm", setupFunc: Eoe, kernelFunc: Roe };
var kM;
function Doe(r) {
kM = r.wasm.cwrap(Xn, "number", ["number", "number", "number", "number", "number", "number"]);
}
function Aoe(r) {
let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, softNmsSigma: i } = o, { boxes: p, scores: u } = t10, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = kM(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = Hc(e, m);
e.wasm._free(g);
let x = e.makeOutput([f], "int32", d), b = e.makeOutput([f], "float32", h);
return [x, b];
}
var NM = { kernelName: Xn, backendName: "wasm", setupFunc: Doe, kernelFunc: Aoe };
var Foe = false;
var TM = Je(qn, Foe, "bool");
var _M;
function Poe(r) {
_M = r.wasm.cwrap(Yn, null, ["number", "number", "number", "number", "number"]);
}
function Ooe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = t10.makeOutput([...n.shape, a], s), c = t10.dataIdMap.get(u.dataId).id, m = t10.dataIdMap.get(n.dataId).id;
return _M(m, a, i, p, c), u;
}
var $M = { kernelName: Yn, backendName: "wasm", setupFunc: Poe, kernelFunc: Ooe };
function Moe(r) {
let { inputs: { x: e }, backend: t10 } = r, o = t10.makeOutput(e.shape, e.dtype);
return t10.typedArrayFromHeap(o).fill(1), o;
}
var EM = { kernelName: na, backendName: "wasm", kernelFunc: Moe };
function Loe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
if (e.length === 1)
return Pg({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
let s = e[0].shape, a = e[0].dtype;
e.forEach((c) => {
y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
});
let i = [], p = e.map((c) => {
let l = Pg({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
return i.push(l), l;
}), u = gv({ inputs: p, backend: t10, attrs: { axis: n } });
return i.forEach((c) => t10.disposeData(c.dataId)), u;
}
var RM = { kernelName: sa, backendName: "wasm", kernelFunc: Loe };
var DM;
function Boe(r) {
DM = r.wasm.cwrap(Qn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function zoe(r) {
let { inputs: { x: e }, backend: t10, attrs: { paddings: o, constantValue: n } } = r, s = o.map((h, g) => h[0] + e.shape[g] + h[1]);
if (y.sizeFromShape(e.shape) === 0)
return yv({ backend: t10, attrs: { shape: s, value: n, dtype: e.dtype } });
let a = t10.dataIdMap.get(e.dataId).id, i = t10.makeOutput(s, e.dtype), u = t10.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(e.shape).buffer), l = o.map((h) => h[0]), m = o.map((h) => h[1]), d = new Uint8Array(new Int32Array(l).buffer), f = new Uint8Array(new Int32Array(m).buffer);
return DM(a, c, e.shape.length, we[e.dtype], d, f, n, u), i;
}
var Og = { kernelName: Qn, backendName: "wasm", kernelFunc: zoe, setupFunc: Boe };
var Voe = false;
var AM = Je(Zn, Voe);
var FM;
function Woe(r) {
FM = r.wasm.cwrap(Jn, null, ["number", "number", "number"]);
}
function Uoe(r) {
let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = t10.dataIdMap.get(o.dataId).id, a = t10.dataIdMap.get(n.dataId).id, i = s, p = o, u = p;
p.dtype !== "float32" && (u = Pr({ backend: t10, inputs: { x: o }, attrs: { dtype: "float32" } }), i = t10.dataIdMap.get(u.dataId).id);
let c = t10.makeOutput(o.shape, "float32"), l = t10.dataIdMap.get(c.dataId).id;
return FM(i, a, l), p.dtype !== "float32" && t10.disposeData(u.dataId), c;
}
var PM = { kernelName: Jn, backendName: "wasm", setupFunc: Woe, kernelFunc: Uoe };
var OM;
function Goe(r) {
OM = r.wasm.cwrap(es, null, ["number", "number", "number", "number"]);
}
function Hoe(r) {
let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e), f = l;
if (d) {
let w = e.dataIdMap.get(c.dataId).id;
w !== i && (u = c, p = w, f = C.getInnerMostAxes(f.length, u.shape.length));
}
C.assertAxesAreInnerMostDims("prod", f, u.shape.length);
let [h, g] = C.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let w = e.dataIdMap.get(b.dataId).id;
OM(p, x, we[b.dtype], w);
}
if (d && e.disposeData(c.dataId), s) {
let w = C.expandShapeToKeepDim(b.shape, m);
b.shape = w;
}
return b;
}
var MM = { kernelName: es, backendName: "wasm", setupFunc: Goe, kernelFunc: Hoe };
var Koe = (r) => {
let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = ap(o, n, s, a), p = e.makeOutput([i.length], a);
return e.typedArrayFromHeap(p).set(i), p;
};
var LM = { kernelName: aa, backendName: "wasm", kernelFunc: Koe };
var qoe = true;
var BM = Je(dn, qoe);
var zM = Ce(ts);
var VM = Ce(rs);
var WM = Ce(ss);
var UM;
function joe(r) {
UM = r.wasm.cwrap(ns, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Xoe(r) {
let { backend: e, inputs: t10, attrs: o } = r, { images: n } = t10, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.dataIdMap.get(n.dataId), g;
h.dtype !== "float32" && (g = Pr({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), h = e.dataIdMap.get(g.dataId));
let x = h.id, b = e.makeOutput(f, "float32");
if (y.sizeFromShape(n.shape) === 0)
return b;
let w = e.dataIdMap.get(b.dataId).id;
return UM(x, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, w), g != null && e.disposeData(g.dataId), b;
}
var GM = { kernelName: ns, backendName: "wasm", setupFunc: joe, kernelFunc: Xoe };
var HM;
function Yoe(r) {
HM = r.wasm.cwrap(qa, null, ["number", "number", "number", "array", "array", "boolean"]);
}
function Qoe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = t10.makeOutput(n.shape, "float32"), p = t10.dataIdMap.get(n.dataId), u;
return p.dtype !== "float32" && (u = Pr({ backend: t10, inputs: { x: n }, attrs: { dtype: "float32" } }), p = t10.dataIdMap.get(u.dataId)), HM(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && t10.disposeData(u.dataId), i;
}
var KM = { kernelName: qa, backendName: "wasm", setupFunc: Yoe, kernelFunc: Qoe };
var qM;
function Zoe(r) {
qM = r.wasm.cwrap(os, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Joe(r) {
let { backend: e, inputs: t10, attrs: o } = r, { images: n } = t10, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.makeOutput(f, "float32");
if (y.sizeFromShape(n.shape) === 0)
return h;
let g = e.dataIdMap.get(n.dataId), x;
g.dtype !== "float32" && (x = Pr({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), g = e.dataIdMap.get(x.dataId));
let b = g.id, w = e.dataIdMap.get(h.dataId).id;
return qM(b, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, w), x != null && e.disposeData(x.dataId), h;
}
var jM = { kernelName: os, backendName: "wasm", setupFunc: Zoe, kernelFunc: Joe };
var XM;
function ene(r) {
XM = r.wasm.cwrap(Ka, null, ["number", "number", "number", "array", "array", "boolean"]);
}
function tne(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = t10.makeOutput(n.shape, "float32"), p = t10.dataIdMap.get(n.dataId), u;
return p.dtype !== "float32" && (u = Pr({ backend: t10, inputs: { x: n }, attrs: { dtype: "float32" } }), p = t10.dataIdMap.get(u.dataId)), XM(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(i.dataId).id, new Uint8Array(new Int32Array(n.shape).buffer), new Uint8Array(new Int32Array(s.shape).buffer), a), u != null && t10.disposeData(u.dataId), i;
}
var YM = { kernelName: Ka, backendName: "wasm", setupFunc: ene, kernelFunc: tne };
var QM;
function rne(r) {
QM = r.wasm.cwrap(as, null, ["number", "array", "number", "array", "number", "number"]);
}
function one(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = y.parseAxisParam(s, n.shape);
if (n.shape.length === 0)
return vp({ inputs: { x: n }, backend: t10 });
let i = t10.makeOutput(n.shape, n.dtype), p = t10.dataIdMap.get(n.dataId).id, u = t10.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(a).buffer), l = new Uint8Array(new Int32Array(n.shape).buffer);
QM(p, c, a.length, l, n.shape.length, u);
let m = zt({ inputs: { x: i }, attrs: { shape: n.shape }, backend: t10 });
return t10.disposeData(i.dataId), m;
}
var ZM = { kernelName: as, backendName: "wasm", kernelFunc: one, setupFunc: rne };
var JM;
function nne(r) {
JM = r.wasm.cwrap(_s, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function sne(r) {
let { inputs: e, backend: t10, attrs: o } = r, { image: n } = e, { radians: s, fillValue: a, center: i } = o, p = t10.makeOutput(n.shape, n.dtype), u = t10.dataIdMap.get(n.dataId).id, c = t10.dataIdMap.get(p.dataId).id, [l, m, d, f] = n.shape, [h, g] = C.getImageCenter(i, m, d), x = a === 0, b = 255, w = typeof a == "number" ? [a, a, a, x ? 0 : b] : [...a, b], S = new Uint8Array(new Int32Array(w).buffer);
return JM(u, l, m, d, f, s, h, g, S, w.length, c), p;
}
var eL = { kernelName: _s, backendName: "wasm", kernelFunc: sne, setupFunc: nne };
var tL = Ce(is);
var rL = Ce(us);
var oL;
function ane(r) {
oL = r.wasm.cwrap(ps, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function ine(r) {
let { backend: e, inputs: t10, attrs: o } = r, { indices: n, updates: s } = t10, { shape: a } = o, i = e.makeOutput(a, s.dtype);
if (y.sizeFromShape(a) === 0)
return i;
let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = pu.calculateShapes(s, n, a), f = e.dataIdMap.get(n.dataId).id, g = e.dataIdMap.get(s.dataId).id, x = new Uint8Array(new Int32Array(l).buffer), b = e.dataIdMap.get(i.dataId).id;
return oL(f, g, we[s.dtype], p, u, c, x, m, b), i;
}
var nL = { kernelName: ps, backendName: "wasm", setupFunc: ane, kernelFunc: ine };
var sL;
function une(r) {
sL = r.wasm.cwrap(ls, null, ["number", "number", "number", "number", "number", "number", "bool", "number"]);
}
function pne(r) {
let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o;
if (n.dtype !== s.dtype)
throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${n.dtype} and ${s.dtype}`);
let i = t10.makeOutput(s.shape, "int32");
function p(u) {
return t10.dataIdMap.get(u.dataId).id;
}
return sL(p(n), p(s), n.shape[0], n.shape[1], s.shape[1], we[n.dtype], a === "left", p(i)), i;
}
var aL = { kernelName: ls, backendName: "wasm", setupFunc: une, kernelFunc: pne };
var iL;
function cne(r) {
iL = r.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
}
function lne(r) {
let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = t10.dataIdMap.get(o.dataId).id, i = t10.dataIdMap.get(n.dataId).id, p = t10.dataIdMap.get(s.dataId).id, u = t10.makeOutput(n.shape, n.dtype), c = t10.dataIdMap.get(u.dataId).id, l = o.shape.length, m = n.shape.length, d = l === 0 || l > 1 || m === 1 ? 1 : y.sizeFromShape(n.shape.slice(1));
return iL(a, i, p, d, c), u;
}
var uL = { kernelName: ua, backendName: "wasm", kernelFunc: lne, setupFunc: cne };
var pL = Ce(ms);
var cL;
function mne(r) {
cL = r.wasm.cwrap(hs, null, ["number", "number"]);
}
function dne(r) {
let { backend: e, inputs: { x: t10 } } = r, o = e.dataIdMap.get(t10.dataId).id, n = e.makeOutput(t10.shape, t10.dtype), s = e.dataIdMap.get(n.dataId).id;
return y.sizeFromShape(n.shape) === 0 || cL(o, s), n;
}
var lL = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: mne, kernelFunc: dne };
var mL = Ce(fs);
var dL = Ce(ds);
var fL = Ce(gs);
function fne(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o, i = y.sizeFromShape(s), p = [[0, 0]];
p.push(...a);
for (let _ = 1 + s.length; _ < n.shape.length; ++_)
p.push([0, 0]);
let u = Og.kernelFunc({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), c = C.getReshaped(u.shape, s, i, false), l = C.getPermuted(c.length, s.length, false), m = C.getReshapedPermuted(u.shape, s, i, false), h = zt({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), b = mo({ inputs: { x: h }, backend: t10, attrs: { perm: l } }), k = zt({ inputs: { x: b }, backend: t10, attrs: { shape: m } });
return t10.disposeData(u.dataId), t10.disposeData(h.dataId), t10.disposeData(b.dataId), k;
}
var hL = { kernelName: ca, backendName: "wasm", kernelFunc: fne };
var gL;
function hne(r) {
gL = r.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function gne(r) {
let { backend: e, inputs: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = t10, i = o.shape[0], p = o.shape[1], u = e.readSync(s.dataId)[0], c = [i + u, p], l = e.dataIdMap.get(o.dataId).id, m = e.dataIdMap.get(n.dataId).id, d = e.dataIdMap.get(a.dataId).id, f = e.makeOutput(c, o.dtype), h = e.dataIdMap.get(f.dataId).id, g = e.makeOutput(c.slice(0, 1), n.dtype), x = e.dataIdMap.get(g.dataId).id, b = e.makeOutput([u], "bool"), w = e.dataIdMap.get(b.dataId).id, S = e.makeOutput([i], o.dtype), k = e.dataIdMap.get(S.dataId).id, _ = e.makeOutput([4], "int32"), E = e.dataIdMap.get(_.dataId).id, R = gL(l, m, we[n.dtype], i, u, p, d, h, x, w, k, E), D = e.readSync(_.dataId), F;
switch (D[0]) {
case 1: {
F = C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(D[1]);
break;
}
case 2: {
F = C.getSparseFillEmptyRowsNegativeIndexErrorMessage(D[1], D[2]);
break;
}
case 3:
F = C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(D[1], D[2], D[3]);
break;
default:
F = "";
}
if (e.disposeData(_.dataId), F)
throw e.disposeData(f.dataId), e.disposeData(g.dataId), e.disposeData(b.dataId), e.disposeData(S.dataId), new Error(F);
let O = f, M = g;
return R !== c[0] && (O = Ao({ inputs: { x: f }, attrs: { begin: 0, size: [R, p] }, backend: e }), M = Ao({ inputs: { x: g }, attrs: { begin: 0, size: R }, backend: e }), e.disposeData(f.dataId), e.disposeData(g.dataId)), [O, M, b, S];
}
var xL = { kernelName: Vi, backendName: "wasm", setupFunc: hne, kernelFunc: gne };
var yL;
function xne(r) {
yL = r.wasm.cwrap(Xa, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function yne(r) {
let { backend: e, inputs: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = t10;
if (o.shape.length !== 2)
throw new Error(`Input indices should be a matrix but received shape
${o.shape}`);
if (n.shape.length !== 1)
throw new Error(`Input shape should be a vector but received shape
${n.shape}`);
if (s.shape.length !== 1)
throw new Error(`Target shape should be a vector but received shape ${s.shape}`);
let a = e.dataIdMap.get(o.dataId).id, i = e.dataIdMap.get(n.dataId).id, p = e.dataIdMap.get(s.dataId).id, u = o.shape[0], c = y.sizeFromShape(s.shape), l = e.makeOutput([u, c], o.dtype), m = e.dataIdMap.get(l.dataId).id, d = e.makeOutput([c], s.dtype), f = e.dataIdMap.get(d.dataId).id, h = e.makeOutput([3], "int32"), g = e.dataIdMap.get(h.dataId).id;
yL(a, i, p, u, m, f, g);
let x = e.readSync(h.dataId), b;
switch (x[0]) {
case 0: {
b = C.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(x[1], x[2]);
break;
}
case 1: {
b = C.getSparseReshapeNegativeOutputDimErrorMessage(x[1], x[2]);
break;
}
case 2:
b = C.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();
break;
case 3: {
let w = Array.from(e.readSync(n.dataId)), S = Array.from(e.readSync(d.dataId));
b = C.getSparseReshapeInputOutputMultipleErrorMessage(w, S);
break;
}
case 4: {
let w = Array.from(e.readSync(n.dataId)), S = Array.from(e.readSync(d.dataId));
b = C.getSparseReshapeInputOutputMismatchErrorMessage(w, S);
break;
}
default:
b = "";
}
if (e.disposeData(h.dataId), b)
throw e.disposeData(l.dataId), e.disposeData(d.dataId), new Error(b);
return [l, d];
}
var bL = { kernelName: Xa, backendName: "wasm", setupFunc: xne, kernelFunc: yne };
var CL;
function Mg(r) {
CL = r.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Lg(r, e) {
let { backend: t10, inputs: o } = r, { data: n, indices: s, segmentIds: a } = o, i = s.shape[0], p = t10.readSync(a.dataId, i - 1, i)[0], c = i > 0 ? p + 1 : 0;
if (c < 0)
throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
let l = n.shape.slice();
l[0] = c;
let m = t10.dataIdMap.get(n.dataId).id, d = t10.dataIdMap.get(s.dataId).id, f = t10.dataIdMap.get(a.dataId).id, h = t10.makeOutput(l, n.dtype), g = t10.dataIdMap.get(h.dataId).id, x = t10.makeOutput([4], "int32"), b = t10.dataIdMap.get(x.dataId).id;
CL(m, we[n.dtype], n.shape[0], d, f, g, b, e, 0);
let w = t10.readSync(x.dataId), S;
switch (w[0]) {
case 0: {
S = C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();
break;
}
case 1: {
S = C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();
break;
}
case 2:
S = C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(w[1], w[2]);
break;
case 3:
S = C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(w[1], w[2], w[3]);
break;
default:
S = "";
}
if (t10.disposeData(x.dataId), S)
throw t10.disposeData(h.dataId), new Error(S);
return h;
}
function bne(r) {
return Lg(r, true);
}
var wL = { kernelName: Wi, backendName: "wasm", setupFunc: Mg, kernelFunc: bne };
function Cne(r) {
return Lg(r, false);
}
var SL = { kernelName: Ui, backendName: "wasm", setupFunc: Mg, kernelFunc: Cne };
var IL;
function wne(r) {
IL = r.wasm.cwrap(Cs, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function Sne(r) {
let { backend: e, inputs: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = t10, { outputShape: i } = o, p = e.makeOutput(i, a.dtype);
if (y.sizeFromShape(i) === 0)
return p;
let { sliceRank: u, numUpdates: c, sliceSize: l, strides: m, outputSize: d } = C.calculateShapes(s, n, i), f = e.dataIdMap.get(n.dataId).id, h = e.dataIdMap.get(s.dataId).id, g = e.dataIdMap.get(a.dataId).id, x = new Uint8Array(new Int32Array(m).buffer), b = e.dataIdMap.get(p.dataId).id;
return IL(f, h, s.shape.length, g, we[a.dtype], u, c, l, x, d, b), p;
}
var vL = { kernelName: Cs, backendName: "wasm", setupFunc: wne, kernelFunc: Sne };
function Ine(r) {
let { inputs: e, attrs: t10, backend: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = t10, i = y.parseAxisParam(a, n.shape)[0], p = C.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice();
return p.map((l) => {
let m = [...c];
m[i] = l;
let d = Ao({ inputs: { x: n }, attrs: { begin: u, size: m }, backend: o });
return u[i] += l, d;
});
}
var kL = { kernelName: la, backendName: "wasm", kernelFunc: Ine };
var NL = Ce(xs);
var TL = Ce(Gi);
var vne = true;
var _L = Je(ws, vne);
var $L;
function kne(r) {
$L = r.wasm.cwrap(yo, null, ["number", "number", "number", "number"]);
}
function Nne(r) {
let { backend: e, inputs: t10, attrs: o } = r, { alpha: n } = o, { x: s } = t10, a = e.dataIdMap.get(s.dataId).id, i = e.makeOutput(s.shape, s.dtype), p = e.dataIdMap.get(i.dataId).id;
return $L(a, n, we[s.dtype], p), i;
}
var EL = { kernelName: yo, backendName: "wasm", setupFunc: kne, kernelFunc: Nne };
var RL;
function Tne(r) {
RL = r.wasm.cwrap(Ss, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
}
function _ne(r) {
let { backend: e, inputs: t10, attrs: o } = r, { x: n } = t10, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: w, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
if (h)
k = zt({ inputs: { x: n }, backend: e, attrs: { shape: f } });
else if (g || x) {
y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
let _ = ct.computeOutShape(b, w, S), E = Ao({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } });
k = zt({ inputs: { x: E }, backend: e, attrs: { shape: f } }), e.disposeData(E.dataId);
} else {
let _ = e.makeOutput(d, "float32"), E = e.dataIdMap.get(n.dataId).id, R = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), D = new Uint8Array(new Int32Array(b).buffer), F = new Uint8Array(new Int32Array(w).buffer), O = new Uint8Array(new Int32Array(S).buffer), M = new Uint8Array(new Int32Array(d).buffer), L = new Uint8Array(new Int32Array(y.computeStrides(d)).buffer), B = e.dataIdMap.get(_.dataId).id;
RL(E, R, n.shape.length, D, F, O, M, L, d.length, B), k = zt({ inputs: { x: _ }, backend: e, attrs: { shape: f } }), e.disposeData(_.dataId);
}
return k;
}
var DL = { kernelName: Ss, backendName: "wasm", setupFunc: Tne, kernelFunc: _ne };
function $ne(r) {
let { backend: e, inputs: t10, attrs: o } = r, { data: n, dataSplits: s } = t10, { separator: a, nGramWidths: i, leftPad: p, rightPad: u, padWidth: c, preserveShortSequences: l } = o, m = e.readSync(n.dataId), d = e.readSync(s.dataId), [f, h] = up(m, d, a, i, p, u, c, l), g = e.makeOutput([f.length], "string"), x = e.dataIdMap.get(g.dataId);
x.stringBytes = f;
let b = e.makeOutput(s.shape, "int32");
return e.typedArrayFromHeap(b).set(h), [g, b];
}
var AL = { kernelName: ma, backendName: "wasm", kernelFunc: $ne };
function Ene(r) {
let { backend: e, inputs: t10, attrs: o } = r, { input: n, delimiter: s } = t10, { skipEmpty: a } = o, i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c, l] = pp(i, p[0], a), m = c.length, d = e.makeOutput([m, 2], "int32");
e.typedArrayFromHeap(d).set(u);
let h = e.makeOutput([m], "string"), g = e.dataIdMap.get(h.dataId);
g.stringBytes = c;
let x = e.makeOutput([2], "int32");
return e.typedArrayFromHeap(x).set(l), [d, h, x];
}
var FL = { kernelName: Hi, backendName: "wasm", kernelFunc: Ene };
function Rne(r) {
let { backend: e, inputs: t10, attrs: o } = r, { input: n } = t10, { numBuckets: s } = o, a = e.readSync(n.dataId), i = cp(a, s), p = e.makeOutput(n.shape, "int32");
return e.typedArrayFromHeap(p).set(i), p;
}
var PL = { kernelName: Ki, backendName: "wasm", kernelFunc: Rne };
var Dne = true;
var OL = Je(Is, Dne);
var ML;
function Ane(r) {
ML = r.wasm.cwrap(ys, null, ["number", "number", "number", "number"]);
}
function Fne(r) {
let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e), f = l;
if (d) {
let w = e.dataIdMap.get(c.dataId).id;
w !== i && (u = c, p = w, f = C.getInnerMostAxes(f.length, u.shape.length));
}
C.assertAxesAreInnerMostDims("sum", f, u.shape.length);
let [h, g] = C.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
if (y.sizeFromShape(u.shape) !== 0) {
let w = e.dataIdMap.get(b.dataId).id;
ML(p, x, we[b.dtype], w);
}
if (d && e.disposeData(c.dataId), s) {
let w = C.expandShapeToKeepDim(b.shape, m);
b.shape = w;
}
return b;
}
var LL = { kernelName: ys, backendName: "wasm", setupFunc: Ane, kernelFunc: Fne };
var BL = Ce(vs);
var zL = Ce(ks);
var VL;
function Pne(r) {
VL = r.wasm.cwrap(cs, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number", "number"]);
}
function One(r) {
let { backend: e, inputs: t10, attrs: o } = r, { tensor: n, indices: s, updates: a } = t10, {} = o, i = e.makeOutput(n.shape, n.dtype);
if (y.sizeFromShape(n.shape) === 0)
return i;
let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = pu.calculateShapes(a, s, n.shape), f = e.dataIdMap.get(s.dataId).id, g = e.dataIdMap.get(a.dataId).id, b = e.dataIdMap.get(n.dataId).id, w = new Uint8Array(new Int32Array(l).buffer), S = e.dataIdMap.get(i.dataId).id;
return VL(f, g, we[a.dtype], p, u, c, w, m, S, b), i;
}
var WL = { kernelName: cs, backendName: "wasm", setupFunc: Pne, kernelFunc: One };
var UL;
function Mne(r) {
UL = r.wasm.cwrap(so, null, ["number", "array", "number", "array", "number", "number"]);
}
function Lne(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, s = t10.dataIdMap.get(n.dataId).id, { reps: a } = o, i = new Array(n.shape.length);
for (let m = 0; m < i.length; m++)
i[m] = n.shape[m] * a[m];
let p = new Uint8Array(new Int32Array(n.shape).buffer), u = new Uint8Array(new Int32Array(i).buffer), c = t10.makeOutput(i, n.dtype), l = t10.dataIdMap.get(c.dataId).id;
return UL(s, p, n.shape.length, u, i.length, we[c.dtype], l), c;
}
var GL = { kernelName: so, backendName: "wasm", setupFunc: Mne, kernelFunc: Lne };
var HL;
function Bne(r) {
HL = r.wasm.cwrap(Ns, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
}
var zne = ({ inputs: r, backend: e, attrs: t10 }) => {
let { x: o } = r, { k: n, sorted: s } = t10, a = e.dataIdMap.get(o.dataId).id, i = new Uint8Array(new Int32Array(o.shape).buffer), p = o.shape.slice();
p[p.length - 1] = n;
let u = e.makeOutput(p, o.dtype), c = e.dataIdMap.get(u.dataId).id, l = e.makeOutput(p, "int32"), m = e.dataIdMap.get(l.dataId).id;
return HL(a, i, o.shape.length, we[o.dtype], n, s, c, m), [u, l];
};
var KL = { kernelName: Ns, backendName: "wasm", setupFunc: Bne, kernelFunc: zne };
var qL;
function Vne(r) {
qL = r.wasm.cwrap(Ts, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "array", "number", "number", "number", "number", "number"]);
}
function Wne(r) {
let { backend: e, inputs: t10, attrs: o } = r, { image: n, transforms: s } = t10, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), b = new Uint8Array(new Int32Array(y.computeStrides(g)).buffer), w = e.makeOutput(g, n.dtype), S = e.dataIdMap.get(w.dataId).id, _ = e.dataIdMap.get(n.dataId).id, R = e.dataIdMap.get(s.dataId).id, D = a === "nearest" ? 1 : 2, F;
switch (i) {
case "constant":
F = 1;
break;
case "reflect":
F = 2;
break;
case "wrap":
F = 3;
break;
case "nearest":
F = 4;
break;
default:
F = 1;
break;
}
return qL(_, R, s.shape[0] > 1, c, f, h, d, m, l, x, n.shape.length - 1, b, g.length - 1, D, F, p, S), w;
}
var jL = { kernelName: Ts, backendName: "wasm", setupFunc: Vne, kernelFunc: Wne };
function Une(r) {
let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e, { outputValues: a, outputShape: i, indices: p } = lp(o.readSync(s.dataId), n, s.shape, s.dtype);
return [o.makeOutput(i, s.dtype, void 0, a), o.makeOutput([p.length], "int32", void 0, p)];
}
var XL = { kernelName: qi, backendName: "wasm", kernelFunc: Une };
function Gne(r) {
let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o;
s < 0 && (s += n.shape.length);
let a = n.shape[s], i = n.shape.length, p = new Array(i - 1), u = 0;
for (let d = 0; d < i; d++)
d !== s && (p[u++] = n.shape[d]);
let c = new Array(a), l = new Array(i).fill(0), m = n.shape.slice();
m[s] = 1;
for (let d = 0; d < c.length; d++)
l[s] = d, c[d] = Ao({ inputs: { x: n }, attrs: { begin: l, size: m }, backend: t10 });
return c.map(({ dataId: d, dtype: f }) => ({ dataId: d, dtype: f, shape: p }));
}
var YL = { kernelName: da, backendName: "wasm", kernelFunc: Gne };
function Hne(r) {
let { inputs: { x: e }, backend: t10 } = r, o = t10.makeOutput(e.shape, e.dtype);
return t10.typedArrayFromHeap(o).fill(0), o;
}
var QL = { kernelName: fa, backendName: "wasm", kernelFunc: Hne };
var Kne = [K3, q3, j3, X3, Y3, Z3, oP, sP, aP, iP, uP, pP, cP, lP, mP, fP, gP, yP, wP, IP, kP, NP, TP, _P, EP, RP, AP, PP, MP, BP, VP, WP, UP, HP, qP, XP, QP, JP, tO, oO, sO, iO, pO, cO, mO, dO, fO, hO, gO, xO, bO, CO, wO, IO, kO, TO, $O, RO, DO, AO, J3, FO, PO, OO, LO, BO, zO, WO, GO, UO, HO, KO, qO, jO, YO, ZO, eM, tM, oM, sM, iM, pM, lM, mM, fM, yM, bM, CM, SM, vM, NM, TM, $M, EM, RM, Og, AM, PM, MM, LM, BM, zM, VM, WM, bP, GM, KM, jM, YM, ZM, eL, tL, rL, nL, aL, uL, pL, lL, mL, dL, SP, gM, fL, hL, xL, bL, wL, SL, vL, kL, NL, TL, _L, EL, DL, AL, FL, PL, OL, LL, BL, zL, WL, GL, KL, jL, tP, XL, YL, QL];
for (let r of Kne)
Ya(r);
var wv = P();
wv.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => {
try {
return WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11]));
} catch (r) {
return false;
}
});
wv.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
if (wv.get("IS_NODE"))
return false;
try {
return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)), WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 5, 4, 1, 3, 1, 1, 10, 11, 1, 9, 0, 65, 0, 254, 16, 2, 0, 26, 11]));
} catch (r) {
return false;
}
});
var $v = Bp(tB());
var iB = Bp(oB());
var Ev = Bp(nB());
var sB = $v.default || $v;
var qne = Ev.default || Ev;
var am = class extends ro {
constructor(e) {
super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(pB), _v = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Lo(this, ur());
}
write(e, t10, o) {
let n = { id: this.dataIdNextNumber++ };
return this.move(n, e, t10, o, 1), n;
}
numDataIds() {
return this.dataIdMap.numDataIds();
}
async time(e) {
let t10 = y.now();
return e(), { kernelMs: y.now() - t10 };
}
move(e, t10, o, n, s) {
let a = this.dataIdNextNumber++;
if (n === "string") {
let c = t10;
this.dataIdMap.set(e, { id: a, stringBytes: c, shape: o, dtype: n, memoryOffset: null, refCount: s });
return;
}
let i = y.sizeFromShape(o), p = i * y.bytesPerElement(n), u = this.wasm._malloc(p) >>> 0;
this.dataIdMap.set(e, { id: a, memoryOffset: u, shape: o, dtype: n, refCount: s }), this.wasm.tfjs.registerTensor(a, i, u), t10 != null && this.wasm.HEAPU8.set(new Uint8Array(t10.buffer, t10.byteOffset, p), u);
}
async read(e) {
return this.readSync(e);
}
readSync(e, t10, o) {
let { memoryOffset: n, dtype: s, shape: a, stringBytes: i } = this.dataIdMap.get(e);
if (s === "string")
return (t10 == null || t10 === 0) && (o == null || o >= i.length) ? i : i.slice(t10, o);
t10 = t10 || 0, o = o || y.sizeFromShape(a);
let p = y.bytesPerElement(s), u = this.wasm.HEAPU8.slice(n + t10 * p, n + o * p);
return Xne(u.buffer, s);
}
disposeData(e, t10 = false) {
if (this.dataIdMap.has(e)) {
let o = this.dataIdMap.get(e);
if (o.refCount--, !t10 && o.refCount > 0)
return false;
this.wasm._free(o.memoryOffset), this.wasm.tfjs.disposeData(o.id), this.dataIdMap.delete(e);
}
return true;
}
refCount(e) {
return this.dataIdMap.has(e) ? this.dataIdMap.get(e).refCount : 0;
}
incRef(e) {
let t10 = this.dataIdMap.get(e);
t10 != null && t10.refCount++;
}
floatPrecision() {
return 32;
}
getMemoryOffset(e) {
return this.dataIdMap.get(e).memoryOffset;
}
dispose() {
this.wasm.tfjs.dispose(), "PThread" in this.wasm && this.wasm.PThread.terminateAllThreads(), this.wasm = null;
}
memory() {
return { unreliable: false };
}
makeOutput(e, t10, o, n) {
let s;
if (o == null)
s = this.write(n != null ? n : null, e, t10);
else {
let a = this.dataIdNextNumber++;
s = { id: a }, this.dataIdMap.set(s, { id: a, memoryOffset: o, shape: e, dtype: t10, refCount: 1 });
let i = y.sizeFromShape(e);
this.wasm.tfjs.registerTensor(a, i, o);
}
return { dataId: s, shape: e, dtype: t10 };
}
typedArrayFromHeap({ shape: e, dtype: t10, dataId: o }) {
let n = this.wasm.HEAPU8.buffer, { memoryOffset: s } = this.dataIdMap.get(o), a = y.sizeFromShape(e);
switch (t10) {
case "float32":
return new Float32Array(n, s, a);
case "int32":
return new Int32Array(n, s, a);
case "bool":
return new Uint8Array(n, s, a);
default:
throw new Error(`Unknown dtype ${t10}`);
}
}
};
function jne(r) {
return (e, t10) => (y.fetch(r, { credentials: "same-origin" }).then((o) => {
o.ok || e.env.a(`failed to load wasm binary file at '${r}'`), o.arrayBuffer().then((n) => {
WebAssembly.instantiate(n, e).then((s) => {
t10(s.instance, s.module);
});
});
}), {});
}
function aB(r, e, t10) {
if (Vg != null)
return Vg;
let o = "tfjs-backend-wasm.wasm";
return r && e ? o = "tfjs-backend-wasm-threaded-simd.wasm" : r && (o = "tfjs-backend-wasm-simd.wasm"), nm != null && nm[o] != null ? nm[o] : t10 + o;
}
async function uB() {
let [r, e] = await Promise.all([P().getAsync("WASM_HAS_SIMD_SUPPORT"), P().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);
return new Promise((t10, o) => {
let n = {};
n.locateFile = (i, p) => {
if (i.endsWith(".worker.js")) {
let u = iB.wasmWorkerContents.replace(/\n/g, "\\n"), c = new Blob([u], { type: "application/javascript" });
return URL.createObjectURL(c);
}
return i.endsWith(".wasm") ? aB(r, e, om != null ? om : p) : p + i;
}, Rv && (n.instantiateWasm = jne(aB(r, e, om != null ? om : "")));
let s = false;
n.onAbort = () => {
if (s || sm)
return;
sm = true, o({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" });
};
let a;
e && r && Vg == null ? (n.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + sB.toString()], { type: "text/javascript" }), a = sB(n)) : a = qne(n), a.then((i) => {
s = true, sm = false;
let p = null;
i.tfjs = { init: i.cwrap("init", null, []), initWithThreadsCount: i.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: i.cwrap("get_threads_count", "number", []), registerTensor: i.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: i.cwrap("dispose_data", p, ["number"]), dispose: i.cwrap("dispose", p, []) }, t10({ wasm: i });
}).catch(o);
});
}
function Xne(r, e) {
switch (e) {
case "float32":
return new Float32Array(r);
case "int32":
return new Int32Array(r);
case "bool":
return new Uint8Array(r);
default:
throw new Error(`Unknown dtype ${e}`);
}
}
var Yne = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
var Vg = null;
var om = null;
var nm = {};
var sm = false;
var Rv = false;
function Qne(r, e = false) {
if (bw("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), sm)
throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
Vg = r, Rv = e;
}
function Zne(r, e = false) {
if (sm)
throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");
if (typeof r == "string")
om = r;
else {
nm = r;
let t10 = Yne.filter((o) => nm[o] == null);
if (t10.length > 0)
throw new Error(`There were no entries found for the following binaries: ${t10.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);
}
Rv = e;
}
var pB = -1;
var _v = -1;
function Jne(r) {
pB = r;
}
function ese() {
if (_v === -1)
throw new Error("WASM backend not initialized.");
return _v;
}
var tse = "4.5.0";
var rse = 2;
eu("wasm", async () => {
let { wasm: r } = await uB();
return new am(r);
}, rse);
var Fo = P();
Fo.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
Fo.registerFlag("WEBGPU_CPU_FORWARD", () => true);
Fo.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE", () => -1);
Fo.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => true);
Fo.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
Fo.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
Fo.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
Fo.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE", () => true);
Fo.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG", () => false);
Fo.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL", () => 0);
Fo.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false);
Fo.registerFlag("WEBGPU_PRINT_SHADER", () => "");
var Wg = class {
constructor(e) {
e && (this.vendor = e.vendor, this.architecture = e.architecture, this.intelGPUGeneration = this.getIntelGPUGeneration());
}
getIntelGPUGeneration() {
if (this.isIntel()) {
if (this.architecture.startsWith("gen"))
return Number(this.architecture.match(/\d+/));
if (this.architecture.startsWith("xe"))
return 12;
}
return 0;
}
isIntel() {
return this.vendor === "intel";
}
};
var Ug = class {
constructor(e) {
this.device = e, this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
}
acquireBuffer(e, t10, o = false, n = true) {
let s, a = cB(e, t10);
return n ? (this.freeBuffers.has(a) || this.freeBuffers.set(a, []), this.freeBuffers.get(a).length > 0 ? (s = this.freeBuffers.get(a).pop(), this.numFreeBuffers--) : (s = this.device.createBuffer({ size: e, usage: t10, mappedAtCreation: o }), this.numBytesAllocated += e)) : (s = this.device.createBuffer({ size: e, usage: t10, mappedAtCreation: o }), this.numBytesAllocated += e), this.usedBuffers.has(a) || this.usedBuffers.set(a, []), this.usedBuffers.get(a).push(s), this.numUsedBuffers++, this.numBytesUsed += e, s;
}
releaseBuffer(e, t10, o, n = true) {
if (this.freeBuffers.size === 0)
return;
let s = cB(t10, o), a = this.usedBuffers.get(s), i = a.indexOf(e);
if (i < 0)
throw new Error("Cannot find the buffer in buffer manager");
a[i] = a[a.length - 1], a.pop(), this.numUsedBuffers--, this.numBytesUsed -= t10, n ? (this.freeBuffers.get(s).push(e), this.numFreeBuffers++) : (e.destroy(), this.numBytesAllocated -= t10);
}
getNumUsedBuffers() {
return this.numUsedBuffers;
}
getNumFreeBuffers() {
return this.numFreeBuffers;
}
dispose() {
this.freeBuffers.forEach((e, t10) => {
e.forEach((o) => {
o.destroy();
});
}), this.usedBuffers.forEach((e, t10) => {
e.forEach((o) => {
o.destroy();
});
}), this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
}
};
function cB(r, e) {
return `${r}_${e}`;
}
var Gg = class {
constructor(e) {
this.device = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
}
acquireTexture(e, t10, o, n) {
let s = mB(o), a = e * t10 * s, i = lB(e, t10, o, n);
if (this.freeTextures.has(i) || this.freeTextures.set(i, []), this.usedTextures.has(i) || this.usedTextures.set(i, []), this.numBytesUsed += a, this.numUsedTextures++, this.freeTextures.get(i).length > 0) {
this.numFreeTextures--;
let u = this.freeTextures.get(i).shift();
return this.usedTextures.get(i).push(u), u;
}
this.numBytesAllocated += a;
let p = this.device.createTexture({ size: [e, t10], format: o, usage: n });
return this.usedTextures.get(i).push(p), p;
}
releaseTexture(e, t10, o, n, s) {
if (this.freeTextures.size === 0)
return;
let a = lB(t10, o, n, s);
this.freeTextures.has(a) || this.freeTextures.set(a, []), this.freeTextures.get(a).push(e), this.numFreeTextures++, this.numUsedTextures--;
let i = this.usedTextures.get(a), p = i.indexOf(e);
if (p < 0)
throw new Error("Cannot release a texture that was never provided by this texture manager");
i.splice(p, 1);
let u = mB(n), c = t10 * o * u;
this.numBytesUsed -= c;
}
getNumUsedTextures() {
return this.numUsedTextures;
}
getNumFreeTextures() {
return this.numFreeTextures;
}
dispose() {
this.freeTextures.forEach((e, t10) => {
e.forEach((o) => {
o.destroy();
});
}), this.usedTextures.forEach((e, t10) => {
e.forEach((o) => {
o.destroy();
});
}), this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numUsedTextures = 0, this.numFreeTextures = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
}
};
function lB(r, e, t10, o) {
return `${r}_${e}_${t10}_${o}`;
}
function mB(r) {
if (r === "rgba8unorm")
return 16;
throw new Error(`${r} is not supported!`);
}
function dB(r, e) {
if (Math.max(...r) > 5)
throw new Error("Cannot symbolically compute strides for rank > 6 tensor.");
let t10 = r.length, o = "xyzwuv", n = r.map((a) => `${e}.${o[a]}`), s = new Array(t10 - 1);
s[t10 - 2] = n[t10 - 1];
for (let a = t10 - 3; a >= 0; --a)
s[a] = `(${s[a + 1]} * ${n[a + 1]})`;
return s;
}
var Bs = (r, e, t10) => t10 === "int32" ? `atomicAdd(${r}, bitcast<i32>(${e}));` : `
{
var oldValue = 0;
loop {
let newValueF32 = bitcast<f32>(oldValue) + (${e});
let newValue = bitcast<i32>(newValueF32);
let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);
if res.exchanged {
break;
}
oldValue = res.old_value;
}
}`;
var xB = (r, e, t10, o, n) => {
let s = { dtype: o.dtype, shape: o.shape }, a = nse(t10, s, e), i = r.createShaderModule({ code: a, label: e.constructor.name }), p = r.createComputePipeline({ compute: { module: i, entryPoint: "_start" }, label: e.constructor.name, layout: "auto" }), u = P().get("WEBGPU_PRINT_SHADER");
if (u !== "") {
u = u.toLowerCase();
let c = u.split(",");
(u === "all" || c.some((l) => n.toLowerCase().includes(l))) && (console.group(n), console.debug(a), console.groupEnd());
}
return p;
};
var Ae = (r, e = "f32") => {
switch (r) {
case 1:
return `${e}`;
case 2:
return `vec2<${e}>`;
case 3:
return `vec3<${e}>`;
case 4:
return `vec4<${e}>`;
default:
throw new Error(`${r}-component ${e} is not supported.`);
}
};
function Nt(r) {
if (r <= 1)
return "i32";
if (r === 2)
return "vec2<i32>";
if (r === 3)
return "vec3<i32>";
if (r === 4)
return "vec4<i32>";
if (r === 5)
return "vec5";
if (r === 6)
return "vec6";
throw Error(`GPU for rank ${r} is not yet supported`);
}
function Po(r) {
if (r === 0)
return "x";
if (r === 1)
return "y";
if (r === 2)
return "z";
if (r === 3)
return "w";
if (r === 4)
return "u";
if (r === 5)
return "v";
throw Error(`Index ${r} is not yet supported`);
}
function K(...r) {
let e;
switch (r.length) {
case 0:
e = `
fn main()
`;
break;
case 1:
e = `
fn main(${r[0]} : i32)
`;
break;
default:
throw Error("Unreachable");
}
return e;
}
function fB(r, e) {
let t10;
return t10 = `
${ose(e)}
fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
@builtin(global_invocation_id) GlobalId : vec3<u32>,
@builtin(local_invocation_index) LocalIndex: u32,
@builtin(workgroup_id) WorkgroupId : vec3<u32>,
@builtin(num_workgroups) NumWorkgroups : vec3<u32>) {
localId = LocalId;
localIndex = LocalIndex;
globalId = GlobalId;
numWorkgroups = NumWorkgroups;
workgroupId = WorkgroupId;
${r ? "main(getGlobalIndex());" : "main();"};
}
`, t10;
}
function ose(r) {
return `
@compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})
`;
}
function nse(r, e, t10) {
let o = [], n = t10.workgroupSize[0] * t10.workgroupSize[1] * t10.workgroupSize[2];
if (t10.outputComponent = t10.outputComponent ? t10.outputComponent : 1, o.push(`
var<private> localId: vec3<u32>;
var<private> localIndex: u32;
var<private> globalId: vec3<u32>;
var<private> numWorkgroups: vec3<u32>;
var<private> workgroupId: vec3<u32>;
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex() -> i32 {
${bB(t10) ? " return i32(globalId.x);" : ` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +
workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +
localIndex);
`}
}
`), t10.isFromPixels) {
o.push(`
struct Uniform {
size : i32,
numChannels : i32,
outShapeStrides : vec2<i32>,
};
@group(0) @binding(0) var<storage, read_write> result: array<${kp(e.dtype, t10.outputComponent)}>;
@group(0) @binding(2) var<uniform> uniforms: Uniform;
`);
let f = gB(t10);
return [hB, o.join(`
`), Dv(e.shape), t10.getUserCode(), fB(f, t10)].join(`
`);
}
let s, a, i = "struct Uniforms { NAN : f32, INFINITY : f32, ";
t10.variableNames.forEach((f, h) => {
let g = Nt(r[h].shape.length);
i += `${f.charAt(0).toLowerCase() + f.slice(1)}Shape : ${g}, `, s = r[h].shape.length - 1, a = Nt(s), i += `${f.charAt(0).toLowerCase() + f.slice(1)}ShapeStrides: ${a}, `;
});
let p = Nt(e.shape.length);
i += `outShape : ${p}, `, s = e.shape.length - 1, a = Nt(s), i += `
outShapeStrides: ${a}, `, t10.size && (i += "size : i32, "), t10.uniforms && (i += t10.uniforms), i += "};", i = mse(i), o.push(i), t10.atomic ? o.push(`
@group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
`) : o.push(`
@group(0) @binding(0) var<storage, read_write> result: array<${kp(e.dtype, t10.outputComponent)}>;
`), t10.variableNames.forEach((f, h) => {
o.push(`
@group(0) @binding(${1 + h}) var<storage, read> ${f}: array<${t10.variableComponents ? kp(r[h].dtype, t10.variableComponents[h]) : kp(r[h].dtype, t10.outputComponent)}>;
`);
}), i !== "" && o.push(`
@group(0) @binding(${1 + t10.variableNames.length}) var<uniform> uniforms: Uniforms;
`);
let u = pse(e.shape, t10.dispatchLayout), c = [hB, o.join(`
`) + sse, Dv(e.shape), u, cse(e.shape.length)];
t10.atomic || c.push(lse(e.shape, e.dtype, t10.outputComponent)), t10.variableNames.forEach((f, h) => {
c.push(`${Dv(r[h].shape, f)}`);
});
let l = r.map((f, h) => use(f, e.shape, t10.variableComponents ? t10.variableComponents[h] : t10.outputComponent, t10.dispatchLayout.x.length === e.shape.length)).join(`
`);
c.push(l), c.push(t10.getUserCode());
let m = gB(t10);
return c.push(fB(m, t10)), c.join(`
`);
}
function yB(r, e, t10, o) {
let n = r.shaderKey;
if (r.isFromPixels)
return n;
let s = t10.map((c) => c.dtype).concat(o.dtype), a = t10.map((c) => C.getBroadcastDims(c.shape, o.shape)), i = t10.map((c) => y.arraysEqual(c.shape, o.shape)).join("_"), p = a.map((c) => c.join("_")).join(";"), u = bB(r) ? "flatDispatch" : "";
return n += "_" + (r.workgroupSize ? r.workgroupSize.join(",") : "") + e.map((c) => c.length).join(",") + s.join(",") + r.variableNames.join(",") + p + i + u, n;
}
var hB = `
struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};
// Checks whether coordinates lie within the bounds of the shape.
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
return all(coord >= vec2<i32>(0)) && all(coord < shape);
}
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
return all(coord >= vec3<i32>(0)) && all(coord < shape);
}
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
return all(coord >= vec4<i32>(0)) && all(coord < shape);
}
fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
return coord;
}
fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
return dot(coords, vec2<i32>(shape.y, 1));
}
fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
}
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {
let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);
return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;
}
fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {
let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);
return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;
}
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
var res: i32 = a / b;
let modulo: i32 = a % b;
if (sign < 0. && modulo != 0) {
res = res - 1;
}
return res;
}
// NaN defination in IEEE 754-1985 is :
// - sign = either 0 or 1.
// - biased exponent = all 1 bits.
// - fraction = anything except all 0 bits (since all 0 bits represents infinity).
// https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
fn isnan(val: f32) -> bool {
let floatToUint: u32 = bitcast<u32>(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
let floatToUint: vec4<u32> = bitcast<vec4<u32>>(val);
return (floatToUint & vec4<u32>(0x7fffffffu)) > vec4<u32>(0x7f800000u);
}
`;
var sse = `
fn isinf(val: f32) -> bool {
return abs(val) == uniforms.INFINITY;
}
`;
function Dv(r, e = "") {
let t10 = r.length, o = e !== "" ? `get${e.charAt(0).toUpperCase() + e.slice(1)}CoordsFromIndex` : "getCoordsFromIndex", n = e !== "" ? `${e.charAt(0).toLowerCase() + e.slice(1)}ShapeStrides` : "outShapeStrides";
if (t10 <= 1)
return `fn ${o}(index : i32) -> i32 { return index; }`;
let s = y.computeStrides(r), a = Nt(t10), i = [];
for (let u = 0; u < t10; u++)
i.push(`d${u}`);
if (s.length === 1)
return ` fn ${o}(index : i32) -> vec2<i32> {
let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n};
return vec2<i32>(d0, d1);
}`;
let p;
return p = "var index2 = index;" + s.map((u, c) => {
let l = `let ${i[c]} = index2 / uniforms.${n}.${Po(c)}`, m = c === s.length - 1 ? `let ${i[c + 1]} = index2 - ${i[c]} * uniforms.${n}.${Po(c)}` : `index2 = index2 - ${i[c]} * uniforms.${n}.${Po(c)}`;
return `${l}; ${m};`;
}).join(""), `
fn ${o}(index : i32) -> ${a} {
${p}
return ${a}(${i.join(",")});
}
`;
}
function ase(r, e) {
let t10 = r.name, o = r.shape.length, n = Nt(o), s = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = a.map((c) => `${c} : i32`).join(", ");
if (o < 1)
return `
fn ${s}() -> ${Ae(e)} {
return ${Ae(e)}(${t10}[0]);
}
`;
let p = `uniforms.${t10.charAt(0).toLowerCase() + t10.slice(1)}Shape`, u = `${o}D`;
return o === 0 && (u = "1D"), `
fn ${s}(${i}) -> ${Ae(e)} {
return ${Ae(e)}(${t10}[getIndexFromCoords${u}(${n}(${a.join(",")}),
${p})${e === 1 ? "" : ` / ${e}`}]);
}
`;
}
function ise(r, e, t10, o) {
let n = r.name, s = n.charAt(0).toUpperCase() + n.slice(1), a = "get" + s + "ByOutput", i = r.shape.length, p = e.length, u = Nt(p);
if (y.arraysEqual(r.shape, e) && o)
return `
fn ${a}Index(globalIndex : i32) -> ${Ae(t10)} {
return ${Ae(t10)}(${n}[globalIndex]);
}
fn ${a}Coords(coords : ${u}) -> ${Ae(t10)} {
return ${Ae(t10)}(${n}[${p > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}${t10 === 1 ? "" : ` / ${t10}`}]);
}
`;
let c = C.getBroadcastDims(r.shape, e), l = p - i, m = "";
if (i === 0)
return `
fn ${a}Index(globalIndex : i32) -> ${Ae(t10)}{
return get${s}();
}
fn ${a}Coords(coords : ${u}) -> ${Ae(t10)}{
return get${s}();
}
`;
p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((g) => `coords.${Po(g + l)} = 0;`).join(`
`);
let d = "";
if (p < 2 && i > 0)
d = "coords";
else if (p > 1) {
let g = Nt(i), x = r.shape.map((b, w) => `coords.${Po(w + l)}`).join(", ");
d = `${g}(${x})`;
} else
d = "coords";
let f = `uniforms.${n.charAt(0).toLowerCase() + n.slice(1)}Shape`, h = `${i}D`;
return `
fn ${a}Index(globalIndex : i32) -> ${Ae(t10)} {
var coords = getCoordsFromIndex(globalIndex);
${m}
return ${Ae(t10)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t10 === 1 ? "" : ` / ${t10}`}]);
}
fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t10)} {
var coords = coordsIn;
${m}
return ${Ae(t10)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t10 === 1 ? "" : ` / ${t10}`}]);
}
`;
}
function use(r, e, t10, o) {
let n = ase(r, t10);
return r.shape.length <= e.length && (n += ise(r, e, t10, o)), n;
}
function pse(r, e) {
let { x: t10, y: o = [], z: n = [] } = e, s = r.length, a = t10.length + o.length + n.length;
if (a !== s)
return "";
if (t10.length === s)
return `fn getOutputCoords() -> ${Nt(s)}{
let globalIndex = getGlobalIndex();
return getCoordsFromIndex(globalIndex);
}
`;
let i = "", p = [t10, o, n];
for (let m = 0; m < p.length; m++) {
let d = p[m];
if (d.length !== 0)
if (d.length === 1)
i += `let d${d[0]} = i32(globalId[${m}]);`;
else {
let f = dB(d, "uniforms.outShape");
i += `var index${m} = i32(globalId[${m}]);`;
for (let h = 0; h < f.length; h++)
i += `let d${d[h]} = index${m} / ${f[h]};`, h === f.length - 1 ? i += `let d${d[h + 1]} = index${m} - d${d[h]} * ${f[h]};` : i += `index${m} = index${m} - d${d[h]} * ${f[h]};`;
}
}
let u = [];
for (let m = 0; m < a; m++)
u.push(`d${m}`);
let c = Nt(a), l = `fn getOutputCoords() -> ${c} {
${i}
`;
return u.length === 0 ? l += `return ${c}(0); }` : l += `return ${c}(${u.join(",")}); }`, l;
}
function cse(r) {
let e = "";
switch (r) {
case 0:
case 1:
e += `
fn getOutputIndexFromCoords(coords : i32) -> i32 {
return coords;
}
`;
break;
case 2:
e += `
fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
}
`;
break;
case 3:
e += `
fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
}
`;
break;
case 4:
e += `
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
}
`;
break;
case 5:
e += `
fn getOutputIndexFromCoords(coords : vec5) -> i32 {
return coords.x * uniforms.outShapeStrides.x +
coords.y * uniforms.outShapeStrides.y +
coords.z * uniforms.outShapeStrides.z +
coords.w * uniforms.outShapeStrides.w +
coords.u;
}
`;
break;
case 6:
e += `
fn getOutputIndexFromCoords(coords : vec6) -> i32 {
return coords.x * uniforms.outShapeStrides.x +
coords.y * uniforms.outShapeStrides.y +
coords.z * uniforms.outShapeStrides.z +
coords.w * uniforms.outShapeStrides.w +
coords.u * uniforms.outShapeStrides.u +
coords.v;
}
`;
break;
default:
y.assert(false, () => `Unsupported ${r}D shape`);
break;
}
return e;
}
function bB(r) {
return r.dispatch[1] === 1 && r.dispatch[2] === 1;
}
function kp(r, e = 1) {
if (r === "float32")
return Ae(e, "f32");
if (r === "int32" || r === "bool")
return Ae(e, "i32");
throw new Error(`type ${r} is not supported.`);
}
function lse(r, e, t10) {
let o = r.length, n = kp(e, t10), s = `fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t10)}) {
result[flatIndex] = ${n}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t10, "i32")}) {
result[flatIndex] = ${n}(value);
}
`;
if (o >= 2) {
let a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = Nt(o);
s += `
fn setOutputAtCoords(${a.map((p) => `${p} : i32`).join(", ")}, value : ${Ae(t10)}) {
let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")}));
setOutputAtIndex(flatIndex${t10 === 1 ? "" : ` / ${t10}`}, value);
}
fn setOutputAtCoordsI32(${a.map((p) => `${p} : i32`).join(", ")}, value : ${Ae(t10, "i32")}) {
let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")}));
setOutputAtIndexI32(flatIndex${t10 === 1 ? "" : ` / ${t10}`}, value);
}
`;
}
return s;
}
function mse(r) {
let e = /(\w+)\s*:\s*vec(5|6)/g;
r = r.replace(e, (o) => "@align(16) " + o);
let t10 = /vec(5|6)\s*,\s*(\w+)/g;
return r = r.replace(t10, (o, n, s) => `vec${n}, @align(16) ${s}`), r;
}
function gB(r) {
return !(r.dispatchLayout.hasOwnProperty("y") && r.dispatchLayout.y.length !== 0 || r.dispatchLayout.hasOwnProperty("z") && r.dispatchLayout.z.length !== 0);
}
var Fv = {};
He(Fv, { GPUBytesPerElement: () => Hg, MatMulProgramType: () => Oo, assertNotComplex: () => cm, computeDispatch: () => q, computeWorkPerThreadForConv2d: () => um, computeWorkgroupInfoForMatMul: () => Av, computeWorkgroupSizeForConv2d: () => im, flatDispatchLayout: () => Z, isWebGPUSupported: () => pm, tilesFitEvenlyIntoShape: () => fse });
var Np = (r) => {
let e = 1;
for (let t10 = 0; t10 < r.length; t10++)
e *= r[t10];
return e;
};
function fse(r, e) {
if (r.length !== e.length)
throw new Error(`Cannot compute whether rank ${r.length} tiles fit evenly into rank ${e.length} shape - ranks must match.`);
return e.every((t10, o) => t10 % r[o] === 0);
}
function q(r, e, t10 = [1, 1, 1], o = [1, 1, 1]) {
let [n, s, a] = [Math.ceil(Np(r.x.map((i) => e[i])) / (t10[0] * o[0])), r.y ? Math.ceil(Np(r.y.map((i) => e[i])) / (t10[1] * o[1])) : 1, r.z ? Math.ceil(Np(r.z.map((i) => e[i])) / (t10[2] * o[2])) : 1];
return [n, s, a];
}
function Av(r, e, t10, o = false) {
let n = [8, 8, 1], s = [4, 4, 1];
return o || (r <= 8 && (s[1] = 1), e <= 16 && t10 <= 16 && (n[0] = 4)), { workgroupSize: n, elementsPerThread: s };
}
function im(r, e, t10 = false) {
if (t10)
return [8, 8, 1];
let o = Np(r.x.map((s) => e[s])), n = Np(r.y.map((s) => e[s]));
return o <= 4 ? [4, 16, 1] : n <= 4 ? [16, 4, 1] : [16, 16, 1];
}
function um(r, e, t10 = false) {
if (t10)
return [4, 4, 1];
let o = Np(r.x.map((s) => e[s])), n = Np(r.y.map((s) => e[s]));
return o <= 4 ? [1, 2, 1] : n <= 4 ? [2, 1, 1] : [2, 2, 1];
}
function Z(r) {
return { x: r.map((e, t10) => t10) };
}
function Hg(r) {
if (r === "float32" || r === "int32" || r === "bool" || r === "string")
return 4;
if (r === "complex64")
return 8;
throw new Error(`Unknown dtype ${r}`);
}
function pm() {
return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
}
function cm(r, e) {
Array.isArray(r) || (r = [r]), r.forEach((t10) => {
t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGPU backend.`);
});
}
var Oo;
(function(r) {
r[r.MatMulReduceProgram = 0] = "MatMulReduceProgram", r[r.MatMulSplitKProgram = 1] = "MatMulSplitKProgram", r[r.MatMulSmallOutputSizeProgram = 2] = "MatMulSmallOutputSizeProgram", r[r.MatMulPackedProgram = 3] = "MatMulPackedProgram", r[r.MatMulMax = 4] = "MatMulMax";
})(Oo || (Oo = {}));
var hse = P().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
var gse = (r, e) => {
let t10 = r.limits.maxComputeWorkgroupsPerDimension, o = e.dispatchLayout, n = e.dispatch;
if (n.every((a) => a <= t10))
return n;
y.assert(n[0] > t10 && o.y === void 0 && o.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension.");
let s = Math.ceil(Math.sqrt(n[0]));
return s > t10 ? (s = Math.ceil(Math.cbrt(n[0])), y.assert(s <= t10, () => "Total dispatch size exceeds WebGPU maximum."), [s, s, s]) : [s, s, 1];
};
var Cu = class extends ro {
nextDataId() {
return Cu.nextDataId++;
}
constructor(e, t10) {
if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.dispatchNumberInEncoder = 0, this.disposed = false, this.downloadWaitMs = 0, this.tensorDataPendingDisposal = [], this.stagingPendingDisposal = [], this.uniformPendingDisposal = [], this.uploadWaitMs = 0, !pm())
throw new Error("WebGPU is not supported on this device");
this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = e.features.has("timestamp-query-inside-passes"), this.adapterInfo = new Wg(t10), this.thresholdToIncreaseWorkgroups = this.adapterInfo.intelGPUGeneration >= 12 ? 16 : 8, this.bufferManager = new Ug(this.device), this.textureManager = new Gg(this.device), this.tensorMap = new Lo(this, ur()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), P().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas));
}
floatPrecision() {
return 32;
}
defaultGpuBufferUsage() {
return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST;
}
disposeData(e, t10 = false) {
if (this.tensorDataPendingDisposal.indexOf(e) >= 0)
return false;
if (!this.tensorMap.has(e))
return true;
let o = this.tensorMap.get(e);
if (this.decRef(e), !t10 && o.refCount > 0)
return false;
if (this.commandQueueOwnedIds.has(e))
return this.tensorDataPendingDisposal.push(e), false;
let { complexTensorInfos: n } = this.tensorMap.get(e);
return n != null && (this.disposeData(n.real.dataId, t10), this.disposeData(n.imag.dataId, t10)), this.releaseResource(e), this.tensorMap.delete(e), true;
}
memory() {
return { numBytesInGPU: this.bufferManager.numBytesUsed, numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated, unreliable: false };
}
releaseResource(e) {
let t10 = this.tensorMap.get(e);
if (!(!t10 || !t10.resourceInfo)) {
if (t10.external) {
t10.resourceInfo = null;
return;
}
if ("texture" in t10.resourceInfo) {
let o = t10.resourceInfo;
o.texture instanceof GPUTexture && this.textureManager.releaseTexture(o.texture, o.width, o.height, o.format, o.usage), o.texture = null;
} else {
let o = t10.resourceInfo;
this.bufferManager.releaseBuffer(o.buffer, o.size, o.usage), o.buffer = null;
}
t10.resourceInfo = null;
}
}
refCount(e) {
return this.tensorMap.has(e) ? this.tensorMap.get(e).refCount : 0;
}
incRef(e) {
let t10 = this.tensorMap.get(e);
t10.refCount++;
}
decRef(e) {
if (this.tensorMap.has(e)) {
let t10 = this.tensorMap.get(e);
t10.refCount--;
}
}
write(e, t10, o) {
if (o === "complex64" && e != null)
throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
let n = { id: this.nextDataId() };
return this.tensorMap.set(n, { dtype: o, shape: t10, values: e, refCount: 1 }), n;
}
move(e, t10, o, n, s) {
if (n === "complex64")
throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
this.tensorMap.set(e, { dtype: n, shape: o, values: t10, refCount: s });
}
submitQueue() {
this.ensureComputePassEnded(), this.queue.submit([this.currentCommandEncoder.finish()]), this.currentCommandEncoder = null, this.dispatchNumberInEncoder = 0, this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDataPendingDisposal.forEach((e) => {
this.releaseResource(e), this.tensorMap.delete(e);
}), this.uniformPendingDisposal.forEach((e) => this.bufferManager.releaseBuffer(e.buffer, e.size, e.usage)), this.stagingPendingDisposal.forEach((e) => this.bufferManager.releaseBuffer(e.buffer, e.size, e.usage, false)), this.tensorDataPendingDisposal = [], this.uniformPendingDisposal = [], this.stagingPendingDisposal = [];
}
ensureCommandEncoderReady() {
this.currentCommandEncoder || (this.currentCommandEncoder = this.device.createCommandEncoder());
}
ensureComputePassEnded() {
this.currentComputePass && (this.currentComputePass.end(), this.currentComputePass = null);
}
getComputePass() {
return this.currentComputePass || (this.currentComputePass = this.currentCommandEncoder.beginComputePass()), this.currentComputePass;
}
async getBufferData(e, t10) {
let o = this.bufferManager.acquireBuffer(t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);
this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e, 0, o, 0, t10), this.submitQueue(), await o.mapAsync(GPUMapMode.READ);
let n = o.getMappedRange().slice(0);
return o.unmap(), o != null && this.bufferManager.releaseBuffer(o, t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ), P().getBool("WEBGPU_USE_PROFILE_TOOL") && (y.assert(this.dummyContext !== void 0, () => "Fail to get context for profiling tool"), this.dummyContext.getCurrentTexture()), n;
}
convertAndCacheOnCPU(e, t10) {
let o = this.tensorMap.get(e);
return this.releaseResource(e), o.values = t10, o.values;
}
readSync(e) {
let t10 = this.tensorMap.get(e), { values: o, complexTensorInfos: n } = t10;
if (o != null || t10.dtype === "string")
return o;
if (t10.dtype === "complex64") {
let h = this.readSync(n.real.dataId), g = this.readSync(n.imag.dataId), x = y.convertBackendValuesAndArrayBuffer(C.mergeRealAndImagArrays(h, g).buffer, "float32");
return this.convertAndCacheOnCPU(e, x), x;
}
let s = ["opaque", "premultiplied"], a = t10.resourceInfo, i = a.size;
y.assert(i % 4 === 0, () => "Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");
let p = i / 4, u = new ArrayBuffer(i), c = 256, l = 256, m = s.map((h) => new OffscreenCanvas(c, l)), d = new OffscreenCanvas(c, l);
this.ensureComputePassEnded(), m.map((h, g) => {
let x = h.getContext("webgpu");
return x.configure({ device: this.device, format: "bgra8unorm", usage: GPUTextureUsage.COPY_DST, alphaMode: s[g] }), x.getCurrentTexture();
}).map((h, g) => {
let x = c * 4, b = (R, D, F) => {
this.ensureCommandEncoderReady(), this.currentCommandEncoder.copyBufferToTexture({ buffer: a.buffer, bytesPerRow: x, offset: F }, { texture: h }, { width: R, height: D }), this.submitQueue();
let O = d.getContext("2d", { willReadFrequently: true });
O.clearRect(0, 0, R, D), O.drawImage(m[g], 0, 0);
let M = O.getImageData(0, 0, R, D).data, L = s[g], B = new Uint8ClampedArray(u, F, R * D * 4);
for (let z = 0; z < B.length; z += 4)
if (L === "premultiplied")
B[z + 3] = M[z + 3];
else {
let U = M[z];
B[z] = M[z + 2], B[z + 1] = M[z + 1], B[z + 2] = U;
}
}, w = Math.floor(p / (c * l)), S = c, k = l, _ = 0;
for (let R = 0; R < w; R++)
b(S, k, _), _ += c * l * 4;
let E = p % (c * l);
k = Math.floor(E / c), k > 0 && (b(S, k, _), _ += k * (c * 4)), S = E % c, S > 0 && b(S, 1, _);
});
let f = y.convertBackendValuesAndArrayBuffer(u, t10.dtype);
return this.convertAndCacheOnCPU(e, f), f;
}
async read(e) {
if (!this.tensorMap.has(e))
throw new Error(`Tensor ${e} was not registered!`);
let t10 = this.tensorMap.get(e), { values: o } = t10;
if (o != null)
return o;
let n;
if (t10.dtype === "complex64") {
let s = await Promise.all([this.read(t10.complexTensorInfos.real.dataId), this.read(t10.complexTensorInfos.imag.dataId)]), a = s[0], i = s[1];
n = C.mergeRealAndImagArrays(a, i);
} else {
let s = t10.resourceInfo, a = await this.getBufferData(s.buffer, s.size);
n = y.convertBackendValuesAndArrayBuffer(a, t10.dtype);
}
return this.convertAndCacheOnCPU(e, n), n;
}
copyBuffer(e, t10, o) {
let n = this.bufferManager.acquireBuffer(t10, o);
return this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e, 0, n, 0, t10), this.submitQueue(), n;
}
createTensorFromGPUData(e, t10, o) {
let n = e.buffer;
if (o === "complex64")
throw new Error("Cannot write to a complex64 dtype. ");
let s = { id: this.nextDataId() };
this.tensorMap.set(s, { dtype: o, shape: t10, values: null, refCount: 1, external: e.zeroCopy });
let a = this.tensorMap.get(s), i = Hg(a.dtype) * y.sizeFromShape(a.shape);
if (e.buffer.size < i)
throw new Error(`GPUBuffer size(${e.buffer.size}) is smaller than tensor size(${i})!`);
if ((e.buffer.usage & (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC)) !== (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC))
throw new Error("GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!");
return e.zeroCopy !== true && (n = this.copyBuffer(n, i, n.usage)), a.resourceInfo = { size: n.size, usage: n.usage, buffer: n }, ur().makeTensorFromDataId(s, t10, o, this);
}
readToGPU(e) {
let t10 = this.tensorMap.get(e), { values: o, dtype: n, shape: s, resourceInfo: a } = t10;
if (n === "complex64")
throw new Error("Does not support reading buffer for complex64 dtype.");
if (a == null)
throw o != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU.");
let i = a.size, p = this.bufferManager.acquireBuffer(i, a.usage);
this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(a.buffer, 0, p, 0, i), this.submitQueue();
let u = this.makeTensorInfo(s, n), c = ur().makeTensorFromTensorInfo(u), l = this.tensorMap.get(u.dataId);
return l.resourceInfo = { size: i, usage: this.defaultGpuBufferUsage(), buffer: p }, { tensorRef: c, buffer: p };
}
bufferSync(e) {
let t10 = this.readSync(e.dataId);
if (e.dtype === "string")
try {
let o = t10.map((n) => y.decodeString(n));
return me(e.shape, e.dtype, o);
} catch (o) {
throw new Error("Failed to decode encoded string bytes into utf-8");
}
return me(e.shape, e.dtype, t10);
}
async time(e) {
this.supportTimeQuery || console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");
let t10 = this.activeTimers, o = [], n = false;
this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, e();
let s = y.flatten(this.activeTimers.map((u) => u.query)).filter((u) => u != null), a = y.flatten(this.activeTimers.map((u) => u.name)).filter((u) => u != null);
this.activeTimers = t10, n && (this.programTimersStack = null);
let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null }, p = await Promise.all(s);
return i.kernelMs = y.sum(p), i.getExtraProfileInfo = () => p.map((u, c) => ({ name: a[c], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", "), this.uploadWaitMs = 0, this.downloadWaitMs = 0, i;
}
makeTensorInfo(e, t10, o) {
return t10 === "string" && o != null && o.length > 0 && y.isString(o[0]) && (o = o.map((s) => y.encodeString(s))), { dataId: this.write(o, e, t10), shape: e, dtype: t10 };
}
tensorToBinding(e) {
if (!e)
return null;
let t10 = this.tensorMap.get(e.dataId);
if ("texture" in t10.resourceInfo) {
let n = t10.resourceInfo;
return n.texture instanceof GPUExternalTexture ? n.texture : n.texture.createView();
}
let o = t10.resourceInfo;
return { offset: 0, size: o.size, buffer: o.buffer };
}
async getQueryTime(e) {
return this.supportTimeQuery ? this.getTimeFromQuerySet(e) : 0;
}
uploadToGPU(e) {
let t10 = this.tensorMap.get(e);
if (t10.resourceInfo)
return;
let o = Hg(t10.dtype) * y.sizeFromShape(t10.shape), n;
if (t10.values) {
if (n = this.bufferManager.acquireBuffer(o, this.defaultGpuBufferUsage(), true), n.mapState === "unmapped") {
let s = this.bufferManager.acquireBuffer(o, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, true, false), a = s.getMappedRange();
t10.dtype === "int32" || t10.dtype === "bool" ? new Int32Array(a).set(t10.values) : new Float32Array(a).set(t10.values), s.unmap(), this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(s, 0, n, 0, o), this.stagingPendingDisposal.push({ size: o, usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, buffer: s });
} else {
let s = n.getMappedRange();
t10.dtype === "int32" || t10.dtype === "bool" ? new Int32Array(s).set(t10.values) : new Float32Array(s).set(t10.values), n.unmap();
}
t10.values = null;
} else
n = this.bufferManager.acquireBuffer(o, this.defaultGpuBufferUsage());
t10.resourceInfo = { size: o, usage: this.defaultGpuBufferUsage(), buffer: n };
}
makeUniforms(e) {
let t10 = 0, o = 0, n = [], s = 1;
e.forEach((u) => {
u.data.length === 0 && (u.data = [1]);
let c;
switch (u.data.length) {
case 1:
c = 4;
break;
case 2:
c = 8;
break;
case 3:
c = 16;
break;
case 4:
c = 16;
break;
case 5:
c = 16;
break;
case 6:
c = 16;
break;
default:
y.assert(false, () => `Unsupported ${u.data.length}D shape`);
}
(o === 5 || o === 6) && (c = 16), c > s && (s = c), t10 = Math.ceil(t10 / c) * c, o = u.data.length, n.push(t10), t10 += u.data.length * 4;
}), t10 = Math.ceil(t10 / s) * s;
let a = new ArrayBuffer(t10);
e.forEach((u, c) => {
let l = n[c];
u.type === "int32" ? new Int32Array(a, l, u.data.length).set(u.data) : u.type === "uint32" ? new Uint32Array(a, l, u.data.length).set(u.data) : new Float32Array(a, l, u.data.length).set(u.data);
});
let i = this.bufferManager.acquireBuffer(t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);
this.queue.writeBuffer(i, 0, a, 0, t10);
let p = { size: t10, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, buffer: i };
return this.uniformPendingDisposal.push(p), { offset: 0, size: t10, buffer: i };
}
runWebGPUProgram(e, t10, o, n, s) {
if (s || (s = this.makeTensorInfo(e.outputShape, o)), y.sizeFromShape(s.shape) === 0)
return this.tensorMap.get(s.dataId).values = y.getTypedArrayFromDType(s.dtype, 0), s;
this.uploadToGPU(s.dataId), e.dispatch = gse(this.device, e);
let a = [], i = [];
if (!e.isFromPixels) {
a.push({ type: "float32", data: [NaN] }, { type: "float32", data: [1 / 0] }), i = t10.concat(s).map((g) => g.shape);
let h = "int32";
if (i.map((g) => {
a.push({ type: h, data: g });
let x = y.computeStrides(g);
a.push({ type: h, data: x });
}), e.size) {
let g = y.sizeFromShape(e.outputShape);
a.push({ type: h, data: [e.outputComponent ? g / e.outputComponent : g] });
}
}
let p = t10.map((h, g) => {
if (h.dtype === "complex64")
throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");
return this.uploadToGPU(h.dataId), { dtype: this.tensorMap.get(h.dataId).dtype, shape: h.shape, name: e.variableNames[g] };
}), u = yB(e, i, p, s), c;
u in this.pipelineCache ? c = this.pipelineCache[u] : (c = xB(this.device, e, p, s, u), this.pipelineCache[u] = c), n && (a = [...a, ...n]);
let l = [this.tensorToBinding(s), ...t10.map((h) => this.tensorToBinding(h)), this.makeUniforms(a)], m = this.device.createBindGroup({ layout: c.getBindGroupLayout(0), entries: l.map((h, g) => ({ binding: g, resource: h })) });
this.ensureCommandEncoderReady();
let d = this.getComputePass(), f = this.activeTimers != null;
return f && this.supportTimeQuery && d.writeTimestamp(this.querySet, 0), d.setPipeline(c), d.setBindGroup(0, m), d.dispatchWorkgroups(e.dispatch[0], e.dispatch[1], e.dispatch[2]), f && this.supportTimeQuery && d.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t10.forEach((h) => {
this.commandQueueOwnedIds.add(h.dataId);
}), this.commandQueueOwnedIds.add(s.dataId), P().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), f && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) }), s;
}
async getTimeFromQuerySet(e) {
let t10 = this.bufferManager.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), o = this.bufferManager.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);
this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.resolveQuerySet(e, 0, 2, t10, 0), this.currentCommandEncoder.copyBufferToBuffer(t10, 0, o, 0, 16), this.submitQueue(), await o.mapAsync(GPUMapMode.READ);
let n = new BigUint64Array(o.getMappedRange()), s = Number(n[1] - n[0]);
return o.unmap(), this.bufferManager.releaseBuffer(o, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST), this.bufferManager.releaseBuffer(t10, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), s / 1e6;
}
shouldExecuteOnCPU(e, t10 = hse) {
return P().getBool("WEBGPU_CPU_FORWARD") && e.every((o) => this.tensorMap.get(o.dataId).resourceInfo == null && y.sizeFromShape(o.shape) < t10);
}
numDataIds() {
return this.tensorMap.numDataIds() - this.tensorDataPendingDisposal.length;
}
dispose() {
this.disposed || (this.bufferManager.dispose(), this.textureManager.dispose(), this.disposed = true);
}
};
Cu.nextDataId = 0;
pm() && eu("webgpu", async () => {
let r = { powerPreference: P().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, e = await navigator.gpu.requestAdapter(r), t10 = {};
e.features.has("timestamp-query-inside-passes") && (t10.requiredFeatures = ["timestamp-query-inside-passes"]);
let o = e.limits;
t10.requiredLimits = { maxComputeWorkgroupStorageSize: o.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: o.maxComputeWorkgroupsPerDimension, maxStorageBufferBindingSize: o.maxStorageBufferBindingSize, maxBufferSize: o.maxBufferSize, maxComputeWorkgroupSizeX: o.maxComputeWorkgroupSizeX, maxComputeInvocationsPerWorkgroup: o.maxComputeInvocationsPerWorkgroup };
let n = await e.requestDevice(t10), s = await e.requestAdapterInfo();
return new Cu(n, s);
}, 3);
var fe;
(function(r) {
r[r.ADD = 0] = "ADD", r[r.ATAN2 = 1] = "ATAN2", r[r.COMPLEX_MULTIPLY_IMAG = 2] = "COMPLEX_MULTIPLY_IMAG", r[r.COMPLEX_MULTIPLY_REAL = 3] = "COMPLEX_MULTIPLY_REAL", r[r.DIV = 4] = "DIV", r[r.ELU_DER = 5] = "ELU_DER", r[r.EQUAL = 6] = "EQUAL", r[r.GREATER = 7] = "GREATER", r[r.GREATER_EQUAL = 8] = "GREATER_EQUAL", r[r.INT_DIV = 9] = "INT_DIV", r[r.LESS = 10] = "LESS", r[r.LESS_EQUAL = 11] = "LESS_EQUAL", r[r.LOGICAL_AND = 12] = "LOGICAL_AND", r[r.LOGICAL_OR = 13] = "LOGICAL_OR", r[r.MAX = 14] = "MAX", r[r.MIN = 15] = "MIN", r[r.MOD = 16] = "MOD", r[r.MUL = 17] = "MUL", r[r.NOT_EQUAL = 18] = "NOT_EQUAL", r[r.POW = 19] = "POW", r[r.PRELU = 20] = "PRELU", r[r.SQUARED_DIFFERENCE = 21] = "SQUARED_DIFFERENCE", r[r.SUB = 22] = "SUB";
})(fe || (fe = {}));
var xse = `
resultTemp = select(resultTemp, valueForNaN, isNaN | isnan(a) | isnan(b));`;
var yse = `
resultTemp = select(
resultTemp, vec4<f32>(valueForNaN),
vec4<bool>(isNaN) | isnanVec4(a) | isnanVec4(b));
`;
var bse = "return a + b;";
var Cse = "var resultTemp = atan2(a, b);";
var wse = "return areal * breal - aimag * bimag;";
var Sse = "return areal * bimag + aimag * breal;";
var Ise = "return a / b;";
var vse = "return select(a * (b + 1.0), a, b >= 0.);";
var kse = "return select(a * (b + vec4<f32>(1.0)), a, b >= vec4<f32>(0.));";
var Nse = "return f32(a == b);";
var Tse = "return vec4<f32>(a == b);";
var _se = "return f32(a > b);";
var $se = "return vec4<f32>(a > b);";
var Ese = "return f32(a >= b);";
var Rse = "return vec4<f32>(a >= b);";
var Dse = `
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`;
var Ase = `
let ia = vec4<i32>(round(a));
let ib = vec4<i32>(round(b));
let cond = ib != vec4<i32>(0);
var resultTemp = vec4<i32>(0);
let s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
resultTemp[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
resultTemp[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
resultTemp[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
resultTemp[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4<f32>(resultTemp);
`;
var Fse = "return f32(a < b);";
var Pse = "return vec4<f32>(a < b);";
var Ose = "return f32(a <= b);";
var Mse = "return vec4<f32>(a <= b);";
var Lse = "return f32(a >= 1.0 && b >= 1.0);";
var Bse = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`;
var zse = "return f32(a >= 1.0 || b >= 1.0);";
var Vse = `return min(vec4<f32>(a >= vec4<f32>(1.0)) +
vec4<f32>(b >= vec4<f32>(1.0)), vec4<f32>(1.0));`;
var Wse = "var resultTemp = max(a, b);";
var Use = "var resultTemp = min(a, b);";
var Gse = `
let isNaN = b == 0.;
var resultTemp = a % b;
resultTemp = select((resultTemp + b) % b, resultTemp,
(a < 0. && b < 0.) || (a >= 0. && b > 0.));
`;
var Hse = `
let isNaN = !vec4<bool>(b);
var resultTemp = vec4<f32>(a % b);
if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {
resultTemp[0] = (resultTemp[0] + b[0]) % b[0];
}
if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {
resultTemp[1] = (resultTemp[1] + b[1]) % b[1];
}
if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {
resultTemp[2] = (resultTemp[2] + b[2]) % b[2];
}
if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {
resultTemp[3] = (resultTemp[3] + b[3]) % b[3];
}
`;
var Kse = "return a * b;";
var qse = `
var resultTemp = f32(a != b);
let valueForNaN = 1.0;
`;
var jse = `
var resultTemp = vec4<f32>(a != b);
let valueForNaN = 1.0;
`;
var Xse = `
let isNaN = a < 0.0 && floor(b) < b;
if (b == 0.0) {
return 1.0;
}
var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b),
round(abs(b) % 2.0) != 1.0);
`;
var Yse = `
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
let isModRound1 = vec4<f32>(isModRound1Bool);
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
var resultTemp = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
let isExpZero = b == vec4<f32>(0.0);
if (isExpZero.r) {
resultTemp.r = 1.0;
}
if (isExpZero.g) {
resultTemp.g = 1.0;
}
if (isExpZero.b) {
resultTemp.b = 1.0;
}
if (isExpZero.a) {
resultTemp.a = 1.0;
}
let isNaN = (a < vec4<f32>(0.0)) & (floor(b) < b);
`;
var Qse = "if (a < 0.0) { return b * a; } return a;";
var Zse = `
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;
var Jse = "return (a - b) * (a - b);";
var eae = "return a - b;";
function Kc(r, e) {
do {
let t10;
switch (r) {
case fe.ATAN2:
t10 = Cse;
break;
case fe.MAX:
t10 = Wse;
break;
case fe.MIN:
t10 = Use;
break;
case fe.MOD:
t10 = e ? Hse : Gse;
break;
case fe.NOT_EQUAL:
t10 = e ? jse : qse;
break;
case fe.POW:
t10 = e ? Yse : Xse;
break;
default:
continue;
}
return `
let isNaN = false;
let valueForNaN = uniforms.NAN;
{
${t10}
${e ? yse : xse}
return resultTemp;
}
`;
} while (false);
switch (r) {
case fe.ADD:
return bse;
case fe.COMPLEX_MULTIPLY_IMAG:
return Sse;
case fe.COMPLEX_MULTIPLY_REAL:
return wse;
case fe.DIV:
return Ise;
case fe.ELU_DER:
return e ? kse : vse;
case fe.EQUAL:
return e ? Tse : Nse;
case fe.GREATER:
return e ? $se : _se;
case fe.GREATER_EQUAL:
return e ? Rse : Ese;
case fe.INT_DIV:
return e ? Ase : Dse;
case fe.LESS:
return e ? Pse : Fse;
case fe.LESS_EQUAL:
return e ? Mse : Ose;
case fe.LOGICAL_AND:
return e ? Bse : Lse;
case fe.LOGICAL_OR:
return e ? Vse : zse;
case fe.MUL:
return Kse;
case fe.PRELU:
return e ? Zse : Qse;
case fe.SQUARED_DIFFERENCE:
return Jse;
case fe.SUB:
return eae;
default:
throw new Error(`BinaryType ${r} is not implemented!`);
}
}
var Q;
(function(r) {
r[r.ABS = 0] = "ABS", r[r.ACOS = 1] = "ACOS", r[r.ACOSH = 2] = "ACOSH", r[r.ASIN = 3] = "ASIN", r[r.ASINH = 4] = "ASINH", r[r.ATAN = 5] = "ATAN", r[r.ATANH = 6] = "ATANH", r[r.CEIL = 7] = "CEIL", r[r.COS = 8] = "COS", r[r.COSH = 9] = "COSH", r[r.ELU = 10] = "ELU", r[r.ERF = 11] = "ERF", r[r.EXP = 12] = "EXP", r[r.EXPM1 = 13] = "EXPM1", r[r.FLOOR = 14] = "FLOOR", r[r.IS_FINITE = 15] = "IS_FINITE", r[r.IS_INF = 16] = "IS_INF", r[r.IS_NAN = 17] = "IS_NAN", r[r.LINEAR = 18] = "LINEAR", r[r.LOG = 19] = "LOG", r[r.LOG1P = 20] = "LOG1P", r[r.LOGICAL_NOT = 21] = "LOGICAL_NOT", r[r.NEG = 22] = "NEG", r[r.RELU = 23] = "RELU", r[r.RELU6 = 24] = "RELU6", r[r.LEAKYRELU = 25] = "LEAKYRELU", r[r.RECIPROCAL = 26] = "RECIPROCAL", r[r.ROUND = 27] = "ROUND", r[r.RSQRT = 28] = "RSQRT", r[r.SELU = 29] = "SELU", r[r.SIGMOID = 30] = "SIGMOID", r[r.SIGN = 31] = "SIGN", r[r.SIN = 32] = "SIN", r[r.SINH = 33] = "SINH", r[r.SOFTPLUS = 34] = "SOFTPLUS", r[r.SQRT = 35] = "SQRT", r[r.SQUARE = 36] = "SQUARE", r[r.STEP = 37] = "STEP", r[r.TAN = 38] = "TAN", r[r.TANH = 39] = "TANH", r[r.TO_INT = 40] = "TO_INT";
})(Q || (Q = {}));
var tae = "return abs(a);";
var rae = `
if (abs(a) > 1.) {
return uniforms.NAN;
}
return acos(a);
`;
var oae = `
if (a < 1.) {
return uniforms.NAN;
}
return acosh(a);
`;
var nae = `
if (abs(a) > 1.) {
return uniforms.NAN;
}
return asin(a);
`;
var sae = "return asinh(a);";
var aae = `
if (isnan(a)) {
return uniforms.NAN;
}
return atan(a);
`;
var iae = `
if (abs(a) > 1.) {
return uniforms.NAN;
}
if (a == 1.) {
return uniforms.INFINITY;
}
if (a == -1.) {
return -uniforms.INFINITY;
}
return atanh(a);
`;
var uae = "return ceil(a);";
var pae = "return cos(a);";
var cae = `
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`;
var lae = "return exp(a) - 1.0;";
var mae = "if (a >= 0.0) { return a; } return (exp(a) - 1.0);";
var dae = `
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`;
var fae = `
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
let p = ${C.ERF_P};
let a1 = ${C.ERF_A1};
let a2 = ${C.ERF_A2};
let a3 = ${C.ERF_A3};
let a4 = ${C.ERF_A4};
let a5 = ${C.ERF_A5};
let sign = sign(a);
let absA = abs(a);
let t = 1.0 / (1.0 + p * absA);
return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));
`;
var hae = "return exp(a);";
var gae = "return floor(a);";
var xae = "return f32(!isnan(a) && !isinf(a));";
var yae = "return f32(isinf(a));";
var bae = "return f32(isnan(a));";
var Cae = "return a;";
var wae = `if (a < 0.0) { return uniforms.NAN; }
return log(a);`;
var Sae = `
if (isnan(a)) { return a; }
return log(1.0 + a);
`;
var Iae = "return f32(!(a >= 1.0));";
var vae = "return -a;";
var kae = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
var Nae = `
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;
var Tae = "return 1.0 / a;";
var _ae = "return select(a, 0.0, a < 0.0);";
var $ae = "return clamp(a, 0.0, 6.0);";
var Eae = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
var Rae = `
return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
`;
var Dae = "return round(a);";
var Aae = "return inverseSqrt(a);";
var Fae = `
if (a >= 0.0) {
return ${C.SELU_SCALE} * a;
} else {
return ${C.SELU_SCALEALPHA} * (exp(a) - 1.0);
}
`;
var Pae = "return 1.0 / (1.0 + exp(-1.0 * a));";
var Oae = "return sign(a);";
var Mae = "return sin(a);";
var Lae = `
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`;
var Bae = `
let epsilon = 1.1920928955078125e-7;
let threshold = log(epsilon) + 2.0;
let too_large = a > -threshold;
let too_small = a < threshold;
let exp_a = exp(a);
if (too_large) {
return a;
} else if (too_small) {
return exp_a;
} else {
return log(exp_a + 1.0);
}
`;
var zae = "return sqrt(a);";
var Vae = "return a * a;";
var Wae = `
if (isnan(a)) {
return a;
}
return select(uniforms.stepAlpha, 1.0, a > 0.0);
`;
var Uae = "return tan(a);";
var Gae = `
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`;
var Hae = "return f32(i32((a)));";
function yi(r, e) {
switch (r) {
case Q.ABS:
return tae;
case Q.ACOS:
return rae;
case Q.ACOSH:
return oae;
case Q.ASIN:
return nae;
case Q.ASINH:
return sae;
case Q.ATAN:
return aae;
case Q.ATANH:
return iae;
case Q.COS:
return pae;
case Q.COSH:
return cae;
case Q.CEIL:
return uae;
case Q.ELU:
return e ? dae : mae;
case Q.ERF:
return fae;
case Q.EXP:
return hae;
case Q.EXPM1:
return lae;
case Q.FLOOR:
return gae;
case Q.IS_FINITE:
return xae;
case Q.IS_INF:
return yae;
case Q.IS_NAN:
return bae;
case Q.LINEAR:
return Cae;
case Q.LOG:
return wae;
case Q.LOG1P:
return Sae;
case Q.LOGICAL_NOT:
return Iae;
case Q.NEG:
return vae;
case Q.LEAKYRELU:
return e ? Nae : kae;
case Q.RECIPROCAL:
return Tae;
case Q.RELU:
return e ? Rae : _ae;
case Q.RELU6:
return e ? Eae : $ae;
case Q.ROUND:
return Dae;
case Q.RSQRT:
return Aae;
case Q.SELU:
return Fae;
case Q.SIGMOID:
return Pae;
case Q.SIGN:
return Oae;
case Q.SIN:
return Mae;
case Q.SINH:
return Lae;
case Q.SOFTPLUS:
return Bae;
case Q.SQRT:
return zae;
case Q.SQUARE:
return Vae;
case Q.STEP:
return Wae;
case Q.TAN:
return Uae;
case Q.TANH:
return Gae;
case Q.TO_INT:
return Hae;
default:
throw new Error(`BinaryType ${r} is not implemented!`);
}
}
function dr(r, e = false, t10 = false, o = 3) {
if (r === null)
return "";
let n = "";
if (r === "linear")
n = yi(Q.LINEAR);
else if (r === "relu")
n = yi(Q.RELU, t10);
else if (r === "elu")
n = yi(Q.ELU, t10);
else if (r === "relu6")
n = yi(Q.RELU6, t10);
else if (r === "prelu")
n = Kc(fe.PRELU, t10);
else if (r === "sigmoid")
n = yi(Q.SIGMOID, t10);
else if (r === "leakyrelu")
n = yi(Q.LEAKYRELU, t10);
else
throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);
let a = Ae(t10 ? 4 : 1), i = "";
return e ? i = `
fn activation(a : ${a}, coords : vec${o}<i32>) -> ${a} {
let b = getPreluActivationWeightsByOutputCoords(coords);
${n}
}` : i = `
fn activation(a : ${a}, coords : vec${o}<i32>) -> ${a} {
${n}
}`, i;
}
function jr(r, e) {
return `
${r ? "value = value + getBiasByOutputCoords(coords);" : ""}
${e ? "value = activation(value, coords);" : ""}
`;
}
function Pv(r, e, t10 = false, o = false, n = false, s = 1) {
y.assert(r && s === 1 || !r, () => `transposeA ${r} is not compatible with component size ${s}`);
let a = `
${r ? "value = getA(batch, col, row);" : "value = getA(batch, row, col);"}
`, i = e ? "value = getB(batch, col, row);" : "value = getB(batch, row, col);";
return `
fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${Ae(s)} {
var value = ${Ae(s)}(0.0);
let col = colIn * ${s};
${t10 && n ? a : `
${r ? "if(row < uniforms.dimAOuter && col < uniforms.dimInner)" : "if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"}
{
${a}
}
`}
return value;
}
fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${Ae(s)} {
let col = colIn * ${s};
var value = ${Ae(s)}(0.0);
${i}
return value;
}
`;
}
function lm(r, e, t10, o, n = false, s = false, a = false, i = 1) {
return `
${Pv(t10, o, n, s, a, i)}
fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ae(i)}) {
let col = colIn * ${i};
${n && s ? "" : "if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"}
{
var value = valueIn;
let coords = vec3<i32>(batch, row, col);
${jr(r, e)}
setOutputAtCoords(coords[0], coords[1], coords[2], value);
}
}
`;
}
var Kae = (r, e) => r ? `
mm_Asub[inputRow][inputCol] = mm_readA(batchA,
kStart + inputRow,
globalRowStart / ${e} + inputCol);
` : `
mm_Asub[inputRow][inputCol] = mm_readA(batchA,
globalRow + innerRow,
kStart / ${e} + inputCol);
`;
var qae = (r, e, t10) => r ? `
let ACached0 = mm_Asub[k * ${e}][localRow];
let ACached1 = mm_Asub[k * ${e} + 1][localRow];
let ACached2 = mm_Asub[k * ${e} + 2][localRow];
${e === 3 ? "" : `let ACached3 = mm_Asub[k * ${e} + 3][localRow];`}
for (var i = 0; i < ${t10}; i++) {
acc[i] = fma(BCached0, vec4<f32>(ACached0[i]), acc[i]);
acc[i] = fma(BCached1, vec4<f32>(ACached1[i]), acc[i]);
acc[i] = fma(BCached2, vec4<f32>(ACached2[i]), acc[i]);
${e === 3 ? "" : "acc[i] = fma(BCached3, vec4<f32>(ACached3[i]), acc[i]);"}
}` : `
for (var i = 0; i < ${t10}; i++) {
let ACached = mm_Asub[tileRow + i][k];
acc[i] = fma(BCached0, vec4<f32>(ACached.x), acc[i]);
acc[i] = fma(BCached1, vec4<f32>(ACached.y), acc[i]);
acc[i] = fma(BCached2, vec4<f32>(ACached.z), acc[i]);
${e === 3 ? "" : "acc[i] = fma(BCached3, vec4<f32>(ACached.w), acc[i]);"}
}`;
function Tp(r, e, t10 = false, o = 32, n = false, s = 32, a = false) {
let i = e[1] * r[1], p = e[0] * r[0], u = t10 ? i : o, c = t10 ? o : i, l = u / e[0], m = o / e[1], d = r[1];
return y.assert((t10 && l === 4 && r[1] === 4 || !t10 && (l === 3 || l === 4)) && u % e[0] === 0 && o % e[1] === 0 && r[0] === 4, () => `If transposeA ${t10} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.
Otherwise, innerElementSize ${l} must be 3 or 4.
tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`), `
var<workgroup> mm_Asub : array<array<vec${l}<f32>, ${u / l}>, ${c}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${p / r[0]}>, ${o}>;
${K()} {
let localRow = i32(localId.y);
let tileRow = localRow * ${d};
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y) * ${d};
let globalCol = i32(globalId.x);
let batch = ${n ? "0" : "i32(globalId.z)"};
let batchA = ${n || !a ? "batch" : "batch % uniforms.aShape[0]"};
let batchB = ${n || !a ? "batch" : "batch % uniforms.bShape[0]"};
let globalRowStart = i32(workgroupId.y) * ${i};
let numTiles = ${n ? `${Math.ceil(s / o)}` : `(uniforms.dimInner - 1) / ${o} + 1`};
var kStart = ${n ? `i32(globalId.z) * ${s}` : "0"};
var acc: array<vec4<f32>, ${d}>;
// Loop over shared dimension.
let tileRowB = localRow * ${m};
for (var t = 0; t < numTiles; t++) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < ${d}; innerRow++) {
let inputRow = tileRow + innerRow;
let inputCol = tileCol;
${Kae(t10, l)}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < ${m}; innerRow++) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol;
mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);
}
kStart = kStart + ${o};
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${o / l}; k++) {
let BCached0 = mm_Bsub[k * ${l}][tileCol];
let BCached1 = mm_Bsub[k * ${l} + 1][tileCol];
let BCached2 = mm_Bsub[k * ${l} + 2][tileCol];
${l === 3 ? "" : `let BCached3 = mm_Bsub[k * ${l} + 3][tileCol];`}
${qae(t10, l, d)}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < ${d}; innerRow++) {
mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
}
}`;
}
var CB = (r) => r ? `
mm_Asub[inputRow][inputCol] = mm_readA(batchA,
kStart + inputRow,
globalRowStart + inputCol);
` : `
mm_Asub[inputRow][inputCol] = mm_readA(batchA,
globalRowStart + inputRow,
kStart + inputCol);
`;
var jae = (r) => r ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];";
function _p(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) {
let p = r[1] * e[1], u = r[0] * e[0], c = t10 ? p : o, l = t10 ? o : p;
y.assert(l % e[1] === 0 && c % e[0] === 0 && o % e[1] === 0, () => `tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);
let m = l / e[1], d = c / e[0], f = o / e[1], h = r[1], g = r[0], x = a ? `
let localRow = i32(localId.y);
let localCol = i32(localId.x);
let globalRowStart = i32(workgroupId.y) * ${p};
let globalColStart = i32(workgroupId.x) * ${u};
// Loop over shared dimension.
for (var t = 0; t < numTiles; t++) {
// Load one tile of A into local memory.
for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {
for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {
${CB(t10)}
}
}
// Load one tile of B into local memory.
for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {
for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {
mm_Bsub[inputRow][inputCol] = mm_readB(batchB,
kStart + inputRow,
globalColStart + inputCol);
}
}
kStart = kStart + ${o};
workgroupBarrier();
// Compute acc values for a single thread.
var BCached : array<f32, ${g}>;
for (var k = 0; k < ${o}; k++) {
for (var inner = 0; inner < ${g}; inner++) {
BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];
}
for (var innerRow = 0; innerRow < ${h}; innerRow++) {
let ACached = ${t10 ? `mm_Asub[k][localRow + innerRow * ${e[1]}];` : `mm_Asub[localRow + innerRow * ${e[1]}][k];`}
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
acc[innerRow][innerCol] =
fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);
}
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < ${h}; innerRow++) {
let gRow = globalRowStart + localRow + innerRow * ${e[1]};
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
let gCol = globalColStart + localCol + innerCol * ${e[0]};
mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
}
}
` : `
let tileRow = i32(localId.y) * ${h};
let tileCol = i32(localId.x) * ${g};
let globalRow = i32(globalId.y) * ${h};
let globalCol = i32(globalId.x) * ${g};
let globalRowStart = i32(workgroupId.y) * ${p};
let tileRowA = i32(localId.y) * ${m};
let tileColA = i32(localId.x) * ${d};
let tileRowB = i32(localId.y) * ${f};
// Loop over shared dimension.
for (var t = 0; t < numTiles; t++) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < ${m}; innerRow++) {
for (var innerCol = 0; innerCol < ${d}; innerCol++) {
let inputRow = tileRowA + innerRow;
let inputCol = tileColA + innerCol;
${CB(t10)}
}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < ${f}; innerRow++) {
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol + innerCol;
mm_Bsub[inputRow][inputCol] = mm_readB(batchB,
kStart + inputRow,
globalCol + innerCol);
}
}
kStart = kStart + ${o};
workgroupBarrier();
// Compute acc values for a single thread.
var BCached : array<f32, ${g}>;
for (var k = 0; k < ${o}; k++) {
for (var inner = 0; inner < ${g}; inner++) {
BCached[inner] = mm_Bsub[k][tileCol + inner];
}
for (var innerRow = 0; innerRow < ${h}; innerRow++) {
${jae(t10)}
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
acc[innerRow][innerCol] =
fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);
}
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < ${h}; innerRow++) {
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
mm_write(batch, globalRow + innerRow, globalCol + innerCol,
acc[innerRow][innerCol]);
}
}
`;
return `
var<workgroup> mm_Asub : array<array<f32, ${c}>, ${l}>;
var<workgroup> mm_Bsub : array<array<f32, ${u}>, ${o}>;
${K()} {
let batch = ${n ? "0" : "i32(globalId.z)"};
let batchA = ${n || !i ? "batch" : "batch % uniforms.aShape[0]"};
let batchB = ${n || !i ? "batch" : "batch % uniforms.bShape[0]"};
let numTiles = ${n ? `${Math.ceil(s / o)}` : `(uniforms.dimInner - 1) / ${o} + 1`};
var kStart = ${n ? `i32(globalId.z) * ${s}` : "0"};
var acc : array<array<f32, ${g}>, ${h}>;
// Without this initialization strange values show up in acc.
for (var innerRow = 0; innerRow < ${h}; innerRow++) {
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
acc[innerRow][innerCol] = 0.0;
}
}
${x}
}
`;
}
var Xae = (r) => r ? `
mm_readA(batchA, colA, globalRow),
mm_readA(batchA, colA + 1, globalRow),
mm_readA(batchA, colA + 2, globalRow),
mm_readA(batchA, colA + 3, globalRow)
` : `
mm_readA(batchA, globalRow, colA),
mm_readA(batchA, globalRow, colA + 1),
mm_readA(batchA, globalRow, colA + 2),
mm_readA(batchA, globalRow, colA + 3)
`;
function Yae(r, e = false) {
y.assert(r[1] === 1 && r[2] === 1, () => `A linear work group size is required. But got ${r}.`);
let t10 = r[0] * 4;
return `
var<workgroup> mm_Asub : array<vec4<f32>, ${r[0]}>;
${K()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / ${t10} + 1;
let batch = i32(globalId.z);
let batchA = batch % uniforms.aShape[0];
let batchB = batch % uniforms.bShape[0];
// Without this initialization strange values show up in acc.
var acc = 0.0;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t++) {
// Load one tile of A into local memory.
let colA = t * ${t10} + tileCol * 4;
mm_Asub[tileCol] = vec4<f32>(${Xae(e)});
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${t10 / 4}; k++) {
let rowB = t * ${t10} + k * 4;
let BCached = vec4<f32>(mm_readB(batchB, rowB, globalCol),
mm_readB(batchB, rowB + 1, globalCol),
mm_readB(batchB, rowB + 2, globalCol),
mm_readB(batchB, rowB + 3, globalCol));
let ACached = mm_Asub[k];
acc = acc + dot(ACached, BCached);
}
workgroupBarrier();
}
mm_write(batch, globalRow, globalCol, acc);
}
`;
}
var Kg = class {
constructor(e, t10, o = false, n = false, s = null, a = null, i = null, p = false) {
this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t10, this.dispatchLayout = { x: [2], y: [1], z: [0] };
let u = o ? e[1] : e[2];
if (this.isVec4 = (u % 4 === 0 && !o || t10[1] % 4 === 0 && o) && t10[2] % 4 === 0 && !n, this.outputComponent = this.isVec4 ? 4 : 1, this.isVectorA = t10[1] === 1 && !o, !this.isVec4 && this.isVectorA)
this.elementsPerThread = [1, 1, 1], this.workgroupSize = [32, 1, 1];
else {
let m = Av(t10[1], u, t10[2], o);
this.workgroupSize = m.workgroupSize, this.elementsPerThread = m.elementsPerThread;
}
this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread);
let c = s != null, l = i != null;
c && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.sequentialAccessByThreads = p, this.transposeA = o, this.transposeB = n, this.addBias = c, this.activation = a, this.hasPreluActivationWeights = l, [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(t10[1], t10[2], u), this.shaderKey = `matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`;
}
getShapeFit(e, t10, o) {
let n = this.workgroupSize[1] * this.elementsPerThread[1], s = this.workgroupSize[0] * this.elementsPerThread[0];
!this.isVec4 && this.isVectorA ? this.tileInner = this.workgroupSize[0] * 4 : this.tileInner = s;
let a = e % n === 0, i = t10 % s === 0, p = o % this.tileInner === 0;
return [a, i, p];
}
getUserCode() {
return `
${dr(this.activation, this.hasPreluActivationWeights, this.isVec4)}
${lm(this.addBias, this.activation, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)}
${this.isVec4 ? Tp(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, true) : this.isVectorA ? Yae(this.workgroupSize, this.transposeA) : _p(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.sequentialAccessByThreads, true)}
`;
}
};
function Qae(r) {
return `
var<workgroup> sumValues : array<f32, ${r}>;
${K()} {
let coords = getOutputCoords();
let batch = coords[0];
let batchA = batch % uniforms.aShape[0];
let batchB = batch % uniforms.bShape[0];
let row = coords[1];
let col = coords[2];
var sum = 0.0;
let Length = uniforms.dimInner;
for (var k = i32(localId.x); k < Length; k = k + ${r}) {
let dataA = mm_readA(batchA, row, k);
let dataB = mm_readB(batchB, k, col);
sum = sum + dataA * dataB;
}
sumValues[localId.x] = sum;
workgroupBarrier();
for(var currentSize = ${r / 2}u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`;
}
var qg = class {
constructor(e, t10 = false, o = false, n = null, s = null, a = null) {
this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize);
let i = n != null, p = a != null;
i && this.variableNames.push("bias"), p && this.variableNames.push("preluActivationWeights"), this.transposeA = t10, this.transposeB = o, this.addBias = i, this.activation = s, this.hasPreluActivationWeights = p, this.shaderKey = `matMulReduce_${this.activation}_${t10}_${o}`;
}
getUserCode() {
return `
${dr(this.activation, this.hasPreluActivationWeights)}
${lm(this.addBias, this.activation, this.transposeA, this.transposeB)}
${Qae(this.workgroupSize[0])}
`;
}
};
function Zae(r) {
let e = r[1], t10 = r[0], o = e > t10 ? e : t10;
return `
var<workgroup> mm_Asub : array<array<f32, ${o}>, ${e}>;
var<workgroup> mm_Bsub : array<array<f32, ${t10}>, ${o}>;
// If the output size is small for matrix multiplication, avoid to use vec4
// and handle some elements per thread to optimally utilize the ALU.
// Read data from global memory to registers firstly, then store them into
// shared memory, so it is instruction-Level parallelism for arithmetic
// operations and others handle IO operations between barrier api, makes ALU
// and load/store units work simultaneously, could improves the performance.
${K()} {
let tileRow = i32(localId.y);
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y);
let globalCol = i32(globalId.x);
let batch = i32(globalId.z);
let batchA = batch % uniforms.aShape[0];
let batchB = batch % uniforms.bShape[0];
// uniforms.dimInner should be greater than 0.
let numTiles = (uniforms.dimInner - 1) / ${o} + 1;
var acc = 0.0;
var globalColA = tileCol;
var globalRowB = 0;
var regA = mm_readA(batchA, globalRow, globalColA);
var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);
var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);
globalColA = globalColA + ${o};
globalRowB = globalRowB + ${o};
for (var t = 0; t < numTiles; t = t + 1) {
mm_Asub[tileRow][tileCol] = regA;
mm_Bsub[2 * tileRow][tileCol] = regB0;
mm_Bsub[2 * tileRow + 1][tileCol] = regB1;
workgroupBarrier();
regA = mm_readA(batchA, globalRow, globalColA);
regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);
regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);
globalColA = globalColA + ${o};
globalRowB = globalRowB + ${o};
for (var k = 0; k < ${o}; k = k + 1) {
acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];
}
workgroupBarrier();
}
mm_write(batch, globalRow, globalCol, acc);
}
`;
}
var jg = class {
constructor(e, t10, o, n = false, s = false, a = null, i = null, p = null) {
this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [16, 8, 1], this.outputShape = o, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(o[2] / this.workgroupSize[0]), Math.ceil(o[1] / this.workgroupSize[1]), o[0]];
let u = a != null;
u && this.variableNames.push("bias");
let c = p != null;
c && this.variableNames.push("preluActivationWeights"), this.transposeA = n, this.transposeB = s, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = c, this.shaderKey = `matMulSmallOutputSize_${this.activation}_${n}_${s}`;
}
getUserCode() {
return `
${dr(this.activation, this.hasPreluActivationWeights)}
${lm(this.addBias, this.activation, this.transposeA, this.transposeB)}
${Zae(this.workgroupSize)}
`;
}
};
var Xg = class {
constructor(e, t10, o = false, n = false) {
this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [8, 8, 1], this.atomic = true, this.splitedDimInner = 128, y.assert(e[0] === 1, () => "MatMulSplitKProgram only supports batch = 1."), this.outputShape = e, this.dispatchLayout = { x: [2], y: [1], z: [0, 3] };
let s = (o && this.outputShape[1] % 4 === 0 || !o && t10 % 4 === 0) && this.outputShape[2] % 4 === 0;
this.elementsPerThread = [4, 4, this.splitedDimInner], this.outputComponent = s ? 4 : 1, s || (this.outputShape[1] < 16 && (this.elementsPerThread[1] = 1), this.outputShape[2] < 16 && (this.elementsPerThread[0] = 1)), this.dispatch = q(this.dispatchLayout, [this.outputShape[0], this.outputShape[1], this.outputShape[2], t10], this.workgroupSize, this.elementsPerThread), this.transposeA = o, this.transposeB = n, this.shaderKey = `matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`;
}
getUserCode() {
let e = this.outputComponent;
return `
${Pv(false, this.transposeB, false, false, false, e)}
fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Ae(e)}) {
let col = colIn * ${e};
if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {
let coords = vec3<i32>(batch, row, col);
let flatIndex = getOutputIndexFromCoords(coords);
// The problem is that we should initialize output to zero before using.
// Otherwise, the original value will be added to the result.
for (var i = 0; i < ${e}; i = i + 1) {
${Bs("&result[flatIndex + i]", `${e > 1 ? "value[i]" : "value"}`, "float32")}
}
}
}
${e === 4 ? Tp(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner) : _p(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner)}
`;
}
};
var Yg = class {
constructor(e, t10 = null, o = null, n = null) {
this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t10 != null, this.hasPreluActivationWeights = n != null, this.activation = o, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.shaderKey = `biasActivation_${o}`;
}
getUserCode() {
return `
${dr(this.activation, this.hasPreluActivationWeights)}
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
var value = getXByOutputIndex(index);
${jr(this.addBias, this.activation)}
setOutputAtIndex(index, value);
}
}
`;
}
};
var Qg = class {
constructor(e) {
this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "fill";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.value);
}
}
`;
}
};
function Vt(r) {
let { backend: e, attrs: t10 } = r, { shape: o, value: n } = t10, { dtype: s } = t10;
if (s = s || y.inferDtype(n), s === "string") {
let a = y.getArrayFromDType(s, y.sizeFromShape(o));
return a.fill(n), e.makeTensorInfo(o, s, a);
} else {
let a = new Qg(o), i = [{ type: "float32", data: [n] }];
return e.runWebGPUProgram(a, [], s, i);
}
}
var wB = { kernelName: ea, backendName: "webgpu", kernelFunc: Vt };
function pe(r) {
let { inputs: e, attrs: t10 } = r, { x: o } = e, { shape: n } = t10, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s), i = y.sizeFromShape(a);
return y.assert(s === i, () => `The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype };
}
var SB = { kernelName: ia, backendName: "webgpu", kernelFunc: pe };
function $p({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) {
let u = r.shape.length, c = e.shape.length, l = t10 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t10 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), S = Sr.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]);
y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t10} and transposeB=${o} must match.`);
let k = t10 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = pe({ inputs: { x: r }, backend: n, attrs: { shape: k } }), R = pe({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), D = [E, R], F = Math.max(x, b), O = [E, R], M = [{ type: "int32", data: [d] }, { type: "int32", data: [f] }, { type: "int32", data: [l] }], L, B, z = [F, d, f], U = P().get("WEBGPU_MATMUL_PROGRAM_TYPE");
if (U < 0) {
let H = P().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), X = H > 0 ? H : n.thresholdToIncreaseWorkgroups, J = F * Math.ceil(d / 32) * Math.ceil(f / 32);
J <= X || d <= 8 && J <= X * 2 ? F * d * f <= 128 ? U = Oo.MatMulReduceProgram : F === 1 && m >= 2e3 ? U = Oo.MatMulSplitKProgram : U = Oo.MatMulSmallOutputSizeProgram : U = Oo.MatMulPackedProgram;
}
switch (U) {
case Oo.MatMulReduceProgram:
L = new qg(z, t10, o, s, p, a);
break;
case Oo.MatMulSplitKProgram: {
if (B = Vt({ backend: n, attrs: { shape: z, value: 0, dtype: r.dtype } }), L = new Xg(z, m, t10, o), s || p) {
B = n.runWebGPUProgram(L, O, r.dtype, M, B);
let X = new Yg(B.shape, s, p, a), J = null, re = [B];
s && re.push(s), a && re.push(a), p === "leakyrelu" && (J = [{ type: "float32", data: [i] }], X.uniforms += " alpha : f32,");
let ne = n.runWebGPUProgram(X, re, B.dtype, J);
D.push(B);
let ee = pe({ inputs: { x: ne }, backend: n, attrs: { shape: S } });
D.push(ne);
for (let oe of D)
n.disposeData(oe.dataId);
return ee;
}
break;
}
case Oo.MatMulSmallOutputSizeProgram:
L = new jg(k, _, z, t10, o, s, p, a);
break;
case Oo.MatMulPackedProgram:
let H = n.adapterInfo.isIntel();
L = new Kg(k, z, t10, o, s, p, a, H);
break;
default:
throw new Error(`Unsupported MatMulProgramType ${U}.`);
}
s && O.push(s), a && O.push(a), p === "leakyrelu" && (M.push({ type: "float32", data: [i] }), L.uniforms += " alpha : f32,"), B = n.runWebGPUProgram(L, O, r.dtype, M, B);
let j = pe({ inputs: { x: B }, backend: n, attrs: { shape: S } });
D.push(B);
for (let H of D)
n.disposeData(H.dataId);
return j;
}
function Jae(r) {
let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o;
return $p({ a: n, b: s, transposeA: p, transposeB: u, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c });
}
var IB = { kernelName: bo, backendName: "webgpu", kernelFunc: Jae };
var mm = class {
constructor(e, t10, o) {
this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = C.assertAndGetBroadcastShape(t10, o), this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e;
}
getUserCode() {
return `
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${Kc(this.op, false)}
}
${K("index")} {
if(index < uniforms.size) {
let areal = getARealByOutputIndex(index);
let aimag = getAImagByOutputIndex(index);
let breal = getBRealByOutputIndex(index);
let bimag = getBImagByOutputIndex(index);
setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`;
}
};
var bi = class {
constructor(e, t10, o) {
if (this.size = true, this.variableNames = ["A", "B"], this.outputShape = C.assertAndGetBroadcastShape(t10, o), this.dispatchLayout = Z(this.outputShape), this.op = e, this.useSharedMemoryWithA = t10.length <= 1 && o.length > 1 && t10[0] < 128, this.useSharedMemoryWithB = o.length <= 1 && t10.length > 1 && o[0] < 128, this.useSharedMemoryWithA || this.useSharedMemoryWithB)
this.outputComponent = 1, this.variableComponents = [1, 1], this.lastDimensionSize = this.useSharedMemoryWithB ? o[0] : t10[0], this.shaderKey = `binary_${e}_${this.lastDimensionSize}`, this.type = "shared", this.workgroupSize = [256, 1, 1];
else {
let n = t10.length > 0 && t10[t10.length - 1] % 4 === 0, s = o.length > 0 && o[o.length - 1] % 4 === 0;
n && s ? (this.outputComponent = 4, this.variableComponents = [4, 4]) : n && (y.isScalarShape(o) || o[o.length - 1] === 1) || s && (y.isScalarShape(t10) || t10[t10.length - 1] === 1) ? (this.outputComponent = 4, this.variableComponents = n ? [4, 1] : [1, 4]) : (this.outputComponent = 1, this.variableComponents = [1, 1]), this.type = "nonshared", this.shaderKey = `binary_${e}_${this.variableComponents}`, this.workgroupSize = [128, 1, 1];
}
this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.outputComponent, 1, 1]);
}
getUserCode() {
let e, t10 = this.outputComponent === 4 ? "vec4<f32>" : "f32", o = `
fn binaryOperation(a : ${t10}, b : ${t10}) -> ${t10} {
${Kc(this.op, this.outputComponent === 4)}
};
`;
if (this.type === "shared") {
let n = this.lastDimensionSize > 1 ? `coords[${this.outputShape.length - 1}]` : "0", s = this.useSharedMemoryWithB ? `let a = getAByOutputIndex(index);
let b = sharedBuf[${n}];` : `let a = sharedBuf[${n}];
let b = getBByOutputIndex(index);`;
e = `
${o}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${K("index")} {
// Fill in the shared memory buffer.
let localIndex = i32(localId.x);
if(localIndex < ${this.lastDimensionSize}) {
sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB ? "B" : "A"}[localIndex]);
}
workgroupBarrier();
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
${s}
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`;
} else
e = `
${o}
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index * ${this.outputComponent});
let a = ${t10}(getAByOutputCoords(coords));
let b = ${t10}(getBByOutputCoords(coords));
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`;
return e;
}
};
function At(r) {
let { inputs: e } = r, { x: t10 } = e;
return r.backend.incRef(t10.dataId), { dataId: t10.dataId, shape: t10.shape, dtype: t10.dtype };
}
var vB = { kernelName: xo, backendName: "webgpu", kernelFunc: At };
function fo(r) {
let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.makeTensorInfo(o.shape, "complex64"), a = t10.tensorMap.get(s.dataId), i = At({ inputs: { x: o }, backend: t10 }), p = At({ inputs: { x: n }, backend: t10 });
return a.complexTensorInfos = { real: i, imag: p }, s;
}
var kB = { kernelName: Ti, backendName: "webgpu", kernelFunc: fo };
var Xr = class {
constructor(e, t10, o = "") {
this.variableNames = ["A"], this.size = true;
let n = 128;
this.workgroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.op = t10, o !== "" && (this.uniforms = o), this.shaderKey = `unary_${t10}`;
}
getUserCode() {
return `
fn unaryOperation(a : f32) -> f32 {
${yi(this.op, false)}
}
${K("index")} {
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
setOutputAtIndex(index, unaryOperation(a));
}
}
`;
}
};
function xe({ opType: r, cpuKernelImpl: e, dtype: t10 }) {
return ({ inputs: o, backend: n }) => {
let { x: s } = o, a = n, i = t10 || s.dtype;
if (a.shouldExecuteOnCPU([s]) && e != null) {
let u = a.tensorMap.get(s.dataId), c = e(u.values, i);
return a.makeTensorInfo(s.shape, i, c);
}
let p = new Xr(s.shape, r);
return a.runWebGPUProgram(p, [s], i);
};
}
function et({ opType: r, cpuKernelImpl: e, supportsComplex: t10 = false, dtype: o }) {
return ({ inputs: n, backend: s }) => {
let { a, b: i } = n, p = s;
if (t10 && a.dtype === "complex64") {
let l = p.tensorMap.get(a.dataId), m = p.tensorMap.get(i.dataId), d, f;
if (r !== fe.MUL)
[d, f] = [[l.complexTensorInfos.real, m.complexTensorInfos.real], [l.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((g) => {
let [x, b] = g, w = { dataId: x.dataId, dtype: x.dtype, shape: a.shape }, S = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, k = new bi(r, a.shape, i.shape);
return p.runWebGPUProgram(k, [w, S], dt(x.dtype, b.dtype));
});
else {
let g = new mm(fe.COMPLEX_MULTIPLY_REAL, a.shape, i.shape), x = new mm(fe.COMPLEX_MULTIPLY_IMAG, a.shape, i.shape), b = [{ dataId: l.complexTensorInfos.real.dataId, dtype: l.complexTensorInfos.real.dtype, shape: a.shape }, { dataId: l.complexTensorInfos.imag.dataId, dtype: l.complexTensorInfos.imag.dtype, shape: a.shape }, { dataId: m.complexTensorInfos.real.dataId, dtype: m.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: m.complexTensorInfos.imag.dataId, dtype: m.complexTensorInfos.imag.dtype, shape: i.shape }];
d = p.runWebGPUProgram(g, b, "float32"), f = p.runWebGPUProgram(x, b, "float32");
}
let h = fo({ inputs: { real: d, imag: f }, backend: p });
return p.disposeData(d.dataId), p.disposeData(f.dataId), h;
}
let u = o || dt(a.dtype, i.dtype);
if ((a.dtype === "string" || i.dtype === "string" || p.shouldExecuteOnCPU([a, i])) && e != null) {
let l = p.tensorMap.get(a.dataId).values, m = p.tensorMap.get(i.dataId).values, d = a.dtype === "string" ? C.fromUint8ToStringArray(l) : l, f = a.dtype === "string" ? C.fromUint8ToStringArray(m) : m, [h, g] = e(a.shape, i.shape, d, f, u);
return p.makeTensorInfo(g, u, h);
}
let c = new bi(r, a.shape, i.shape);
return p.runWebGPUProgram(c, [a, i], u);
};
}
var { addImpl: NB, castImpl: TB, ceilImpl: _B, concatImpl: $B, equalImpl: EB, expImpl: RB, expm1Impl: DB, floorImpl: AB, floorDivImpl: FB, gatherNdImpl: PB, gatherV2Impl: OB, greaterEqualImpl: MB, greaterImpl: LB, lessEqualImpl: BB, lessImpl: zB, logImpl: VB, maxImpl: WB, maximumImpl: UB, minimumImpl: GB, multiplyImpl: HB, negImpl: KB, notEqualImpl: qB, prodImpl: jB, rangeImpl: XB, rsqrtImpl: YB, scatterImpl: QB, simpleAbsImpl: ZB, sliceImpl: JB, stridedSliceImpl: ez, stringNGramsImpl: tz, subImpl: rz, tileImpl: oz, topKImpl: nz, transposeImpl: sz, uniqueImpl: o3t } = Sc;
var eie = xe({ opType: Q.ABS, cpuKernelImpl: ZB });
var az = { kernelName: Gs, backendName: "webgpu", kernelFunc: eie };
var tie = xe({ opType: Q.ACOS });
var iz = { kernelName: zo, backendName: "webgpu", kernelFunc: tie };
var rie = xe({ opType: Q.ACOSH });
var uz = { kernelName: Vo, backendName: "webgpu", kernelFunc: rie };
var oie = et({ opType: fe.ADD, cpuKernelImpl: NB, supportsComplex: true });
var pz = { kernelName: no, backendName: "webgpu", kernelFunc: oie };
var Zg = class {
constructor(e) {
this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t10, o) => `T${o}`), this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN";
}
getUserCode() {
let e = [];
this.variableNames.forEach((n) => {
e.push(`let v${n} = get${n}ByOutputCoords(coords);`);
});
let t10 = this.variableNames.map((n) => `v${n}`).join(" + ");
return `
${K("index")} {
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
${e.join(`
`)}
setOutputAtIndex(flatIndex, ${t10});
}
}
}
`;
}
};
function nie(r) {
let { inputs: e, backend: t10 } = r, o = e;
if (o.length === 1)
return At({ inputs: { x: o[0] }, backend: t10 });
let n = o.map((i) => i.dtype).reduce((i, p) => dt(i, p)), s = o.map((i) => i.shape), a = new Zg(s);
return t10.runWebGPUProgram(a, o, n);
}
var cz = { kernelName: Wo, backendName: "webgpu", kernelFunc: nie };
var Jg = class {
constructor(e, t10) {
this.variableNames = ["A"], this.workgroupSize = [16, 16, 1];
let o = new Array(e.length);
for (let n = 0; n < o.length; n++)
o[n] = e[t10[n]];
this.outputShape = o, this.dispatchLayout = { x: [0], y: [1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [1, 1, 1]), this.shaderKey = "transposeShared";
}
getUserCode() {
y.assert(this.workgroupSize[0] === this.workgroupSize[1], () => `Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);
let e = this.workgroupSize[0];
return `
var<workgroup> tile : array<array<f32, ${this.workgroupSize[0] + 1}>, ${this.workgroupSize[0]}>;
${K()} {
var x = i32(workgroupId.x) * ${e} + i32(localId.x);
var y = i32(workgroupId.y) * ${e} + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] = f32(A[y * width + x]);
}
workgroupBarrier();
x = i32(workgroupId.y) * ${e} + i32(localId.x);
y = i32(workgroupId.x) * ${e} + i32(localId.y);
if (x < height && y < width) {
setOutputAtIndex((y * height + x), tile[localId.x]
[localId.y]);
}
}
`;
}
};
var ex = class {
constructor(e, t10) {
this.variableNames = ["A"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true;
let o = new Array(e.length);
for (let n = 0; n < o.length; n++)
o[n] = e[t10[n]];
this.outputShape = o, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.newDim = t10, this.shaderKey = `transpose_${t10}`;
}
getUserCode() {
let e = Nt(this.outputShape.length), t10 = sie(this.newDim);
return `
${K("index")} {
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let resRC = getCoordsFromIndex(flatIndex);
setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(
${e}(${t10}), uniforms.aShape)]);
}
}
}
`;
}
};
function sie(r) {
let e = r.length;
if (e > 6)
throw Error(`Transpose for rank ${e} is not yet supported`);
let t10 = new Array(e);
for (let o = 0; o < r.length; o++)
t10[r[o]] = `resRC.${Po(o)}`;
return t10.join();
}
function rr(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t10, i = n.shape.length, p = new Array(i);
for (let c = 0; c < p.length; c++)
p[c] = n.shape[s[c]];
if (t10.shouldExecuteOnCPU([n])) {
let l = a.tensorMap.get(n.dataId).values, m = sz(l, n.shape, n.dtype, s, p);
return t10.makeTensorInfo(p, n.dtype, m);
}
if (n.shape.length === 2 && y.arraysEqual(s, [1, 0])) {
let c = new Jg(n.shape, s);
return a.runWebGPUProgram(c, [n], n.dtype);
}
let u = new ex(n.shape, s);
return a.runWebGPUProgram(u, [n], n.dtype);
}
var lz = { kernelName: ao, backendName: "webgpu", kernelFunc: rr };
var tx = class {
constructor(e, t10, o) {
this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [e.batchSize, e.inSize];
let [n] = C.computeOutAndReduceShapes(this.inputShape, [1]);
this.outputShape = n.length === 0 ? [1] : n, e.inSize >= 32768 && o >= 512 ? this.workgroupSize = [512, 1, 1] : e.inSize >= 4096 ? this.workgroupSize = [256, 1, 1] : this.workgroupSize = [64, 1, 1], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t10, this.shaderKey = `reduce_${t10}`;
}
getUserCode() {
let e = "", t10 = "0.0", o = this.workgroupSize[0];
this.reduceType === "min" || this.reduceType === "max" ? (e = `
if (isnan(candidate)) {
bestValue = uniforms.NAN;
} else if (!isnan(bestValue) && candidate ${this.reduceType === "min" ? "<" : ">"} bestValue)
{ bestValue = candidate; }`, t10 = "f32(x[offset])") : this.reduceType === "sum" || this.reduceType === "mean" ? e = " bestValue = bestValue + candidate; " : this.reduceType === "prod" ? (e = " bestValue = bestValue * candidate; ", t10 = "1.0") : this.reduceType === "all" ? (e = " bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ", t10 = "1.0") : this.reduceType === "any" && (e = " bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ", t10 = "0.0");
let n = this.reduceType === "mean" ? "setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));" : "setOutputAtIndex(outputIndex, bestValue);";
return `
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${`
var<workgroup> xBestValues : array<f32, ${o}>;
`}
fn getOffset(outputIndex : i32) -> i32 {
let outputCoords = getCoordsFromIndex(outputIndex);
let offset = ${this.outputShape.length === 1 ? "outputCoords" : "outputCoords[0]"} * uniforms.reduceSize;
return offset;
}
${K("index")} {
let outputIndex = index / ${o};
let offset = getOffset(outputIndex);
var bestValue = ${t10};
let Length = uniforms.reduceSize;
let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
k = k + ${o}) {
let candidate = f32(x[offset + k]);
${e}
}
xBestValues[localId.x] = bestValue;
workgroupBarrier();
var reduceSize = min(u32(Length), ${o}u);
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
currentSize = reduceSize / 2u) {
let interval = DIV_CEIL(reduceSize, 2u);
if (localId.x < currentSize) {
let candidate = xBestValues[localId.x + interval];
${e}
xBestValues[localId.x] = bestValue;
}
reduceSize = interval;
workgroupBarrier();
}
if (localId.x == 0u && outputIndex < uniforms.size) {
${n}
}
}
`;
}
};
function Yr(r, e, t10, o, n) {
let s = r.shape.length, a = [], i = y.parseAxisParam(e, r.shape), p = i, u = C.getAxesPermutation(p, s), c = r;
u != null && (c = rr({ inputs: { x: r }, attrs: { perm: u }, backend: n }), p = C.getInnerMostAxes(p.length, s), a.push(c)), C.assertAxesAreInnerMostDims(o, p, s);
let [l, m] = C.computeOutAndReduceShapes(c.shape, p), d = l;
t10 && (d = C.expandShapeToKeepDim(l, i));
let f;
if ((o === "max" || o === "prod") && n.shouldExecuteOnCPU([c])) {
let h = n.tensorMap.get(c.dataId).values;
switch (o) {
case "max":
let g = WB(h, y.sizeFromShape(m), d, r.dtype);
f = n.makeTensorInfo(d, r.dtype, g);
break;
case "prod":
let { outVals: x, outShape: b, outDtype: w } = jB(c.shape, c.dtype, h, p);
f = n.makeTensorInfo(b, w, x);
break;
default:
throw new Error(`${o} CPU implementation is not yet supported.`);
}
} else {
let h = y.sizeFromShape(m), x = y.sizeFromShape(c.shape) / h, b = { windowSize: h, inSize: h, batchSize: x, outSize: 1 }, w = o === "mean" ? "float32" : Za(r.dtype), S = [{ type: "int32", data: [h] }], k = new tx(b, o, n.device.limits.maxComputeWorkgroupSizeX), _ = n.runWebGPUProgram(k, [c], w, S);
a.push(_), f = pe({ inputs: { x: _ }, attrs: { shape: d }, backend: n });
}
return a.forEach((h) => n.disposeData(h.dataId)), f;
}
function aie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o;
return Yr(n, a, s, "all", t10);
}
var mz = { kernelName: Uo, backendName: "webgpu", kernelFunc: aie };
function iie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o;
return Yr(n, a, s, "any", t10);
}
var dz = { kernelName: Go, backendName: "webgpu", kernelFunc: iie };
var qc = class {
constructor(e, t10, o) {
this.workgroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true;
let n = [t10];
this.op = o === "min" ? "<" : ">";
let [s, a] = C.computeOutAndReduceShapes(e, n);
this.outputShape = s.length === 0 ? [1] : s, this.dispatchLayout = Z(this.outputShape), y.sizeFromShape(a) < 32 || y.sizeFromShape(s) > 1e3 ? (this.type = "plain", this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize)) : (this.type = "shared", this.dispatch = q(this.dispatchLayout, this.outputShape, [1, 1, 1])), this.inputShape = e, this.shaderKey = `argMinMax_${this.op}_${this.type}`;
}
getUserCode() {
let e = this.workgroupSize[0], t10 = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${Po(this.inputShape.length - 1)}`, o = () => {
let n = "";
if (this.outputShape.length === 1)
this.inputShape.length !== 1 && (n += "outputCoords,");
else
for (let s = 0; s < this.outputShape.length; s++)
n += `outputCoords.${Po(s)},`;
return n;
};
return this.type === "shared" ? `
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${`
var<workgroup> xBestIndices : array<i32, ${e}>;
var<workgroup> xBestValues : array<f32, ${e}>;
`}
${K("index")} {
let outputIndex = index / ${e};
let reduceLength = ${t10()};
var bestIndex = i32(localId.x);
var bestValue = uniforms.infinityValue;
let outputCoords = getCoordsFromIndex(outputIndex);
for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;
k = k + ${e}) {
let candidate = getX(${o()} k);
if (!isnan(candidate) && candidate ${this.op} bestValue) {
bestValue = candidate;
bestIndex = k;
}
}
xBestValues[localId.x] = bestValue;
xBestIndices[localId.x] = bestIndex;
workgroupBarrier();
var reduceSize = min(u32(reduceLength), ${e}u);
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
currentSize = reduceSize / 2u) {
let interval = DIV_CEIL(reduceSize, 2u);
if (localId.x < currentSize) {
let candidate = xBestValues[localId.x + interval];
if (candidate ${this.op} bestValue) {
bestValue = candidate;
xBestValues[localId.x] = bestValue;
xBestIndices[localId.x] = xBestIndices[localId.x + interval];
}
}
reduceSize = interval;
workgroupBarrier();
}
if (localId.x == 0u && outputIndex < uniforms.size) {
setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
}
}
` : `
${K("index")} {
if (index < uniforms.size) {
let outputCoords = getCoordsFromIndex(index);
var bestIndex = 0;
var bestValue = getX(${o()} 0);
let reduceLength = ${t10()};
for (var i = 1; i < reduceLength; i++) {
let candidate = getX(${o()} i);
if (candidate ${this.op} bestValue) {
bestValue = candidate;
bestIndex = i;
}
}
setOutputAtIndexI32(index, bestIndex);
}
}
`;
}
};
function uie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = rr({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), C.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length);
let c = new qc(p.shape, a[0], "max"), l = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], m = t10.runWebGPUProgram(c, [p], "int32", l);
return u.forEach((d) => t10.disposeData(d.dataId)), m;
}
var fz = { kernelName: Hs, backendName: "webgpu", kernelFunc: uie };
function pie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = C.getAxesPermutation(a, n.shape.length), p = n, u = [];
i != null && (p = rr({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = C.getInnerMostAxes(a.length, p.shape.length)), C.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length);
let c = new qc(p.shape, a[0], "min"), l = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], m = t10.runWebGPUProgram(c, [p], "int32", l);
return u.forEach((d) => t10.disposeData(d.dataId)), m;
}
var hz = { kernelName: Ks, backendName: "webgpu", kernelFunc: pie };
var cie = xe({ opType: Q.ASIN });
var gz = { kernelName: Ho, backendName: "webgpu", kernelFunc: cie };
var lie = xe({ opType: Q.ASINH });
var xz = { kernelName: Ko, backendName: "webgpu", kernelFunc: lie };
var mie = xe({ opType: Q.ATAN });
var yz = { kernelName: qo, backendName: "webgpu", kernelFunc: mie };
var die = et({ opType: fe.ATAN2 });
var bz = { kernelName: Xo, backendName: "webgpu", kernelFunc: die };
var fie = xe({ opType: Q.ATANH });
var Cz = { kernelName: jo, backendName: "webgpu", kernelFunc: fie };
var rx = class {
constructor(e) {
this.variableNames = ["x"], this.uniforms = "strides : vec2<i32>,", this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d = coords[3];
let xRCCorner = coords.yz * uniforms.strides;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
let value = getX(batch, xRCorner, xCCorner, d);
setOutputAtIndex(index, value);
}
}
`;
}
};
var Da = class {
constructor(e, t10, o = false, n = false, s = false) {
if (this.variableNames = ["x"], this.uniforms = "strides : vec2<i32>, pads : vec2<i32>, dilations : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,", this.workgroupSize = [128, 1, 1], this.size = true, t10 === "avg" && o)
throw new Error("Cannot compute positions for average pool.");
this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.poolType = t10, this.computePositions = o, this.flattenPositions = n, this.includeBatchIndex = s, this.shaderKey = `pool2D_${t10}_${o}_${n}_${s}`;
}
getUserCode() {
let e;
this.poolType === "avg" ? e = "resultValue = resultValue + value; count = count + 1.0;" : this.computePositions ? e = `let currMaxValue = mix(value, maxValue, maxValueFound);
if (value >= currMaxValue) {
maxValue = value;
maxValueFound = 1.0;
maxPosition = ${this.flattenPositions ? this.includeBatchIndex ? "((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d" : "(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d" : "wR * uniforms.filterDims.y + wC"};
}` : e = "resultValue = max(value, resultValue);";
let t10 = "resultValue";
return this.poolType === "avg" && (t10 = "resultValue / max(count, 1.0)"), `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d = coords[3];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.strides - uniforms.pads;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
${this.computePositions ? `var maxValue = 0.0;
var maxValueFound = 0.0;
var maxPosition = 0;` : `var resultValue = ${this.poolType === "avg" ? "0.0" : "-1.0 / pow(10.0, -20.0)"};`}
var count = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) {
let xR = xRCorner + wR;
if (xR < 0 || xR >= uniforms.convDims.x) {
continue;
}
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) {
let xC = xCCorner + wC;
if (xC < 0 || xC >= uniforms.convDims.y) {
continue;
}
let value = getX(batch, xR, xC, d);
${e}
}
}
${this.computePositions ? "setOutputAtIndexI32(index, maxPosition);" : `setOutputAtIndex(index, ${t10});`}
}
}
`;
}
};
var wu = class {
constructor(e, t10, o = false, n = false, s = false) {
if (this.variableNames = ["x"], this.uniforms = "strides : vec3<i32>, pads : vec3<i32>, convDims : vec3<i32>, filterDims : vec3<i32>,", this.workgroupSize = [128, 1, 1], this.size = true, t10 === "avg" && o)
throw new Error("Cannot compute positions for average pool.");
this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.poolType = t10, this.computePositions = o, this.flattenPositions = n, this.includeBatchIndex = s, this.shaderKey = `pool3D_${t10}_${o}_${n}_${s}`;
}
getUserCode() {
let e;
this.poolType === "avg" ? e = "resultValue += value; count += 1.0;" : this.computePositions ? e = `let currMaxValue = mix(value, maxValue, maxValueFound);
if (value >= currMaxValue) {
maxValue = value;
maxValueFound = 1.0;
maxPosition = ${this.flattenPositions ? this.includeBatchIndex ? "(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch" : "((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch" : "wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC"};
}` : e = "resultValue = max(value, resultValue);";
let t10 = "resultValue";
return this.poolType === "avg" && (t10 = "resultValue / max(count, 1.0)"), `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords.x;
let ch = coords.u;
let xCorner = vec3<i32>(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;
let xDCorner = xCorner.x;
let xRCorner = xCorner.y;
let xCCorner = xCorner.z;
${this.computePositions ? `var maxValue = 0.0;
var maxValueFound = 0.0;
var maxPosition = 0;` : `var resultValue = ${this.poolType === "avg" ? "0.0" : "-1.0 / pow(10.0, -20.0)"};`}
var count = 0.0;
for (var wD = 0; wD < uniforms.filterDims.x; wD++) {
let xD = xDCorner + wD;
if (xD < 0 || xD >= uniforms.convDims.x) {
continue;
}
for (var wR = 0; wR < uniforms.filterDims.y; wR++) {
let xR = xRCorner + wR;
if (xR < 0 || xR >= uniforms.convDims.y) {
continue;
}
for (var wC = 0; wC < uniforms.filterDims.z; wC++) {
let xC = xCCorner + wC;
if (xC < 0 || xC >= uniforms.convDims.z) {
continue;
}
let value = getX(batch, xD, xR, xC, ch);
${e}
}
}
}
${this.computePositions ? "setOutputAtIndexI32(index, maxPosition);" : `setOutputAtIndex(index, ${t10});`}
}
}
`;
}
};
function Ov(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o;
return Yr(n, s, a, "max", t10);
}
var wz = { kernelName: Ln, backendName: "webgpu", kernelFunc: Ov };
function Mv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o;
return Yr(n, a, s, "mean", t10);
}
var Sz = { kernelName: Vn, backendName: "webgpu", kernelFunc: Mv };
function ox(r, e, t10, o) {
if (e.filterWidth === 1 && e.filterHeight === 1 && y.arraysEqual(e.inShape, e.outShape))
return At({ inputs: { x: r }, backend: o });
if (e.filterWidth === e.inWidth && e.filterHeight === e.inHeight && e.batchSize === 1 && e.padInfo.type === "VALID") {
let a = r.shape.length, i = pe({ inputs: { x: r }, backend: o, attrs: { shape: [r.shape[a - 3] * r.shape[a - 2], r.shape[a - 1]] } }), p;
t10 === "avg" ? p = Mv({ inputs: { x: i }, backend: o, attrs: { axis: 0, keepDims: false } }) : (y.assert(t10 === "max", () => `Invalid pool type ${t10}`), p = Ov({ inputs: { x: i }, backend: o, attrs: { reductionIndices: 0, keepDims: false } }));
let u = pe({ inputs: { x: p }, backend: o, attrs: { shape: e.outShape } });
return o.disposeData(i.dataId), o.disposeData(p.dataId), u;
}
let n, s = [{ type: "int32", data: [e.strideHeight, e.strideWidth] }];
return e.filterHeight === 1 && e.filterWidth === 1 ? n = new rx(e) : (t10 === "avg" ? n = new Da(e, "avg") : (y.assert(t10 === "max", () => `Invalid pool type ${t10}`), n = new Da(e, "max")), s.push({ type: "int32", data: [e.padInfo.top, e.padInfo.left] }, { type: "int32", data: [e.dilationHeight, e.dilationWidth] }, { type: "int32", data: [e.inHeight, e.inWidth] }, { type: "int32", data: [e.effectiveFilterHeight, e.effectiveFilterWidth] })), o.runWebGPUProgram(n, [r], r.dtype, s);
}
function hie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = C.computePool2DInfo(n.shape, s, a, u, i, p);
return ox(n, c, "avg", t10);
}
var Iz = { kernelName: Yo, backendName: "webgpu", kernelFunc: hie };
function gie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = C.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new wu(l, "avg"), d = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.front, l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.inDepth, l.inHeight, l.inWidth] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }];
return t10.runWebGPUProgram(m, [n], n.dtype, d);
}
var vz = { kernelName: qs, backendName: "webgpu", kernelFunc: gie };
var nx = class {
constructor(e) {
this.variableNames = ["dy"], this.uniforms = `strides : vec2<i32>, pads : vec2<i32>, dilations : vec2<i32>, filterDims : vec2<i32>,
outHeight : i32, outWidth : i32, avgMultiplier : f32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avgPool2DBackprop";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d = coords[3];
let dyRCCorner = vec2<i32>(coords.yz) - uniforms.pads;
let dyRCorner = dyRCCorner.x;
let 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.
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) {
let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);
if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
continue;
}
let idyR = i32(dyR);
for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) {
let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);
if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
continue;
}
let idyC = i32(dyC);
let dyValue = getDy(batch, idyR, idyC, d);
dotProd = dotProd + dyValue * uniforms.avgMultiplier;
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
var sx = class {
constructor(e) {
this.variableNames = ["dy"], this.uniforms = `strides : vec3<i32>, pads : vec3<i32>, filterDims : vec3<i32>,
outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avgPool3DBackprop";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords.x;
let ch = coords.u;
let dyCorner = vec3<i32>(coords.y, coords.z, coords.w) - uniforms.pads;
let dyDCorner = dyCorner.x;
let dyRCorner = dyCorner.y;
let 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.
var dotProd = 0.0;
for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {
let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);
if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {
continue;
}
let idyD = i32(dyD);
for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {
let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);
if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
continue;
}
let idyR = i32(dyR);
for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {
let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);
if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
continue;
}
let idyC = i32(dyC);
let dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * uniforms.avgMultiplier;
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
function xie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = C.computePool3DInfo(a.shape, i, p, 1, u, c), m = new sx(l), d = 1 / (l.filterDepth * l.filterHeight * l.filterWidth), f = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.effectiveFilterDepth - 1 - l.padInfo.front, l.effectiveFilterHeight - 1 - l.padInfo.top, l.effectiveFilterWidth - 1 - l.padInfo.left] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }, { type: "int32", data: [l.outDepth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "float32", data: [d] }];
return t10.runWebGPUProgram(m, [n], a.dtype, f);
}
var kz = { kernelName: Ni, backendName: "webgpu", kernelFunc: xie };
function yie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s;
cm([n, s], "avgPoolGrad");
let { filterSize: i, strides: p, pad: u } = o, c = C.computePool2DInfo(a.shape, i, p, 1, u), l = new nx(c), m = 1 / (c.filterHeight * c.filterWidth), d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.effectiveFilterHeight - 1 - c.padInfo.top, c.effectiveFilterWidth - 1 - c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }, { type: "int32", data: [c.outHeight] }, { type: "int32", data: [c.outWidth] }, { type: "float32", data: [m] }];
return t10.runWebGPUProgram(l, [n], a.dtype, d);
}
var Nz = { kernelName: Gp, backendName: "webgpu", kernelFunc: yie };
function bie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
return $p({ a: n, b: s, transposeA: a, transposeB: i, backend: t10 });
}
var Tz = { kernelName: Qo, backendName: "webgpu", kernelFunc: bie };
var ax = class {
constructor(e, t10) {
this.variableNames = ["source"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.rank = t10.length, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${Nt(e.length)}, `, this.shaderKey = "slice";
}
getUserCode() {
let e = Nt(this.rank), t10 = Cie(this.rank), o;
return this.start.length === 1 ? o = this.outputShape.map((s, a) => "sourceLoc = uniforms.start + coords;") : o = this.outputShape.map((s, a) => `sourceLoc.${Lv[a]} = uniforms.start.${Po(a)} + coords.${Lv[a]};`), `
${K("index")} {
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getCoordsFromIndex(index);
${o.join(`
`)}
setOutputAtIndex(index, getSource(${t10}));
}
}
`;
}
};
var Lv = ["x", "y", "z", "w", "u", "v"];
function Cie(r) {
if (r === 1)
return "sourceLoc";
if (r <= 6)
return Lv.slice(0, r).map((e) => `sourceLoc.${e}`).join(",");
throw Error(`Slicing for rank ${r} is not yet supported`);
}
function zs(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ct.parseSliceParams(n, s, a);
if (ct.assertParamsValid(n, i, p), t10.shouldExecuteOnCPU([n]) || n.dtype === "string") {
let l = t10.tensorMap.get(n.dataId), m = JB(l.values, i, p, n.shape, n.dtype);
return t10.makeTensorInfo(p, n.dtype, m);
}
if (y.sizeFromShape(p) === 0)
return t10.makeTensorInfo(p, n.dtype, []);
let u = new ax(i, p), c = [{ type: "int32", data: i }];
return t10.runWebGPUProgram(u, [n], n.dtype, c);
}
var _z = { kernelName: pa, backendName: "webgpu", kernelFunc: zs };
var wie = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o;
y.assert(n.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");
let i = s.reduce((b, w) => b * w), p = C.getReshaped(n.shape, s, i), u = C.getPermuted(p.length, s.length), c = C.getReshapedPermuted(n.shape, s, i), l = C.getSliceBeginCoords(a, s.length), m = C.getSliceSize(c, a, s.length), d = [], f = pe({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), h = rr({ inputs: { x: f }, backend: t10, attrs: { perm: u } }), g = pe({ inputs: { x: h }, backend: t10, attrs: { shape: c } }), x = zs({ inputs: { x: g }, backend: t10, attrs: { begin: l, size: m } });
return d.push(f), d.push(h), d.push(g), d.forEach((b) => t10.disposeData(b.dataId)), x;
};
var $z = { kernelName: js, backendName: "webgpu", kernelFunc: wie };
var Sie = `
fn bincount_write(index: i32, value: f32) {
${Bs("&result[index]", "value", "float32")}
}
`;
var Iie = `
fn bincount_write(index: i32, value: f32) {
atomicStore(&result[index], bitcast<i32>(value));
}
`;
var jc = class {
constructor(e, t10, o = false) {
this.outputShape = [], this.variableNames = ["x"], this.uniforms = "binCountSize : i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.hasWeights = true, this.binaryOutput = false, this.outputShape = e, this.rank = e.length, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.binaryOutput = o, o && (this.atomic = false), this.hasWeights = t10, this.hasWeights && this.variableNames.push("w"), this.shaderKey = `bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`;
}
getUserCode() {
return `
${this.binaryOutput ? Iie : Sie}
${K("index")} {
${this.rank === 1 ? `if (index < uniforms.xShape) {
let indexVal = i32(getX(index));
if (indexVal < uniforms.binCountSize) {
let value = ${this.binaryOutput ? 1 : this.hasWeights ? "getW(index)" : "1."};
bincount_write(indexVal, value);
}
}` : `let coord = getCoordsFromIndex(index);
if (coordsInBounds2D(coord, uniforms.xShape)) {
let indexVal = i32(getX(coord[0], coord[1]));
if (indexVal < uniforms.binCountSize) {
let value = ${this.binaryOutput ? 1 : this.hasWeights ? "getW(coord[0], coord[1])" : "1."};
bincount_write(coord.x * uniforms.binCountSize + indexVal, value);
}
}`}
}
`;
}
};
function vie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = y.sizeFromShape(n.shape), u = y.sizeFromShape(s.shape) > 0, c = [a], l = s.dtype, m = Vt({ backend: t10, attrs: { shape: c, value: 0, dtype: l } }), d = new jc([i], u), f = [{ type: "int32", data: [a] }], h = u ? [n, s] : [n];
return t10.runWebGPUProgram(d, h, l, f, m);
}
var Ez = { kernelName: Zo, backendName: "webgpu", kernelFunc: vie };
var ix = class {
constructor(e) {
this.outputShape = [], this.variableNames = ["s0", "s1"], this.uniforms = "s0Size : i32, s1Size : i32, ", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "broadcastArgs";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
var s0 = 1.0;
var s1 = 1.0;
let indexS0 = index - uniforms.size + uniforms.s0Size;
let indexS1 = index - uniforms.size + uniforms.s1Size;
if (indexS0 >= 0) {
s0 = getS0(indexS0);
}
if (indexS1 >= 0) {
s1 = getS1(indexS1);
}
if (s0 == 1.0) {
setOutputAtIndex(index, s1);
} else if (s1 == 1.0) {
setOutputAtIndex(index, s0);
} else if (s0 != s1) {
setOutputAtIndex(index, uniforms.NAN);
} else {
setOutputAtIndex(index, s0);
}
}
}
`;
}
};
function kie(r) {
let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e;
if (t10.shouldExecuteOnCPU([o, n])) {
let c = t10.tensorMap.get(o.dataId), l = t10.tensorMap.get(n.dataId), m = c.values, d = l.values, f = C.assertAndGetBroadcastShape(Array.from(m), Array.from(d));
return t10.makeTensorInfo([f.length], "int32", Int32Array.from(f));
}
let s = y.sizeFromShape(o.shape), a = y.sizeFromShape(n.shape), i = Math.max(s, a), p = new ix(i), u = [{ type: "int32", data: [s] }, { type: "int32", data: [a] }];
return t10.runWebGPUProgram(p, [o, n], "int32", u);
}
var Rz = { kernelName: Xs, backendName: "webgpu", kernelFunc: kie };
var Bv = et({ opType: fe.NOT_EQUAL, dtype: "bool", cpuKernelImpl: qB });
var Dz = { kernelName: qn, backendName: "webgpu", kernelFunc: Bv };
function Ci(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.tensorMap.get(o.dataId);
return At({ inputs: { x: n.complexTensorInfos.real }, backend: t10 });
}
var Az = { kernelName: zi, backendName: "webgpu", kernelFunc: Ci };
function Fz(r, e) {
let t10 = new Xr(r.shape, Q.TO_INT), o = e.runWebGPUProgram(t10, [r], "int32");
return { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
function zv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o;
if (s === "complex64") {
if (n.dtype === "complex64")
return At({ inputs: { x: n }, backend: t10 });
let a = Wr(n.shape), i = zv({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), p = fo({ inputs: { real: i, imag: a }, backend: t10 });
return a.dispose(), t10.disposeData(i.dataId), p;
}
if (n.dtype === "complex64") {
let a = Ci({ inputs: { input: n }, backend: t10 }), i = zv({ inputs: { x: a }, backend: t10, attrs: { dtype: s } });
return t10.disposeData(a.dataId), i;
}
if (!y.hasEncodingLoss(n.dtype, s)) {
let a = At({ inputs: { x: n }, backend: t10 });
return { dataId: a.dataId, shape: a.shape, dtype: s };
}
if (t10.shouldExecuteOnCPU([n])) {
let a = t10.tensorMap.get(n.dataId).values, [i, p, u] = TB(a, n.shape, n.dtype, s);
return t10.makeTensorInfo(i, p, u);
}
if (s === "int32")
return Fz(n, t10);
if (s === "bool") {
let a = t10.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = Bv({ inputs: { a: n, b: a }, backend: t10 });
return t10.disposeData(a.dataId), p;
}
throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`);
}
var Pz = { kernelName: ho, backendName: "webgpu", kernelFunc: zv };
var Nie = xe({ opType: Q.CEIL, cpuKernelImpl: _B });
var Oz = { kernelName: Jo, backendName: "webgpu", kernelFunc: Nie };
var ux = class {
constructor(e) {
this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workgroupSize = [64, 1, 1], this.outputComponent = 4, this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4";
}
getUserCode() {
return `
${K("index")} {
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
var clampedValue = clamp(
value, vec4<f32>(uniforms.minVal), vec4<f32>(uniforms.maxVal));
clampedValue = select(clampedValue, value, isnanVec4(value));
setOutputAtIndex(index, clampedValue);
}
}
`;
}
};
var px = class {
constructor(e) {
this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "clip";
}
getUserCode() {
return `
${K("index")} {
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
if (isnan(value)) {
setOutputAtIndex(index, value);
return;
}
setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`;
}
};
function Tie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i, p = [{ type: "float32", data: [s] }, { type: "float32", data: [a] }];
return y.sizeFromShape(n.shape) % 4 === 0 ? i = new ux(n.shape) : i = new px(n.shape), t10.runWebGPUProgram(i, [n], n.dtype, p);
}
var Mz = { kernelName: go, backendName: "webgpu", kernelFunc: Tie };
var cx = class {
constructor(e) {
this.outputShape = [], this.variableNames = ["real", "imag"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "complexAbs";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let re = abs(getRealByOutputIndex(index));
let im = abs(getImagByOutputIndex(index));
let mx = max(re, im);
// The length function in wgsl may be not underflow-safe on some GPUs.
// So the safe solution is to ensure underflow-safety in all cases.
setOutputAtIndex(index, select(mx * length(vec2<f32>(1, min(re, im)/mx)), 0.0, mx == 0.0));
}
}
`;
}
};
function Lz(r, e) {
return { dataId: e.dataId, dtype: e.dtype, shape: r.shape };
}
function _ie(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e, n = t10.tensorMap.get(o.dataId), s = new cx(o.shape), a = [Lz(o, n.complexTensorInfos.real), Lz(o, n.complexTensorInfos.imag)];
return t10.runWebGPUProgram(s, a, a[0].dtype);
}
var Bz = { kernelName: _i, backendName: "webgpu", kernelFunc: _ie };
var lx = class {
constructor(e) {
this.uniforms = "", this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = C.computeOutShape(e, 1), this.variableNames = e.map((t10, o) => `T${o}`), this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1;
for (let t10 = 0; t10 < this.offsetLength; t10++)
this.uniforms += `offset${t10} : i32,`;
this.shaderKey = "concat";
}
getUserCode() {
let e = [];
if (this.offsetLength > 0) {
e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");
for (let s = 1; s < this.offsetLength; s++)
e.push(`else if (yC < uniforms.offset${[s]}){ setOutputAtCoords(coords.x, coords.y, getT${s}(yR, yC - uniforms.offset${s - 1})); }`);
let o = this.offsetLength, n = this.offsetLength - 1;
e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${o}(yR, yC - uniforms.offset${n})); }`);
} else
e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");
return `
${K("index")} {
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
let yR = coords.x;
let yC = coords.y;
${e.join(`
`)}
}
}
}
`;
}
};
function Ep(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.tensorMap.get(o.dataId);
return At({ inputs: { x: n.complexTensorInfos.imag }, backend: t10 });
}
var zz = { kernelName: Mi, backendName: "webgpu", kernelFunc: Ep };
function Xc(r, e, t10) {
let o = r[0].dtype;
if (o === "complex64") {
let f = r.map((w) => Ci({ inputs: { input: w }, backend: t10 })), h = r.map((w) => Ep({ inputs: { input: w }, backend: t10 })), g = Xc(f, e, t10), x = Xc(h, e, t10), b = fo({ inputs: { real: g, imag: x }, backend: t10 });
return f.forEach((w) => t10.disposeData(w.dataId)), h.forEach((w) => t10.disposeData(w.dataId)), t10.disposeData(g.dataId), t10.disposeData(x.dataId), b;
}
let n = t10.shouldExecuteOnCPU(r);
if (o === "string" && (n = true), n) {
let f = r.map((k) => {
let E = [-1, y.sizeFromShape(k.shape.slice(e))];
return pe({ inputs: { x: k }, backend: t10, attrs: { shape: E } });
}), h = f.map((k) => ({ vals: t10.readSync(k.dataId), shape: k.shape })), g = C.computeOutShape(f.map((k) => k.shape), 1), x = f[0].shape[0] === 1, b = $B(h, g, o, x), w = C.computeOutShape(r.map((k) => k.shape), e), S = t10.makeTensorInfo(w, o, b);
return f.forEach((k) => t10.disposeData(k.dataId)), S;
}
let s = t10.device.limits.maxStorageBuffersPerShaderStage - 1;
if (r.length > s) {
let f = [];
for (let g = 0; g < r.length; g += s) {
let x = r.slice(g, g + s);
f.push(Xc(x, e, t10));
}
let h = Xc(f, e, t10);
for (let g of f)
t10.disposeData(g.dataId);
return h;
}
let { tensors2D: a, outShape: i } = $ie(r, e, t10), p = a.map((f) => f.shape), u = new lx(p), c = [], l = new Array(p.length - 1);
if (l.length > 0) {
l[0] = p[0][1], c.push({ type: "int32", data: [l[0]] });
for (let f = 1; f < l.length; f++)
l[f] = l[f - 1] + p[f][1], c.push({ type: "int32", data: [l[f]] });
}
let m = t10.runWebGPUProgram(u, a, a[0].dtype, c);
a.forEach((f) => t10.disposeData(f.dataId));
let d = pe({ inputs: { x: m }, backend: t10, attrs: { shape: i } });
return t10.disposeData(m.dataId), d;
}
function $ie(r, e, t10) {
let o = C.computeOutShape(r.map((s) => s.shape), e);
return { tensors2D: r.map((s) => pe({ inputs: { x: s }, backend: t10, attrs: { shape: [y.sizeFromShape(s.shape.slice(0, e)), y.sizeFromShape(s.shape.slice(e))] } })), outShape: o };
}
function Vv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape);
C.assertParamsConsistent(a, s);
let i = C.computeOutShape(e.map((u) => u.shape), s);
if (y.sizeFromShape(i) === 0)
return t10.makeTensorInfo(i, e[0].dtype, []);
let p = e.filter((u) => y.sizeFromShape(u.shape) > 0);
return p.length === 1 ? At({ inputs: { x: p[0] }, backend: t10 }) : Xc(p, s, t10);
}
var Vz = { kernelName: Ys, backendName: "webgpu", kernelFunc: Vv };
function Eie(r, e, t10, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) {
let c = (D) => {
switch (D) {
case 1:
return "resData = x[xIndex];";
case 3:
return "resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";
case 4:
return "resData = x[xIndex / 4];";
default:
throw new Error(`innerElementSize ${D} is not supported.`);
}
}, l = (D) => {
switch (D) {
case 1:
return "return W[row * uniforms.wShape[3] + colIn];";
case 4:
return "return W[row * uniforms.wShape[3] / 4 + colIn];";
default:
throw new Error(`innerElementSize ${D} is not supported.`);
}
}, m = r ? `
let coord = vec4<i32>(batch, xRow, xCol, xCh);
` : `
let coord = vec4<i32>(batch, xCh, xRow, xCol);
`, d = r ? `
let coords = vec4<i32>(
batch,
row / outWidth,
row % outWidth,
col);
` : `
let coords = vec4<i32>(
batch,
row,
col / outWidth,
col % outWidth);
`, f = r ? "uniforms.xShape[1]" : "uniforms.xShape[2]", h = r ? "uniforms.xShape[2]" : "uniforms.xShape[3]", g = r ? "row" : "col", x = r ? "col" : "row", b = `
let inChannels = uniforms.wShape[2];
let outWidth = ${r ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
let outRow = ${g} / outWidth;
let outCol = ${g} % outWidth;
let WRow = ${x} / (uniforms.filterDims[1] * inChannels);
let WCol = ${x} / inChannels % uniforms.filterDims[1];
let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0];
let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1];
let xCh = ${x} % inChannels;
var resData = ${Ae(i)}(0.0);
// The bounds checking is always needed since we use it to pad zero for
// the 'same' padding type.
if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {
${m}
let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
${c(i)}
}
return resData;`, w = r ? e && o ? `
let col = colIn * ${i};
${b}` : `
let col = colIn * ${i};
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${b}
}
return ${Ae(i)}(0.0);` : o && t10 ? `
let col = colIn * ${i};
${b}` : `
let col = colIn * ${i};
if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
${b}
}
return ${Ae(i)}(0.0);`, S = `${l(p)}`, k = Ae(u), _ = r ? Ae(i) : Ae(p), E = r ? Ae(p) : Ae(i);
return `
${dr(s, a, u === 4, 4)}
fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {
${r ? w : S}
}
fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} {
${r ? S : w}
}
fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {
let col = colIn * ${u};
if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
{
var value = valueIn;
let outWidth = ${r ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
${d}
${jr(n, s)}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
}
}`;
}
var mx = class {
constructor(e, t10, o, n, s = false, a = null, i = false, p = false) {
this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, strides : vec2<i32>, dilations : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.isVec4 = ((e.inChannels % 4 === 0 || e.inChannels % 3 === 0) && this.isChannelsLast || e.outWidth % 4 === 0 && !this.isChannelsLast) && e.outChannels % 4 === 0, this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] }, this.workgroupSize = im(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = um(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 ? (this.outputComponent = 4, this.isChannelsLast && e.inChannels % 4 !== 0 ? (this.innerElementSize = 3, this.variableComponents = [1, 4]) : (this.innerElementSize = 4, this.variableComponents = [4, 4]), s && (this.variableNames.push("bias"), this.variableComponents.push(4)), i && (this.variableNames.push("preluActivationWeights"), this.variableComponents.push(4))) : (this.innerElementSize = this.elementsPerThread[0], s && this.variableNames.push("bias"), i && this.variableNames.push("preluActivationWeights")), this.sequentialAccessByThreads = p, this.addBias = s, this.activation = a, this.hasPreluActivationWeights = i, this.tileAOuter = this.workgroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workgroupSize[0] * this.elementsPerThread[0], this.tileInner = Math.max(this.workgroupSize[0] * this.innerElementSize, this.workgroupSize[1]), this.fitAOuter = t10 % this.tileAOuter === 0, this.fitBOuter = o % this.tileBOuter === 0, this.fitInner = n % this.tileInner === 0, this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`;
}
getUserCode() {
let e = this.isVec4 ? Tp(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner) : _p(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner, false, null, this.sequentialAccessByThreads), t10 = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1];
return `
${Eie(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, t10[0], t10[1], t10[2])}
${e}
`;
}
};
var dx = class {
constructor(e, t10 = false, o = null, n = false) {
this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec2<i32>, pads: vec2<i32>, strides: vec2<i32>, dilations: vec2<i32>,", this.workgroupSize = [4, 4, 8], this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [2], y: [1], z: [0, 3] } : { x: [3], y: [2], z: [0, 1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t10, this.activation = o, this.hasPreluActivationWeights = n, t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.shaderKey = `conv2dnaive_${this.activation}_${this.isChannelsLast}`;
}
getUserCode() {
return `
${dr(this.activation, this.hasPreluActivationWeights, false, 4)}
fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{
let coords = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coords, uniforms.xShape)) {
return getX(batch, row, col, chan);
} else {
return 0.0;
}
}
fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
let coords = vec4<i32>(row, col, xChannel, outChannel);
if(coordsInBounds4D(coords, uniforms.wShape)) {
return getW(row, col, xChannel, outChannel);
} else {
return 0.0;
}
}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {
let coords = ${this.isChannelsLast ? "vec4<i32>(batch, row, col, chan);" : "vec4<i32>(batch, chan, row, col);"}
if (coordsInBounds4D(coords, uniforms.outShape)) {
var value = valueIn;
${jr(this.addBias, this.activation)}
setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);
}
}
${K("index")} {
let coords = getOutputCoords();
let batch = coords[0];
let outChannel = ${this.isChannelsLast ? "coords[3];" : "coords[1];"}
let outRow = ${this.isChannelsLast ? "coords[1];" : "coords[2];"}
let outCol = ${this.isChannelsLast ? "coords[2];" : "coords[3];"}
var acc : f32 = 0.0;
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0];
let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1];
for (var xChannel = 0; xChannel < ${this.isChannelsLast ? "uniforms.xShape[3];" : "uniforms.xShape[1];"} xChannel = xChannel + 1) {
${this.isChannelsLast ? "let v = readInp(batch, xRow, xCol, xChannel);" : "let v = readInp(batch, xChannel, xRow, xCol);"}
let f = readFilt(row, col, xChannel, outChannel);
acc = acc + v * f;
}
}
}
writeResult(batch, outRow, outCol, outChannel, acc);
}
`;
}
};
var fx = class {
constructor(e, t10) {
this.variableNames = ["x"], this.uniforms = `pads : vec2<i32>, strides : vec2<i32>, dilations : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
inChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = t10, this.shaderKey = `im2col_${this.isChannelsLast}`;
}
getUserCode() {
let e = this.isChannelsLast ? 1 : 2, t10 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? "coords[1]" : "coords[2]", n = this.isChannelsLast ? "coords[2]" : "coords[1]", s = this.isChannelsLast ? "getX(batch, xRow, xCol, ch)" : "getX(batch, ch, xRow, xCol)";
return `
${K("index")} {
let coords = getCoordsFromIndex(index);
if(index < uniforms.size) {
let batch = coords[0];
let row = ${o};
let col = ${n};
let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0];
let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow);
var value = 0.0;
if(xRow < uniforms.xShape[${e}] && xRow >= 0) {
let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] -
uniforms.pads[1];
let xCol = offsetX + uniforms.dilations[1] * ((col %
uniforms.itemsPerBlockRow) / uniforms.inChannels);
let ch = col % uniforms.inChannels;
if(xCol < uniforms.xShape[${t10}] && xCol >= 0) {
value = ${s};
}
}
setOutputAtIndex(index, value);
}
}
`;
}
};
function hx(r, e) {
let t10 = r.length;
return t10 >= 3 ? e ? [...r.slice(0, -3), r[t10 - 3] * r[t10 - 2], r[t10 - 1]] : [...r.slice(0, -3), r[t10 - 3], r[t10 - 2] * r[t10 - 1]] : !e && t10 === 1 && r[0] > 1 ? [r[0], 1] : null;
}
function Rie({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
let p = t10.dataFormat === "channelsLast", u = !p, c = false, l = p && t10.filterHeight === t10.inHeight && t10.filterWidth === t10.inWidth && t10.padInfo.type === "VALID", m = [], d, f;
if (l) {
let x = t10.inHeight * t10.inWidth * t10.inChannels;
d = pe({ inputs: { x: r }, backend: o, attrs: { shape: [1, t10.batchSize, x] } }), f = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, x, t10.outChannels] } });
} else
d = pe({ inputs: { x: r }, backend: o, attrs: { shape: p ? [t10.batchSize, t10.inHeight * t10.inWidth, t10.inChannels] : [t10.batchSize, t10.inChannels, t10.inHeight * t10.inWidth] } }), f = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } });
if (m.push(d), m.push(f), s != null) {
let x = hx(s.shape, p);
x != null && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: x } }), m.push(s));
}
if (n != null) {
let x = hx(n.shape, p);
x != null && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: x } }), m.push(n));
}
let h = $p({ a: p ? d : f, b: p ? f : d, transposeA: u, transposeB: c, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), g = pe({ inputs: { x: h }, backend: o, attrs: { shape: t10.outShape } });
m.push(h);
for (let x of m)
o.disposeData(x.dataId);
return g;
}
function Die({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
let { filterWidth: p, filterHeight: u, inChannels: c, strideWidth: l, strideHeight: m, padInfo: d, outWidth: f, outHeight: h, dilationWidth: g, dilationHeight: x, dataFormat: b } = t10, w = b === "channelsLast", S = p * u * c, k = h * f, _ = w ? [t10.batchSize, k, S] : [t10.batchSize, S, k], E = new fx(_, w), R = [{ type: "int32", data: [d.top, d.left] }, { type: "int32", data: [m, l] }, { type: "int32", data: [x, g] }, { type: "int32", data: [f] }, { type: "int32", data: [c * p] }, { type: "int32", data: [c] }], D = o.runWebGPUProgram(E, [r], r.dtype, R), F = [];
F.push(D);
let O = pe({ inputs: { x: e }, backend: o, attrs: { shape: [1, S, -1] } });
if (F.push(O), s != null) {
let U = hx(s.shape, w);
U != null && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: U } }), F.push(s));
}
if (n != null) {
let U = hx(n.shape, w);
U != null && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: U } }), F.push(n));
}
let B = $p({ a: w ? D : O, b: w ? O : D, transposeA: !w, transposeB: false, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), z = pe({ inputs: { x: B }, backend: o, attrs: { shape: t10.outShape } });
F.push(B);
for (let U of F)
o.disposeData(U.dataId);
return z;
}
function gx({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
let p = n != null, u = s != null, c = t10.dataFormat === "channelsLast", l = c && t10.filterHeight === t10.inHeight && t10.filterWidth === t10.inWidth && t10.padInfo.type === "VALID", m = P().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");
if (!m && (l || t10.filterHeight === 1 && t10.filterWidth === 1 && t10.dilationHeight === 1 && t10.dilationWidth === 1 && t10.strideHeight === 1 && t10.strideWidth === 1 && (t10.padInfo.type === "SAME" || t10.padInfo.type === "VALID")))
return Rie({ x: r, filter: e, convInfo: t10, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a });
let d = P().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), f = d > 0 ? d : o.thresholdToIncreaseWorkgroups, h = t10.batchSize * Math.ceil(t10.outHeight * t10.outWidth / 32) * Math.ceil(t10.outChannels / 32);
if (P().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") || h <= f)
return Die({ x: r, filter: e, convInfo: t10, backend: o, bias: n, preluActivationWeights: s, leakyreluAlpha: a, activation: i });
let g, x = [t10.padInfo.top, t10.padInfo.left], b = [{ type: "int32", data: [t10.filterHeight, t10.filterWidth] }, { type: "int32", data: [...x] }, { type: "int32", data: [t10.strideHeight, t10.strideWidth] }, { type: "int32", data: [t10.dilationHeight, t10.dilationWidth] }];
if (m)
g = new dx(t10, p, i, u);
else {
let _ = c ? t10.outHeight * t10.outWidth : t10.outChannels, E = c ? t10.outChannels : t10.outHeight * t10.outWidth, R = t10.filterHeight * t10.filterWidth * t10.inChannels;
b.push({ type: "int32", data: [_] }, { type: "int32", data: [E] }, { type: "int32", data: [R] });
let D = o.adapterInfo.isIntel();
g = new mx(t10, _, E, R, p, i, u, D);
}
let w = [], S = [r, e];
p && (!c && n.shape.length === 1 && (n = pe({ inputs: { x: n }, backend: o, attrs: { shape: [n.shape[0], 1, 1] } }), w.push(n)), S.push(n)), u && (!c && s.shape.length === 1 && (s = pe({ inputs: { x: s }, backend: o, attrs: { shape: [s.shape[0], 1, 1] } }), w.push(s)), S.push(s)), i === "leakyrelu" && (b.push({ type: "float32", data: [a] }), g.uniforms += " alpha : f32,");
let k = o.runWebGPUProgram(g, S, r.dtype, b);
for (let _ of w)
o.disposeData(_.dataId);
return k;
}
function Aie(r) {
let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = t10, l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l);
return gx({ x: n, filter: s, convInfo: m, backend: o });
}
var Wz = { kernelName: en, backendName: "webgpu", kernelFunc: Aie };
var xx = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, strides : vec2<i32>, outBackprop : vec4<i32>,", this.workgroupSize = [64, 1, 1], this.size = false, this.isVec4 = false, this.workPerThread = 1, this.outputShape = e.inShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.isVec4 = this.isChannelsLast && e.outChannels % 4 === 0 && e.inChannels % 4 === 0, this.isVec4 ? (this.workPerThread = 2, this.outputComponent = 4, this.workgroupSize = [4, 4, 4], this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [4, this.workPerThread, 1])) : (this.size = true, this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize)), this.shaderKey = `conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`;
}
getUserCode() {
let e = this.isChannelsLast ? 1 : 2, t10 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? 3 : 1, n = `
${K()} {
let batch = i32(globalId.z) / uniforms.outShape[1];
let r = i32(globalId.z) % uniforms.outShape[1];
let c = i32(globalId.y) * ${this.workPerThread};
let d1 = i32(globalId.x) * 4;
let dyCorner = vec2<i32>(r, c) - uniforms.pads;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
var dotProd: array<vec4<f32>, ${this.workPerThread}>;
for (var i = 0; i < ${this.workPerThread}; i++) {
dotProd[i] = vec4<f32>(0.0);
}
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x);
let wRPerm = uniforms.filterDims.x - 1 - wR;
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) ||
fract(dyR) > 0.0) {
continue;
}
let idyR = i32(dyR);
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y);
let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y);
let wCPerm = uniforms.filterDims.y - 1 - wC;
var bDyCVal = true;
var bDyCVal2 = true;
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
fract(dyC) > 0.0) {
bDyCVal = false;
}
if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) ||
fract(dyC2) > 0.0) {
bDyCVal2 = false;
}
let idyC = i32(dyC);
let idyC2 = i32(dyC2);
if (bDyCVal && bDyCVal2) {
let d2Length = uniforms.outBackprop[3];
for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {
let wValue0 = getW(wRPerm, wCPerm, d1, d2);
let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);
let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);
let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);
var xValue = getDy(batch, idyR, idyC, d2);
let tmpval = vec4<f32>(dot(xValue, wValue0),
dot(xValue, wValue1),
dot(xValue, wValue2),
dot(xValue, wValue3));
dotProd[0] = dotProd[0] + tmpval;
xValue = getDy(batch, idyR, idyC2, d2);
dotProd[1] = dotProd[1] + vec4<f32>(dot(xValue, wValue0),
dot(xValue, wValue1),
dot(xValue, wValue2),
dot(xValue, wValue3));
}
} else if (bDyCVal) {
let d2Length = uniforms.outBackprop[3];
for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {
let wValue0 = getW(wRPerm, wCPerm, d1, d2);
let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);
let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);
let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);
var xValue = getDy(batch, idyR, idyC, d2);
let tmpval = vec4<f32>(dot(xValue, wValue0),
dot(xValue, wValue1),
dot(xValue, wValue2),
dot(xValue, wValue3));
dotProd[0] = dotProd[0] + tmpval;
}
} else if (bDyCVal2) {
let d2Length = uniforms.outBackprop[3];
for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {
let wValue0 = getW(wRPerm, wCPerm, d1, d2);
let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);
let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);
let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);
var xValue = getDy(batch, idyR, idyC2, d2);
let tmpval = vec4<f32>(dot(xValue, wValue0),
dot(xValue, wValue1),
dot(xValue, wValue2),
dot(xValue, wValue3));
dotProd[1] = dotProd[1] + tmpval;
}
}
}
}
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let coords = vec4<i32>(batch, r, c + i, d1);
if (coordsInBounds4D(coords, uniforms.outShape)) {
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`;
return this.isVec4 ? `
${n}
` : `
${K("index")} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d1 = coords[${o}];
let dyCorner = vec2<i32>(coords[${e}], coords[${t10}]) - uniforms.pads;
let dyRCorner = dyCorner.x;
let 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.
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x);
let wRPerm = uniforms.filterDims.x - 1 - wR;
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
wRPerm < 0) {
continue;
}
let idyR = i32(dyR);
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y);
let wCPerm = uniforms.filterDims.y - 1 - wC;
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
fract(dyC) > 0.0 || wCPerm < 0) {
continue;
}
let idyC = i32(dyC);
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
let xValue = ${this.isChannelsLast ? "getDy(batch, idyR, idyC, d2)" : "getDy(batch, d2, idyR, idyC)"};
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
var yx = class {
constructor(e) {
this.variableNames = ["x", "dy"], this.uniforms = "pads : vec2<i32>, strides : vec2<i32>, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerFilter_${this.isChannelsLast}`;
}
getUserCode() {
return `
${K("index")} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let wR = coords[0];
let wC = coords[1];
let d1 = coords[2];
let d2 = coords[3];
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
for (var b = 0; b < uniforms.batchSize; b = b + 1) {
for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {
let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];
if (xR < 0 || xR >= uniforms.inHeight) {
continue;
}
for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {
let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];
if (xC < 0 || xC >= uniforms.inWidth) {
continue;
}
if (${this.isChannelsLast}) {
let dyValue = getDy(b, yR, yC, d2);
let xValue = getX(b, xR, xC, d1);
dotProd = dotProd + xValue * dyValue;
} else {
let dyValue = getDy(b, d2, yR, yC);
let xValue = getX(b, d1, xR, xC);
dotProd = dotProd + xValue * dyValue;
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
var bx = class {
constructor(e) {
this.variableNames = ["x", "dy"], this.uniforms = `pads : vec3<i32>, strides : vec3<i32>, batchSize : i32, outDepth : i32,
outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3DDerFilter";
}
getUserCode() {
return `
${K("index")} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let wF = coords.x;
let wR = coords.y;
let wC = coords.z;
let d1 = coords.w;
let d2 = coords.u;
var dotProd = 0.0;
for (var b = 0; b < uniforms.batchSize; b++) {
for (var yF = 0; yF < uniforms.outDepth; yF++) {
let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0];
if (xF < 0 || xF >= uniforms.inDepth) {
continue;
}
for (var yR = 0; yR < uniforms.outHeight; yR++) {
let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1];
if (xR < 0 || xR >= uniforms.inHeight) {
continue;
}
for (var yC = 0; yC < uniforms.outWidth; yC++) {
let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2];
if (xC < 0 || xC >= uniforms.inWidth) {
continue;
}
let dyValue = getDy(b, yF, yR, yC, d2);
let xValue = getX(b, xF, xR, xC, d1);
dotProd += xValue * dyValue;
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
var Cx = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.uniforms = `filterDims : vec3<i32>, pads : vec3<i32>, strides : vec3<i32>,
outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3DDerInput";
}
getUserCode() {
return `
${K("index")} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords.x;
let d1 = coords.u;
let dyCorner = vec3<i32>(coords.y, coords.z, coords.w) - uniforms.pads;
let dyFCorner = dyCorner.x;
let dyRCorner = dyCorner.y;
let dyCCorner = dyCorner.z;
var dotProd = 0.0;
for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {
let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]);
if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) {
continue;
}
let idyF = i32(dyF);
let wFPerm = uniforms.filterDims[0] - 1 - wF;
for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {
let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);
if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
continue;
}
let idyR = i32(dyR);
let wRPerm = uniforms.filterDims[1] - 1 - wR;
for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {
let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);
if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
continue;
}
let idyC = i32(dyC);
let wCPerm = uniforms.filterDims[2] - 1 - wC;
for (var d2 = 0; d2 < uniforms.outChannels; d2++) {
let xValue = getDy(batch, idyF, idyR, idyC, d2);
let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
function Fie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = C.convertConv2DDataFormat(p), m = C.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new yx(m), f = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }, { type: "int32", data: [m.inHeight] }, { type: "int32", data: [m.inWidth] }];
return t10.runWebGPUProgram(d, [n, s], n.dtype, f);
}
var Uz = { kernelName: $i, backendName: "webgpu", kernelFunc: Fie };
function Pie(r = 4) {
let e = (s) => {
switch (s) {
case 1:
return "return W[getIndexFromCoords4D(coord, uniforms.wShape)];";
case 4:
return `
let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];
let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];
let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];
let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];
return vec4<f32>(v0, v1, v2, v3);
`;
default:
throw new Error(`innerElementSize ${s} is not supported.`);
}
}, o = `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${`
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]);
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]);
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
return ${Ae(r)}(0.0);
}
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
return ${Ae(r)}(0.0);
}
let coord = vec4<i32>(
batch,
i32(xR),
i32(xC),
col % uniforms.outBackprop[3]);
return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}
}
return ${Ae(r)}(0.0);`;
return `
fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Ae(r)} {
let col = colIn * ${r};
${o}
}
fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Ae(r)} {
let col = colIn * ${r};
let coordX = uniforms.filterDims.x - 1 -
row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let coordY = uniforms.filterDims.y - 1 -
(row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
coordX >= 0 && coordY >= 0) {
let rowInner = row % uniforms.outBackprop[3];
let coord = vec4<i32>(coordX, coordY, col, rowInner);
${e(r)}
}
return ${Ae(r)}(0.0);
}
fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${Ae(r)}) {
let col = colIn * ${r};
if (row < uniforms.dimAOuter && (col + ${r - 1}) < uniforms.dimBOuter) {
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;
}
}`;
}
var wx = class {
constructor(e) {
this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, strides : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.isVec4 = e.inChannels % 4 === 0 && e.outChannels % 4 === 0, this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workgroupSize = im(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = um(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 && (this.outputComponent = 4, this.variableComponents = [4, 1]), this.shaderKey = `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`;
}
getUserCode() {
let e = this.isVec4 ? Tp(this.elementsPerThread, this.workgroupSize) : _p(this.elementsPerThread, this.workgroupSize);
return `
${Pie(this.isVec4 ? 4 : 1)}
${e}
`;
}
};
function Oie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = C.convertConv2DDataFormat(u), m = C.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = [{ type: "int32", data: [m.filterHeight, m.filterWidth] }, { type: "int32", data: [m.filterHeight - 1 - m.padInfo.top, m.filterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize, m.outHeight, m.outWidth, m.outChannels] }], f;
if (P().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE") || m.dataFormat !== "channelsLast")
f = new xx(m);
else {
f = new wx(m);
let h = m.inHeight * m.inWidth, g = m.inChannels, x = m.filterHeight * m.filterWidth * m.outChannels;
d.push({ type: "uint32", data: [h] }, { type: "uint32", data: [g] }, { type: "uint32", data: [x] });
}
return t10.runWebGPUProgram(f, [n, s], "float32", d);
}
var Gz = { kernelName: tn, backendName: "webgpu", kernelFunc: Oie };
var Sx = class {
constructor(e) {
this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec3<i32>, pads: vec3<i32>, strides: vec3<i32>, dilations: vec3<i32>,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "conv3dnaive";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
let batch = coords.x;
let d2 = coords.u;
let xFRCCorner = vec3<i32>(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;
let xFCorner = xFRCCorner.x;
let xRCorner = xFRCCorner.y;
let xCCorner = xFRCCorner.z;
let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4;
let inputDepthVec4Remainder = uniforms.xShape.u % 4;
var dotProd = 0.0;
for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {
let xF = xFCorner + wF * uniforms.dilations[0];
if (xF < 0 || xF >= uniforms.xShape.y) {
continue;
}
for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {
let xR = xRCorner + wR * uniforms.dilations[1];
if (xR < 0 || xR >= uniforms.xShape.z) {
continue;
}
for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {
let xC = xCCorner + wC * uniforms.dilations[2];
if (xC < 0 || xC >= uniforms.xShape.w) {
continue;
}
for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) {
let xValues = vec4<f32>(
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)
);
let wValues = vec4<f32>(
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 (inputDepthVec4Remainder == 1) {
dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) *
getW(wF, wR, wC, inputDepthNearestVec4, d2);
} else if (inputDepthVec4Remainder == 2) {
let xValues = vec2<f32>(
getX(batch, xF, xR, xC, inputDepthNearestVec4),
getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)
);
let wValues = vec2<f32>(
getW(wF, wR, wC, inputDepthNearestVec4, d2),
getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (inputDepthVec4Remainder == 3) {
let xValues = vec3<f32>(
getX(batch, xF, xR, xC, inputDepthNearestVec4),
getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)
);
let wValues = vec3<f32>(
getW(wF, wR, wC, inputDepthNearestVec4, d2),
getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2),
getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}`;
}
};
function Mie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = C.computeConv3DInfo(n.shape, s.shape, a, p, i), c = [u.padInfo.front, u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterDepth, u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationDepth, u.dilationHeight, u.dilationWidth] }], m = new Sx(u), d = dt(n.dtype, s.dtype);
return t10.runWebGPUProgram(m, [n, s], d, l);
}
var Hz = { kernelName: rn, backendName: "webgpu", kernelFunc: Mie };
function Lie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = C.computeConv3DInfo(n.shape, p, a, 1, i), c = new bx(u), l = [{ type: "int32", data: [u.padInfo.front, u.padInfo.top, u.padInfo.left] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.batchSize] }, { type: "int32", data: [u.outDepth] }, { type: "int32", data: [u.outHeight] }, { type: "int32", data: [u.outWidth] }, { type: "int32", data: [u.inDepth] }, { type: "int32", data: [u.inHeight] }, { type: "int32", data: [u.inWidth] }];
return t10.runWebGPUProgram(c, [n, s], s.dtype, l);
}
var Kz = { kernelName: za, backendName: "webgpu", kernelFunc: Lie };
function Bie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, pad: i, inputShape: p } = o, u = C.computeConv3DInfo(p, s.shape, a, 1, i), c = new Cx(u), l = [{ type: "int32", data: [u.filterDepth, u.filterHeight, u.filterWidth] }, { type: "int32", data: [u.filterDepth - 1 - u.padInfo.front, u.filterHeight - 1 - u.padInfo.top, u.filterWidth - 1 - u.padInfo.left] }, { type: "int32", data: [u.strideDepth, u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.outDepth] }, { type: "int32", data: [u.outHeight] }, { type: "int32", data: [u.outWidth] }, { type: "int32", data: [u.outChannels] }];
return t10.runWebGPUProgram(c, [n, s], n.dtype, l);
}
var qz = { kernelName: on, backendName: "webgpu", kernelFunc: Bie };
var zie = xe({ opType: Q.COS });
var jz = { kernelName: nn, backendName: "webgpu", kernelFunc: zie };
var Vie = xe({ opType: Q.COSH });
var Xz = { kernelName: sn, backendName: "webgpu", kernelFunc: Vie };
var Ix = class {
constructor(e, t10, o, n) {
this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true;
let [s] = t10;
this.outputShape = [s, o[0], o[1], e], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.methodId = n === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;
}
getUserCode() {
let [e, t10] = ["f32(uniforms.imageShape[1] - 1)", "f32(uniforms.imageShape[2] - 1)"], [o, n, s] = this.cropHeightBiggerThan1 ? [`(${e} / f32(uniforms.outShape[1] - 1))`, "(y2-y1) * height_ratio", `y1*${e} + f32(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${e}`], [a, i, p] = this.cropWidthBiggerThan1 ? [`(${t10} / f32(uniforms.outShape[2] - 1))`, "(x2-x1) * width_ratio", `x1*${t10} + f32(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${t10}`];
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let height_ratio = f32(${o});
let width_ratio = f32(${a});
let b = coords[0];
let y = coords[1];
let x = coords[2];
let d = coords[3];
// get box vals
let y1 = getBoxes(b, 0);
let x1 = getBoxes(b, 1);
let y2 = getBoxes(b, 2);
let x2 = getBoxes(b, 3);
// get image in batch index
let bInd = i32(round(getBoxInd(b)));
if(bInd < 0 || bInd >= uniforms.outShape[0]) {
return;
}
let height_scale = ${n};
let width_scale = ${i};
let in_y = ${s};
if( in_y < 0.0 || in_y > ${e} ) {
setOutputAtIndex(index, uniforms.extrapolationValue);
return;
}
let in_x = ${p};
if( in_x < 0.0 || in_x > ${t10} ) {
setOutputAtIndex(index, uniforms.extrapolationValue);
return;
}
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
if(${this.methodId} == 1) {
// Compute the four integer indices.
let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
let top = topLeft + (topRight - topLeft) * fracCR.x;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
let newValue = top + (bottom - top) * fracCR.y;
setOutputAtIndex(index, newValue);
} else {
// Compute the coordinators of nearest neighbor point.
let sourceNearestCR = vec2<i32>(floor(
sourceFracIndexCR + vec2<f32>(0.5,0.5)));
let newValue = getImage(
bInd, sourceNearestCR.y, sourceNearestCR.x, d);
setOutputAtIndex(index, newValue);
}
}
}
`;
}
};
var Wie = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new Ix(n.shape[3], s.shape, i, p), l = [{ type: "float32", data: [u] }];
return t10.runWebGPUProgram(c, [n, s, a], "float32", l);
};
var Yz = { kernelName: pn, backendName: "webgpu", kernelFunc: Wie };
var Rp;
(function(r) {
r.Prod = "*", r.Sum = "+";
})(Rp || (Rp = {}));
var dm = class {
constructor(e, t10, o, n) {
this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true, this.workgroupSize = [128, 1, 1], this.outputShape = t10, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.exclusive = o, this.reverse = n, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;
}
getUserCode() {
let e = this.outputShape.length, t10 = this.op === Rp.Prod ? "1.0" : "0.0", o = this.exclusive ? t10 : `getX(${Qz(e, "coords", this.op)})`, n = this.outputShape[this.outputShape.length - 1], s = "", a = "";
return this.exclusive ? (s = this.reverse ? `end != ${n - 1}` : "end != 0", a = this.reverse ? "end + 1" : "end - 1") : (s = this.reverse ? `end + pow2 < ${n}` : "end >= pow2", a = this.reverse ? "end + pow2" : "end - pow2"), `
${K("index")} {
if (index < uniforms.size) {
var coords = getCoordsFromIndex(index);
let end = ${Zz(e, "coords", this.op)};
var val = ${o};
let pow2 = i32(pow(2.0, uniforms.index));
if (${s}) {
let idx = ${a};
${Zz(e, "coords", this.op)} = idx;
val ${this.op}= getX(${Qz(e, "coords", this.op)});
}
setOutputAtIndex(index, val);
}
}
`;
}
};
function Qz(r, e, t10) {
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 ${t10} for rank ${r} is not yet supported`);
}
function Zz(r, e, t10) {
if (r === 1)
return `${e}`;
if (r === 2)
return `${e}.y`;
if (r === 3)
return `${e}.z`;
if (r === 4)
return `${e}.w`;
throw Error(`Cumulative ${t10} for rank ${r} is not yet supported`);
}
function vx(r, e, t10, o, n, s) {
let a = e.shape.length, i = C.getAxesPermutation([o], a), p = e;
i != null && (p = rr({ inputs: { x: e }, backend: t10, attrs: { perm: i } }));
let u = C.getInnerMostAxes(1, a)[0];
if (u !== a - 1)
throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`);
let c = p.shape[u], l = At({ inputs: { x: p }, backend: t10 });
for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) {
let d = new dm(r, p.shape, false, s), f = l, h = [{ type: "float32", data: [m] }];
l = t10.runWebGPUProgram(d, [l], l.dtype, h), t10.disposeData(f.dataId);
}
if (n) {
let m = new dm(r, p.shape, n, s), d = l, f = [{ type: "float32", data: [0] }];
l = t10.runWebGPUProgram(m, [l], l.dtype, f), t10.disposeData(d.dataId);
}
if (i != null) {
let m = C.getUndoAxesPermutation(i), d = rr({ inputs: { x: l }, backend: t10, attrs: { perm: m } });
return t10.disposeData(l.dataId), t10.disposeData(p.dataId), d;
}
return l;
}
function Uie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
return vx(Rp.Prod, n, t10, s, a, i);
}
var Jz = { kernelName: an, backendName: "webgpu", kernelFunc: Uie };
function Gie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
return vx(Rp.Sum, n, t10, s, a, i);
}
var eV = { kernelName: un, backendName: "webgpu", kernelFunc: Gie };
function Hie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o, p = n.shape.length === 1, c = y.sizeFromShape(s.shape) > 0, l = s.dtype, m = p ? [n.shape[0]] : [n.shape[0], n.shape[1]], d = p ? [a] : [n.shape[0], a], f = Vt({ backend: t10, attrs: { shape: d, value: 0, dtype: l } }), h = new jc(m, c, i), g = [{ type: "int32", data: [a] }], x = c ? [n, s] : [n];
return t10.runWebGPUProgram(h, x, l, g, f);
}
var tV = { kernelName: Qs, backendName: "webgpu", kernelFunc: Hie };
var kx = class {
constructor(e, t10) {
this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `depthToSpace_${t10}`, this.dataFormat = t10;
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let h = ${this.getHeightCoordString()};
let w = ${this.getWidthCoordString()};
let d = ${this.getDepthCoordString()};
let in_h = h / uniforms.blockSize;
let offset_h = h % uniforms.blockSize;
let in_w = w / uniforms.blockSize;
let offset_w = w % uniforms.blockSize;
let offset_d = (offset_h * uniforms.blockSize + offset_w) *
${this.getOutputDepthSize()};
let in_d = d + offset_d;
let rlt = ${this.getInputSamplingString()};
setOutputAtIndex(index, rlt);
}
}`;
}
getHeightCoordString() {
return this.dataFormat === "NHWC" ? "coords[1]" : "coords[2]";
}
getWidthCoordString() {
return this.dataFormat === "NHWC" ? "coords[2]" : "coords[3]";
}
getDepthCoordString() {
return this.dataFormat === "NHWC" ? "coords[3]" : "coords[1]";
}
getOutputDepthSize() {
return this.dataFormat === "NHWC" ? "uniforms.outShape[3]" : "uniforms.outShape[1]";
}
getInputSamplingString() {
return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
}
};
function Kie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = [{ type: "int32", data: [s] }], g = new kx(f, a);
return t10.runWebGPUProgram(g, [n], n.dtype, h);
}
var rV = { kernelName: cn, backendName: "webgpu", kernelFunc: Kie };
var Nx = class {
constructor(e, t10, o, n = false, s = null, a = false) {
this.variableNames = ["x", "W"], this.uniforms = "pads : vec2<i32>, inDims : vec2<i32>,", this.workgroupSize = [16, 16, 1], this.outputShape = e, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), n && this.variableNames.push("bias"), a && this.variableNames.push("preluActivationWeights"), this.addBias = n, this.activation = s, this.hasPreluActivation = a, this.filterHeight = t10, this.filterWidth = o, this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`;
}
getUserCode() {
let e = this.filterWidth * this.filterHeight, t10 = this.workgroupSize[0] * this.workgroupSize[1] * this.workgroupSize[2], o = this.workgroupSize[1] + this.filterHeight - 1, n = this.workgroupSize[0] + this.filterWidth - 1;
return `
${dr(this.activation, this.hasPreluActivation, false, 4)}
var<workgroup> mm_Asub : array<array<f32, ${n}>, ${o}>;
var<workgroup> mm_Bsub : array<array<f32, ${this.filterWidth}>, ${this.filterHeight}>;
fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {
var value = 0.0;
if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])
{
value = getX(batch, channel, row, col);
}
return value;
}
${K()} {
let coords = getOutputCoords();
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.zw) - uniforms.pads;
let channelMul = uniforms.wShape[3];
let d1 = coords[1] / channelMul;
let q = coords[1] % channelMul;
let inputRowStart = xRCCorner.x;
let inputColStart = xRCCorner.y;
let localRow = i32(localId.y);
let localCol = i32(localId.x);
// Load one tile of X into local memory.
for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {
for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {
let rowOffset = inputRow - localRow;
let colOffset = inputCol - localCol;
mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);
}
}
// Load one tile of W into local memory.
var wIndex = i32(localIndex);
${e < t10 ? `if (wIndex < ${e})` : `for(; wIndex < ${e}; wIndex = wIndex + ${t10})`}
{
let wRow = wIndex / ${this.filterWidth};
let wCol = wIndex % ${this.filterWidth};
mm_Bsub[wRow][wCol] = getW(wRow, wCol, d1, q);
}
workgroupBarrier();
var value = 0.0;
for (var wR = 0; wR < ${this.filterHeight}; wR = wR + 1) {
for (var wC = 0; wC < ${this.filterWidth}; wC = wC + 1) {
let xVal = mm_Asub[localRow + wR][localCol + wC];
let wVal = mm_Bsub[wR][wC];
value = fma(xVal, wVal, value);
}
}
${jr(this.addBias, this.activation)}
if (coordsInBounds4D(coords, uniforms.outShape)) {
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
}
}
`;
}
};
var Yc = class {
constructor(e, t10 = false, o = null, n = false) {
this.variableNames = ["x", "W"], this.uniforms = "pads : vec2<i32>, inDims : vec2<i32>,", this.workgroupSize = [4, 4, 4], this.workPerThread = 4, this.outputComponent = 4, this.outputShape = e.outShape, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [4, this.workPerThread, 1]), y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t10, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`;
}
getUserCode() {
let e = (this.workPerThread - 1) * this.convInfo.strideWidth + this.convInfo.filterWidth, t10 = this.convInfo.strideHeight, o = this.convInfo.strideWidth;
return `
${dr(this.activation, this.hasPreluActivation, true, 4)}
fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4<f32> {
var value = vec4<f32>(0.0);
if (col >=0 && col < uniforms.inDims[1]) {
value = getX(batch, row, col, channel);
}
return value;
}
${K()} {
let batch = i32(globalId.z) / uniforms.outShape[1];
let r = i32(globalId.z) % uniforms.outShape[1];
let c = i32(globalId.y) * ${this.workPerThread};
let d1 = i32(globalId.x) * 4;
let xRCCorner = vec2<i32>(r, c) * vec2<i32>(${t10}, ${o}) - uniforms.pads;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var xVals : array<vec4<f32>, ${e}>;
var dotProd : array<vec4<f32>, ${this.workPerThread}>;
for (var i = 0; i < ${this.workPerThread}; i++) {
dotProd[i] = vec4<f32>(0.0);
}
// Use constant instead of uniform can give better performance.
for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
let xR = xRCorner + wR;
if (xR >=0 && xR < uniforms.inDims[0]) {
for (var i = 0; i < ${e}; i++) {
xVals[i] = readX(batch, xR, xCCorner + i, d1);
}
for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
let wValue = getW(wR, wC, d1, 0);
for (var i = 0; i < ${this.workPerThread}; i++) {
dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);
}
}
}
}
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let coords = vec4<i32>(batch, r, c + i, d1);
if (coordsInBounds4D(coords, uniforms.outShape)) {
var value = dotProd[i];
${jr(this.addBias, this.activation)}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
}
}
}
`;
}
};
var Qc = class {
constructor(e, t10 = false, o = null, n = false) {
this.variableNames = ["x", "W"], this.uniforms = `pads : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,
filterWidth : i32, strides : vec2<i32>, dilations : vec2<i32>,`, this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t10, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`;
}
getUserCode() {
let e = this.isChannelsLast ? "getX(batch, xR, xC, d1);" : "getX(batch, d1, xR, xC);";
return `
${dr(this.activation, this.hasPreluActivation, false, 4)}
${K("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.${this.isChannelsLast ? "yz" : "zw"}) * uniforms.strides - uniforms.pads;
let d2 = coords[${this.isChannelsLast ? 3 : 1}];
let channelMul = uniforms.wShape[3];
let d1 = d2 / channelMul;
let q = d2 % channelMul;
let inputRowStart = xRCCorner.x;
let inputColStart = xRCCorner.y;
let inputRowEnd = inputRowStart + uniforms.filterHeight *
uniforms.dilations[0];
let inputColEnd = inputColStart + uniforms.filterWidth *
uniforms.dilations[1];
// Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get
// y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all
// values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.
// x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.
var value = 0.0;
// Extract if checking out of for loop for performance.
if (inputRowStart >= 0 && inputColStart >= 0 &&
inputRowEnd < uniforms.inDims[0] &&
inputColEnd < uniforms.inDims[1]) {
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilations[0];
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilations[1];
let xVal = ${e};
let wVal = getW(wR, wC, d1, q);
value = value + xVal * wVal;
}
}
} else {
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilations[0];
if (xR < 0 || xR >= uniforms.inDims[0]) {
continue;
}
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilations[1];
if (xC < 0 || xC >= uniforms.inDims[1]) {
continue;
}
let xVal = ${e};
let wVal = getW(wR, wC, d1, q);
value = value + xVal * wVal;
}
}
}
${jr(this.addBias, this.activation)}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
}
}
`;
}
};
function qie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = C.convertConv2DDataFormat(p), m = u;
m == null && (m = [1, 1]);
let d = C.computeConv2DInfo(n.shape, s.shape, a, m, i, c, true, l), f = [{ type: "int32", data: [d.padInfo.top, d.padInfo.left] }, { type: "int32", data: [d.inHeight, d.inWidth] }], h = d.dataFormat === "channelsLast", g;
return !h && d.inHeight > 16 && d.inWidth > 16 && d.strideHeight === 1 && d.strideWidth === 1 && d.dilationWidth === 1 && d.dilationHeight === 1 && d.inChannels === d.outChannels ? g = new Nx(d.outShape, d.filterHeight, d.filterWidth) : h && d.outHeight > 4 && d.outWidth > 4 && d.strideWidth <= 2 && d.inChannels === d.outChannels && d.dilationHeight === 1 && d.dilationWidth === 1 && d.inChannels % 4 === 0 ? g = new Yc(d) : (g = new Qc(d), f.push({ type: "int32", data: [d.filterHeight] }, { type: "int32", data: [d.filterWidth] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.dilationHeight, d.dilationWidth] })), t10.runWebGPUProgram(g, [n, s], n.dtype, f);
}
var oV = { kernelName: ln, backendName: "webgpu", kernelFunc: qie };
var Tx = class {
constructor(e) {
this.variableNames = ["x", "dy"], this.uniforms = `strides : vec2<i32>, pads : vec2<i32>, filterDims : vec2<i32>, outHeight : i32,
outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "depthwise_conv2d_backprop_filter";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let wR = coords[0];
let wC = coords[1];
let d1 = coords[2];
let dm = coords[3];
let d2 = d1 * uniforms.channelMul + dm;
var dotProd = 0.0;
for (var b = 0; b < uniforms.batchSize; b++) {
for (var yR = 0; yR < uniforms.outHeight; yR++) {
let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];
if (xR < 0 || xR >= uniforms.inHeight) {
continue;
}
for (var yC = 0; yC < uniforms.outWidth; yC++) {
let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];
if (xC < 0 || xC >= uniforms.inWidth) {
continue;
}
let dyValue = getDy(b, yR, yC, d2);
let xValue = getX(b, xR, xC, d1);
dotProd += xValue * dyValue;
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
var _x = class {
constructor(e) {
this.variableNames = ["dy", "W"], this.uniforms = `strides : vec2<i32>, pads : vec2<i32>, filterDims : vec2<i32>,
outHeight : i32, outWidth : i32, channelMul : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "depthwise_conv2d_backprop_input";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d1 = coords[3];
let dyCorner = coords.yz - uniforms.pads;
let dyRCorner = dyCorner.x;
let dyCCorner = dyCorner.y;
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {
let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);
if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
continue;
}
let idyR = i32(dyR);
let wRPerm = uniforms.filterDims[0] - 1 - wR;
for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {
let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);
if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
continue;
}
let idyC = i32(dyC);
let wCPerm = uniforms.filterDims[1] - 1 - wC;
for (var dm = 0; dm < uniforms.channelMul; dm++) {
let d2 = d1 * uniforms.channelMul + dm;
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
function jie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = C.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new Tx(l), d = [{ type: "int32", data: [l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.filterHeight, l.filterWidth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "int32", data: [l.inHeight] }, { type: "int32", data: [l.inWidth] }, { type: "int32", data: [l.batchSize] }, { type: "int32", data: [l.outChannels / l.inChannels] }];
return t10.runWebGPUProgram(m, [n, s], "float32", d);
}
var nV = { kernelName: Ei, backendName: "webgpu", kernelFunc: jie };
function Xie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = C.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new _x(l), d = [{ type: "int32", data: [l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.filterHeight - 1 - l.padInfo.top, l.filterWidth - 1 - l.padInfo.left] }, { type: "int32", data: [l.filterHeight, l.filterWidth] }, { type: "int32", data: [l.outHeight] }, { type: "int32", data: [l.outWidth] }, { type: "int32", data: [l.outChannels / l.inChannels] }];
return t10.runWebGPUProgram(m, [n, s], n.dtype, d);
}
var sV = { kernelName: Ri, backendName: "webgpu", kernelFunc: Xie };
var $x = class {
constructor(e) {
this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, e], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "diag";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);
setOutputAtIndex(index, value);
}
}
`;
}
};
function Yie(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = pe({ inputs: { x: o }, backend: t10, attrs: { shape: [s] } }), i = new $x(s), p = t10.runWebGPUProgram(i, [a], a.dtype), u = pe({ inputs: { x: p }, backend: t10, attrs: { shape: n } });
return t10.disposeData(a.dataId), t10.disposeData(p.dataId), u;
}
var aV = { kernelName: Zs, backendName: "webgpu", kernelFunc: Yie };
var Ex = class {
constructor(e) {
this.variableNames = ["x", "w"], this.uniforms = "filterDims: vec2<i32>, pads: vec2<i32>, strides: vec2<i32>, dilations: vec2<i32>", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "dilation2d";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let neg_infinity = -3.4e38;
let coords = getOutputCoords();
let batch = coords.x;
let d1 = coords.w;
let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads;
let hBeg = outTopLeftCorner.x;
let wBeg = outTopLeftCorner.y;
var curVal = neg_infinity;
for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {
let hIn = hBeg + h * uniforms.dilations[0];
if (hIn >= 0 && hIn < uniforms.xShape[1]) {
for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {
let wIn = wBeg + w * uniforms.dilations[1];
if (wIn >= 0 && wIn < uniforms.xShape[2]) {
let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);
if (val > curVal) {
curVal = val;
}
}
}
}
}
setOutputAtIndex(index, curVal);
}
}
`;
}
};
function Qie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = C.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = [u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationHeight, u.dilationWidth] }], m = new Ex(u);
return t10.runWebGPUProgram(m, [n, s], n.dtype, l);
}
var iV = { kernelName: mn, backendName: "webgpu", kernelFunc: Qie };
var Rx = class {
constructor(e, t10) {
if (this.variableNames = ["x", "w", "dy"], this.uniforms = "filterDims: vec2<i32>, pads: vec2<i32>, strides: vec2<i32>, dilations: vec2<i32>, dySize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = e.inShape, this.dispatchLayout = Z(e.outShape), this.dispatch = q(this.dispatchLayout, e.outShape, this.workgroupSize), t10 !== "float32" && t10 !== "int32")
throw new Error(`Dilation2DBackpropInput only supports float32 and int32
types, does not support ${t10} type.`);
this.type = t10, this.shaderKey = "dilation2DBackpropInput";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.dySize) {
let coords = getDyCoordsFromIndex(index);
let b = coords[0];
let r = coords[1];
let c = coords[2];
let d = coords[3];
let dyCorner = vec2<i32>(r, c) * uniforms.strides - uniforms.pads;
var curVal = -3.4e38; // neg_infinity
var xRMax = 0;
var xCMax = 0;
// In the case of multiple argmax branches, we only back-propagate
// along the last branch, i.e., the one with largest value of
// 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling
// backward routines.
for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {
let xR = dyCorner.x + wR * uniforms.dilations[0];
if (xR >= 0 && xR < uniforms.xShape[1]) {
for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {
let xC = dyCorner.y + wC * uniforms.dilations[1];
if (xC >= 0 && xC < uniforms.xShape[2]) {
let val = getX(b, xR, xC, d) + getW(wR, wC, d);
if (val > curVal) {
curVal = val;
xRMax = xR;
xCMax = xC;
}
}
}
}
}
let flatIndexIn = d + uniforms.xShape[3] *
(xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b));
let value = getDy(b, r, c, d);
${Bs("&result[flatIndexIn]", "value", this.type)}
}
}
`;
}
};
var Dx = class {
constructor(e, t10, o) {
if (this.variableNames = ["x", "w", "dy"], this.uniforms = "filterDims: vec2<i32>, pads: vec2<i32>, strides: vec2<i32>, dilations: vec2<i32>, dySize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = e.filterShape, this.dispatchLayout = Z(e.outShape), this.dispatch = q(this.dispatchLayout, e.outShape, this.workgroupSize), o !== "float32" && o !== "int32")
throw new Error(`Dilation2DBackpropFilter only supports float32 and int32
types, does not support ${o} type.`);
this.type = o, this.shaderKey = "dilation2DBackpropFilter";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.dySize) {
let coords = getDyCoordsFromIndex(index);
let b = coords[0];
let r = coords[1];
let c = coords[2];
let d = coords[3];
let dyCorner = vec2<i32>(r, c) * uniforms.strides - uniforms.pads;
var curVal = -3.4e38; // neg_infinity
var wRMax = 0;
var wCMax = 0;
// In the case of multiple argmax branches, we only back-propagate
// along the last branch, i.e., the one with largest value of
// 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling
// backward routines.
for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {
let xR = dyCorner.x + wR * uniforms.dilations[0];
if (xR >= 0 && xR < uniforms.xShape[1]) {
for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {
let xC = dyCorner.y + wC * uniforms.dilations[1];
if (xC >= 0 && xC < uniforms.xShape[2]) {
let val = getX(b, xR, xC, d) + getW(wR, wC, d);
if (val > curVal) {
curVal = val;
wRMax = wR;
wCMax = wC;
}
}
}
}
}
let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]);
let value = getDy(b, r, c, d);
${Bs("&result[flatIndexIn]", "value", this.type)}
}
}
`;
}
};
function Zie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o, c = C.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = s.dtype, m = new Dx(c, s.shape, l), d = [{ type: "int32", data: [c.filterHeight, c.filterWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [y.sizeFromShape(c.outShape)] }], f = Vt({ backend: t10, attrs: { shape: s.shape, value: 0, dtype: l } });
return t10.runWebGPUProgram(m, [n, s, a], l, d, f);
}
var uV = { kernelName: Ai, backendName: "webgpu", kernelFunc: Zie };
function Jie(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, dy: a } = e, { strides: i, pad: p, dilations: u } = o, c = C.computeDilation2DInfo(n.shape, s.shape, i, p, "NHWC", u), l = n.dtype, m = new Rx(c, l), d = [{ type: "int32", data: [c.filterHeight, c.filterWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [y.sizeFromShape(c.outShape)] }], f = Vt({ backend: t10, attrs: { shape: c.inShape, value: 0, dtype: l } });
return t10.runWebGPUProgram(m, [n, s, a], l, d, f);
}
var pV = { kernelName: Di, backendName: "webgpu", kernelFunc: Jie };
var Wv = et({ opType: fe.MUL, cpuKernelImpl: HB, supportsComplex: true });
var cV = { kernelName: Kn, backendName: "webgpu", kernelFunc: Wv };
function Uv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
return Yr(n, s, a, "sum", t10);
}
var lV = { kernelName: ys, backendName: "webgpu", kernelFunc: Uv };
function eue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = C.decodeEinsumEquation(n, s.length);
C.checkEinsumDimSizes(a.length, p, s);
let { path: u, steps: c } = C.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = [];
for (let h = 0; h < l; ++h) {
for (let g of c[h]) {
let { permutationIndices: x, expandDims: b } = C.getEinsumPermutation(d, p[g]), w;
C.isIdentityPermutation(x) ? w = s[g] : (w = rr({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(w));
let S = w.shape.slice();
for (let k = 0; k < b.length; ++k)
S.splice(b[k], 0, 1);
y.arraysEqual(w.shape, S) || (w = pe({ inputs: { x: w }, backend: t10, attrs: { shape: S } }), f.push(w)), m === null ? m = w : (m = Wv({ inputs: { a: w, b: m }, backend: t10 }), f.push(m));
}
h < l - 1 && (u[h] >= 0 && (m = Uv({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--);
}
for (let h of f)
h !== m && t10.disposeData(h.dataId);
return m;
}
var mV = { kernelName: Fi, backendName: "webgpu", kernelFunc: eue };
var tue = xe({ opType: Q.ELU });
var dV = { kernelName: fn, backendName: "webgpu", kernelFunc: tue };
var rue = (r) => {
let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e, s = new bi(fe.ELU_DER, o.shape, n.shape);
return t10.runWebGPUProgram(s, [o, n], o.dtype);
};
var fV = { kernelName: Va, backendName: "webgpu", kernelFunc: rue };
var oue = et({ opType: fe.EQUAL, dtype: "bool", cpuKernelImpl: EB });
var hV = { kernelName: hn, backendName: "webgpu", kernelFunc: oue };
var nue = xe({ opType: Q.ERF });
var gV = { kernelName: Wa, backendName: "webgpu", kernelFunc: nue };
var sue = xe({ opType: Q.EXP, cpuKernelImpl: RB, dtype: "float32" });
var xV = { kernelName: gn, backendName: "webgpu", kernelFunc: sue };
function Ax(r) {
let { inputs: e, attrs: t10, backend: o } = r, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n;
return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), pe({ inputs: { x: s }, backend: o, attrs: { shape: i } });
}
var yV = { kernelName: Js, backendName: "webgpu", kernelFunc: Ax };
var aue = xe({ opType: Q.EXPM1, cpuKernelImpl: DB });
var bV = { kernelName: xn, backendName: "webgpu", kernelFunc: aue };
var fm = class {
constructor(e, t10) {
this.variableNames = ["real", "imag"], this.outputShape = [], this.uniforms = "exponentMultiplier : f32, denominator: f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.component = e, this.shaderKey = `fft_${e}`;
}
getUserCode() {
return `
fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {
${this.component === "real" ? "return real * expR - imag * expI;" : "return real * expI + imag * expR;"}
}
fn mulMatDFT(batch: i32, index: i32) -> f32 {
let indexRatio = f32(index) / f32(uniforms.realShape[1]);
let exponentMultiplierTimesIndexRatio =
uniforms.exponentMultiplier * indexRatio;
var result = 0.0;
for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {
// x = (-2|2 * PI / N) * index * i;
let x = exponentMultiplierTimesIndexRatio * f32(i);
let expR = cos(x);
let expI = sin(x);
let real = getReal(batch, i);
let imag = getImag(batch, i);
result = result +
unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;
}
return result;
}
${K("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));
}
}
`;
}
};
function Fx(r, e, t10) {
let o = t10.tensorMap.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = [], p = pe({ inputs: { x: r }, backend: t10, attrs: { shape: [a, s] } });
i.push(p);
let u = p.shape, c = new fm("real", u), l = new fm("imag", u), m = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: u }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: u }], d = e ? 2 * Math.PI : -2 * Math.PI, f = e ? u[1] : 1, h = [{ type: "float32", data: [d] }, { type: "float32", data: [f] }], g = t10.runWebGPUProgram(c, m, "float32", h);
i.push(g);
let x = t10.runWebGPUProgram(l, m, "float32", h);
i.push(x);
let b = fo({ inputs: { real: g, imag: x }, backend: t10 });
i.push(b);
let w = pe({ inputs: { x: b }, backend: t10, attrs: { shape: r.shape } });
return i.forEach((S) => t10.disposeData(S.dataId)), w;
}
function iue(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e;
return Fx(o, false, t10);
}
var CV = { kernelName: Pi, backendName: "webgpu", kernelFunc: iue };
var Px = class {
constructor(e) {
this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "flipLeftRight";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let coordX = uniforms.xShape[2] - coords[2] - 1;
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
setOutputAtIndex(index, outputValue);
}
}
`;
}
};
var wV = { kernelName: yn, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => {
let { image: t10 } = r, o = e, n = new Px(t10.shape);
return o.runWebGPUProgram(n, [t10], t10.dtype);
} };
var uue = xe({ opType: Q.FLOOR, cpuKernelImpl: AB });
var SV = { kernelName: bn, backendName: "webgpu", kernelFunc: uue };
var pue = et({ opType: fe.INT_DIV, cpuKernelImpl: FB, dtype: "int32" });
var IV = { kernelName: Cn, backendName: "webgpu", kernelFunc: pue };
var Ox = class {
constructor(e, t10, o = false) {
this.isFromPixels = true, this.outputShape = [0], this.variableNames = [], this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [t10, 1, 1]), this.importVideo = o, this.shaderKey = `fromPixels_${this.importVideo}`;
}
getUserCode() {
let e = this.importVideo ? "textureLoad(src, vec2<i32>(coords.yx));" : "textureLoad(src, vec2<i32>(coords.yx), 0)";
return `
@binding(1) @group(0) var src: ${this.importVideo ? "texture_external" : "texture_2d<f32>"};
${K("index")} {
let flatIndex = index * uniforms.numChannels;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
let values = ${e};
for (var i = 0; i < uniforms.numChannels; i = i + 1) {
result[flatIndex + i] = i32(floor(255.0 * values[i]));
}
}
}
`;
}
};
var vV = { kernelName: $u, backendName: "webgpu", kernelFunc: cue };
var Zc;
var Gv = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
function cue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o;
if (n == null)
throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
let a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, p = typeof HTMLCanvasElement != "undefined" && n instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && n instanceof OffscreenCanvas, u = typeof ImageBitmap != "undefined" && n instanceof ImageBitmap, [c, l] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], m = [l, c, s], d = false, f = a || i;
if (u || p || f) {
let b;
if (d)
b = { width: c, height: l, format: null, usage: null, texture: t10.device.importExternalTexture({ source: n }) };
else {
if (f) {
let L = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
(Zc == null || L !== Gv) && (Gv = L, Zc = document.createElement("canvas").getContext("2d", { willReadFrequently: Gv })), Zc.canvas.width = c, Zc.canvas.height = l, Zc.drawImage(n, 0, 0, c, l), n = Zc.canvas;
}
let F = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, O = "rgba8unorm", M = t10.textureManager.acquireTexture(m[1], m[0], O, F);
t10.queue.copyExternalImageToTexture({ source: n }, { texture: M }, [m[1], m[0]]), b = { width: c, height: l, format: O, usage: F, texture: M };
}
let w = y.sizeFromShape(m), S = y.computeStrides(m), k = new Ox(m, s, d), _ = [{ type: "uint32", data: [w] }, { type: "uint32", data: [s] }, { type: "uint32", data: [...S] }], E = t10.makeTensorInfo([l, c], "int32"), R = t10.tensorMap.get(E.dataId);
R.resourceInfo = b;
let D = t10.runWebGPUProgram(k, [E], "int32", _);
return t10.disposeData(E.dataId), D;
}
let h = n.data, g = h;
if (s != null && s !== 4) {
g = new Uint8Array(n.width * n.height * s);
let b = h.length, w = 0;
for (let S = 0; S < b; S++)
S % 4 < s && (g[w++] = h[S]);
}
let x = t10.makeTensorInfo(m, "int32", new Int32Array(g));
return t10.uploadToGPU(x.dataId), x;
}
var Mx = class {
constructor(e, t10, o, n, s) {
this.uniforms = "varianceEpsilon : f32,", this.workgroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t10), C.assertAndGetBroadcastShape(e, o), this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), n != null && (C.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset")), s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale")), this.offsetShape = n, this.scaleShape = s, this.shaderKey = "batchNorm";
}
getUserCode() {
let e = "0.0";
this.offsetShape != null && (e = "getOffsetByOutputIndex(index)");
let t10 = "1.0";
return this.scaleShape != null && (t10 = "getScaleByOutputIndex(index)"), `
${K("index")} {
if (index < uniforms.size)
{
let xValue = getXByOutputIndex(index);
let meanValue = getMeanByOutputIndex(index);
let varianValue = getVarianceByOutputIndex(index);
let offsetValue = ${e};
let scaleValue = ${t10};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
}
`;
}
};
var kV = { kernelName: wn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
let { x: o, scale: n, offset: s, mean: a, variance: i } = r, { varianceEpsilon: p } = e, u = t10, c = [o, a, i], l = null;
s != null && (l = s.shape, c.push(s));
let m = null;
n != null && (m = n.shape, c.push(n));
let d = new Mx(o.shape, a.shape, i.shape, l, m), f = [{ type: "float32", data: [p] }];
return u.runWebGPUProgram(d, c, o.dtype, f);
} };
function lue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h);
return gx({ x: n, filter: s, convInfo: g, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: f, activation: d });
}
var NV = { kernelName: Co, backendName: "webgpu", kernelFunc: lue };
function mue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = c;
f == null && (f = [1, 1]), y.assert(C.eitherStridesOrDilationsAreOne(p, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);
let h = C.computeConv2DInfo(n.shape, s.shape, p, f, u, l, true), g = [n, s], x = a != null, b = i != null;
x && g.push(a), b && g.push(i);
let w = [{ type: "int32", data: [h.padInfo.top, h.padInfo.left] }, { type: "int32", data: [h.inHeight, h.inWidth] }], S;
return h.outHeight > 4 && h.outWidth > 4 && h.strideWidth <= 2 && h.inChannels === h.outChannels && h.dilationHeight === 1 && h.dilationWidth === 1 && h.inChannels % 4 === 0 ? S = new Yc(h, x, m, b) : (S = new Qc(h, x, m, b), w.push({ type: "int32", data: [h.filterHeight] }, { type: "int32", data: [h.filterWidth] }, { type: "int32", data: [h.strideHeight, h.strideWidth] }, { type: "int32", data: [h.dilationHeight, h.dilationWidth] })), m === "leakyrelu" && (w.push({ type: "float32", data: [d] }), S.uniforms += " alpha : f32,"), t10.runWebGPUProgram(S, g, "float32", w);
}
var TV = { kernelName: wo, backendName: "webgpu", kernelFunc: mue };
var Lx = class {
constructor(e, t10) {
this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${Nt(e)},`;
}
getUserCode() {
let e;
return this.sliceDim > 1 ? e = "uniforms.strides[j]" : e = "uniforms.strides", `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
var flattenIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexTemp = i32(round(getIndices(coords[0], j)));
let strideNum = ${e};
flattenIndex = flattenIndex + indexTemp * strideNum;
}
setOutputAtIndex(index, getA(flattenIndex, coords[1]));
}
}
`;
}
};
function due(r) {
let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = C.prepareAndValidate(o, n), m = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [u, a] } }), d = pe({ inputs: { x: o }, backend: t10, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } });
if (t10.shouldExecuteOnCPU([o, n]) || o.dtype === "string") {
let b = t10.readSync(n.dataId), w = t10.bufferSync(o), S = PB(b, w, o.dtype, u, a, c, l, o.shape, i);
return t10.makeTensorInfo(p, o.dtype, S.values);
}
let f = new Lx(a, [u, c]), h = [{ type: "int32", data: [a] }, { type: "int32", data: l }], g = t10.runWebGPUProgram(f, [d, m], d.dtype, h), x = pe({ inputs: { x: g }, backend: t10, attrs: { shape: p } });
return t10.disposeData(m.dataId), t10.disposeData(d.dataId), t10.disposeData(g.dataId), x;
}
var _V = { kernelName: Sn, backendName: "webgpu", kernelFunc: due };
var Bx = class {
constructor(e, t10) {
this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t10, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "gather";
}
getUserCode() {
let e = fue(this.aShape);
return `
${K("index")} {
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let indexZ = i32(getIndices(resRC.x, resRC.z));
let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
setOutputAtIndex(index, inBounds * getA(${e}));
}
}
`;
}
};
function fue(r) {
let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], t10 = [];
for (let o = 0; o < r.length; o++)
o === 2 ? t10.push("indexZ") : t10.push(`${e[o]}`);
return t10.join();
}
function Hv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = C.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = pe({ inputs: { x: s }, backend: t10, attrs: { shape: [u.batchSize, c / u.batchSize] } });
l.push(m), l.push(d);
let f = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize];
if (t10.shouldExecuteOnCPU([n, s])) {
let w = t10.tensorMap.get(d.dataId).values, S = me(d.shape, d.dtype, w), _ = t10.tensorMap.get(m.dataId).values, E = me(m.shape, m.dtype, _), R = OB(E, S, f);
return l.forEach((D) => t10.disposeData(D.dataId)), t10.makeTensorInfo(u.outputShape, R.dtype, R.values);
}
let h = new Bx(m.shape, f), g = t10.runWebGPUProgram(h, [m, d], m.dtype);
l.push(g);
let x = pe({ inputs: { x: g }, backend: t10, attrs: { shape: u.outputShape } });
return l.forEach((b) => t10.disposeData(b.dataId)), x;
}
var $V = { kernelName: ta, backendName: "webgpu", kernelFunc: Hv };
var hue = et({ opType: fe.GREATER, cpuKernelImpl: LB, dtype: "bool" });
var EV = { kernelName: In, backendName: "webgpu", kernelFunc: hue };
var gue = et({ opType: fe.GREATER_EQUAL, dtype: "bool", cpuKernelImpl: MB });
var RV = { kernelName: vn, backendName: "webgpu", kernelFunc: gue };
function xue(r) {
let { inputs: e, backend: t10 } = r, { input: o } = e;
return Fx(o, true, t10);
}
var DV = { kernelName: Oi, backendName: "webgpu", kernelFunc: xue };
var yue = xe({ opType: Q.IS_FINITE, dtype: "bool" });
var AV = { kernelName: kn, backendName: "webgpu", kernelFunc: yue };
var bue = xe({ opType: Q.IS_INF, dtype: "bool" });
var FV = { kernelName: Nn, backendName: "webgpu", kernelFunc: bue };
var Cue = xe({ opType: Q.IS_NAN, dtype: "bool" });
var PV = { kernelName: Tn, backendName: "webgpu", kernelFunc: Cue };
function wue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = [{ type: "float32", data: [s] }], i = new Xr(n.shape, Q.LEAKYRELU, "alpha : f32,");
return t10.runWebGPUProgram(i, [n], "float32", a);
}
var OV = { kernelName: _n, backendName: "webgpu", kernelFunc: wue };
var Sue = et({ opType: fe.LESS, dtype: "bool", cpuKernelImpl: zB });
var MV = { kernelName: $n, backendName: "webgpu", kernelFunc: Sue };
var Iue = et({ opType: fe.LESS_EQUAL, dtype: "bool", cpuKernelImpl: BB });
var LV = { kernelName: En, backendName: "webgpu", kernelFunc: Iue };
var zx = class {
constructor(e) {
this.variableNames = [], this.outputShape = [], this.uniforms = "start : f32, step : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "linSpace";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);
}
}
`;
}
};
function vue(r) {
let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = (n - o) / (s - 1), i = new zx(s), p = [{ type: "float32", data: [o] }, { type: "float32", data: [a] }];
return e.runWebGPUProgram(i, [], "float32", p);
}
var BV = { kernelName: Rn, backendName: "webgpu", kernelFunc: vue };
var kue = xe({ opType: Q.LOG, cpuKernelImpl: VB });
var zV = { kernelName: Dn, backendName: "webgpu", kernelFunc: kue };
var Nue = xe({ opType: Q.LOG1P });
var VV = { kernelName: An, backendName: "webgpu", kernelFunc: Nue };
var Tue = et({ opType: fe.LOGICAL_AND, dtype: "bool" });
var WV = { kernelName: Fn, backendName: "webgpu", kernelFunc: Tue };
var _ue = xe({ opType: Q.LOGICAL_NOT });
var UV = { kernelName: Pn, backendName: "webgpu", kernelFunc: _ue };
var $ue = et({ opType: fe.LOGICAL_OR });
var GV = { kernelName: On, backendName: "webgpu", kernelFunc: $ue };
var HV = `
var powValue = 0.0;
let basis = uniforms.bias + uniforms.alpha * sum;
if (uniforms.beta == 0.5) {
powValue = inverseSqrt(basis);
} else if (uniforms.beta == 1.0) {
powValue = 1.0 / basis;
} else {
powValue = exp(log(basis) * (-uniforms.beta));
}
`;
var Vx = class {
constructor(e) {
this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
let b = coords[0];
let r = coords[1];
let c = coords[2];
let d = coords[3];
let x = getX(b, r, c, d);
var sum = 0.0;
for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {
let idx = d + i;
if (idx >= 0 && idx < uniforms.xShape[3]) {
let z = getX(b, r, c, idx);
sum = sum + z * z;
}
}
${HV}
setOutputAtIndex(index, x * powValue);
}
}
`;
}
};
var Wx = class {
constructor(e, t10) {
this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [256, 1, 1], this.maxAllowRadius = 16, y.assert(t10 <= this.maxAllowRadius, () => `Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t10}`), this.outputShape = e, this.elementsPerWorkgroup = this.workgroupSize[0] - 2 * this.maxAllowRadius, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = q(this.dispatchLayout, this.outputShape, [this.elementsPerWorkgroup, this.workgroupSize[1], this.workgroupSize[2]]), this.shaderKey = "lrn_shared";
}
getUserCode() {
return `
var <workgroup>lrnSub: array<f32, ${this.workgroupSize[0]}>;
const elementsPerWorkgroup = ${this.elementsPerWorkgroup};
const maxAllowRadius = ${this.maxAllowRadius};
${K()} {
let localDepth = i32(localId.x);
let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;
let xDepth = workgroupDepth + localDepth - maxAllowRadius;
let b = i32(globalId.z) / uniforms.xShape[1];
let r = i32(globalId.z) - b * uniforms.xShape[1];
let c = i32(globalId.y);
let d = workgroupDepth + localDepth;
var x = 0.0;
if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {
x = getX(b, r, c, xDepth);
}
lrnSub[localDepth] = x;
workgroupBarrier();
if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {
var sum = 0.0;
let index = localDepth + maxAllowRadius;
for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {
let z = lrnSub[index + i];
sum = sum + z * z;
}
${HV}
setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);
}
} `;
}
};
function Eue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u;
s > 16 ? u = new Vx(n.shape) : u = new Wx(n.shape, s);
let c = [{ type: "int32", data: [s] }, { type: "float32", data: [a] }, { type: "float32", data: [i] }, { type: "float32", data: [p] }];
return t10.runWebGPUProgram(u, [n], n.dtype, c);
}
var KV = { kernelName: Mn, backendName: "webgpu", kernelFunc: Eue };
var Ux = class {
constructor(e) {
this.outputShape = [], this.variableNames = ["inputImage", "outputImage", "dy"], this.uniforms = "depthRadius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn_grad";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
let b = coords[0];
let r = coords[1];
let c = coords[2];
let MIN_DEPTH_BEGIN = 0;
let MAX_DEPTH_END = uniforms.outShape[3];
var result = 0.0;
for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {
let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);
let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);
var norm = 0.0;
for (var 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 = uniforms.alpha * norm + uniforms.bias;
for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {
if (k < depthBegin) {
continue;
} else if (k >= depthBegin && k < depthEnd) {
var dyi = -2.0 * uniforms.alpha * uniforms.beta
* getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;
if (k == d) {
dyi += pow(norm, -1.0 * uniforms.beta);
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
} else {
break;
}
}
}
setOutputAtIndex(index, result);
}
}
`;
}
};
function Rue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new Ux(n.shape), m = [{ type: "int32", data: [i] }, { type: "float32", data: [p] }, { type: "float32", data: [u] }, { type: "float32", data: [c] }];
return t10.runWebGPUProgram(l, [n, s, a], n.dtype, m);
}
var qV = { kernelName: Ua, backendName: "webgpu", kernelFunc: Rue };
var Due = et({ opType: fe.MAX, cpuKernelImpl: UB });
var jV = { kernelName: Bn, backendName: "webgpu", kernelFunc: Due };
function Aue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = C.computePool2DInfo(n.shape, s, a, u, i, p);
return ox(n, c, "max", t10);
}
var XV = { kernelName: zn, backendName: "webgpu", kernelFunc: Aue };
function Fue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = C.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new wu(l, "max"), d = [{ type: "int32", data: [l.strideDepth, l.strideHeight, l.strideWidth] }, { type: "int32", data: [l.padInfo.front, l.padInfo.top, l.padInfo.left] }, { type: "int32", data: [l.inDepth, l.inHeight, l.inWidth] }, { type: "int32", data: [l.effectiveFilterDepth, l.effectiveFilterHeight, l.effectiveFilterWidth] }];
return t10.runWebGPUProgram(m, [n], n.dtype, d);
}
var YV = { kernelName: ra, backendName: "webgpu", kernelFunc: Fue };
var Gx = class {
constructor(e) {
this.variableNames = ["dy", "maxPos"], this.uniforms = `strides : vec2<i32>, pads : vec2<i32>, dilations : vec2<i32>, filterDims : vec2<i32>,
outHeight : i32, outWidth : i32`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "maxPool2DBackprop";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d = coords[3];
let dyRCCorner = vec2<i32>(coords.yz) - uniforms.pads;
let dyRCorner = dyRCCorner.x;
let 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.
var dotProd = 0.0;
let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1;
for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) {
let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);
if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
continue;
}
let idyR = i32(dyR);
for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) {
let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);
if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
continue;
}
let idyC = i32(dyC);
let dyValue = getDy(batch, idyR, idyC, d);
let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
let curPosValue = wR * uniforms.filterDims[1] + wC;
let mask = select(0.0, 1.0, maxPosValue == curPosValue);
dotProd += dyValue * mask;
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
var Hx = class {
constructor(e) {
this.variableNames = ["dy", "maxPos"], this.uniforms = `strides : vec3<i32>, pads : vec3<i32>, filterDims : vec3<i32>,
outDepth : i32, outHeight : i32, outWidth : i32`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "maxPool3DBackprop";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords.x;
let ch = coords.u;
let dyCorner = vec3<i32>(coords.y, coords.z, coords.w) - uniforms.pads;
let dyDCorner = dyCorner.x;
let dyRCorner = dyCorner.y;
let 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.
var dotProd = 0.0;
let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1;
for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {
let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);
if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {
continue;
}
let idyD = i32(dyD);
for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {
let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);
if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
continue;
}
let idyR = i32(dyR);
for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {
let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);
if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
continue;
}
let idyC = i32(dyC);
let dyValue = getDy(batch, idyD, idyR, idyC, ch);
let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC;
let mask = select(0.0, 1.0, maxPosValue == curPosValue);
dotProd += dyValue * mask;
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`;
}
};
function Pue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = C.computePool3DInfo(a.shape, i, p, l, u, c), d = new wu(m, "max", true), f = [{ type: "int32", data: [m.strideDepth, m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.padInfo.front, m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.inDepth, m.inHeight, m.inWidth] }, { type: "int32", data: [m.effectiveFilterDepth, m.effectiveFilterHeight, m.effectiveFilterWidth] }], h = t10.runWebGPUProgram(d, [a], "int32", f), g = new Hx(m);
f = [{ type: "int32", data: [m.strideDepth, m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.effectiveFilterDepth - 1 - m.padInfo.front, m.effectiveFilterHeight - 1 - m.padInfo.top, m.effectiveFilterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.effectiveFilterDepth, m.effectiveFilterHeight, m.effectiveFilterWidth] }, { type: "int32", data: [m.outDepth] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }];
let x = t10.runWebGPUProgram(g, [n, h], a.dtype, f);
return t10.disposeData(h.dataId), x;
}
var QV = { kernelName: Li, backendName: "webgpu", kernelFunc: Pue };
function Oue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s;
cm([s, a], "maxPoolGrad");
let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = C.computePool2DInfo(i.shape, p, u, 1, c, l), d = new Da(m, "max", true), f = [{ type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.inHeight, m.inWidth] }, { type: "int32", data: [m.effectiveFilterHeight, m.effectiveFilterWidth] }], h = t10.runWebGPUProgram(d, [i], "int32", f), g = new Gx(m);
f = [{ type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.effectiveFilterHeight - 1 - m.padInfo.top, m.effectiveFilterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.effectiveFilterHeight, m.effectiveFilterWidth] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }];
let x = t10.runWebGPUProgram(g, [n, h], i.dtype, f);
return t10.disposeData(h.dataId), x;
}
var ZV = { kernelName: Hp, backendName: "webgpu", kernelFunc: Oue };
function Mue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = o, { x: p } = e;
y.assert(p.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);
let u = [1, 1];
y.assert(C.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);
let c = C.computePool2DInfo(p.shape, n, s, u, a), l = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }], m = new Da(c, "max", false), d = t10.runWebGPUProgram(m, [p], p.dtype, l);
m = new Da(c, "max", true, true, i);
let f = t10.runWebGPUProgram(m, [p], "int32", l);
return [d, f];
}
var JV = { kernelName: Bi, backendName: "webgpu", kernelFunc: Mue };
function Lue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
return Yr(n, s, a, "min", t10);
}
var eW = { kernelName: Wn, backendName: "webgpu", kernelFunc: Lue };
var Bue = et({ opType: fe.MIN, cpuKernelImpl: GB });
var tW = { kernelName: Un, backendName: "webgpu", kernelFunc: Bue };
var Kx = class {
constructor(e, t10, o) {
this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = e, t10.map((n, s) => {
this.uniforms += ` pad${s} : vec2<i32>,`;
}), this.offset = o === "reflect" ? 0 : 1, this.shaderKey = `mirrorPad_${o}`;
}
getUserCode() {
let e = this.xShape.length, t10 = this.xShape.map((u, c) => `uniforms.pad${c}[0]`).join(","), o = this.xShape.map((u, c) => `uniforms.pad${c}[0] + uniforms.xShape${e > 1 ? `[${c}]` : ""}`).join(","), n = e === 1 ? "start" : "start[i]", s = e === 1 ? "end" : "end[i]", a = e === 1 ? "outC" : "outC[i]", i = Nt(e), p = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
return `
${K("index")} {
if (index < uniforms.size) {
let start = ${i}(${t10});
let end = ${i}(${o});
var outC = getCoordsFromIndex(index);
for (var i = 0; i < ${e}; i = i + 1) {
if (${a} < ${n}) {
${a} = ${n} * 2 - ${a} - ${this.offset};
} else if(${a} >= ${s}) {
${a} = (${s} - 1) * 2 - ${a} + ${this.offset};
}
}
let coords = outC - start;
setOutputAtIndex(index, getX(${p}));
}
}
`;
}
};
var rW = { kernelName: Gn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
let { x: o } = r, { paddings: n, mode: s } = e, a = t10, i = n.map((c) => ({ type: "int32", data: [c[0], c[1]] })), p = new Kx(o.shape, n, s);
return a.runWebGPUProgram(p, [o], o.dtype, i);
} };
var zue = et({ opType: fe.MOD });
var oW = { kernelName: Ga, backendName: "webgpu", kernelFunc: zue };
var qx = class {
constructor(e, t10) {
this.variableNames = ["probs"], this.outputShape = [], this.uniforms = "seed : f32, numOutcomes: i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t10], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "multinomial";
}
getUserCode() {
return `
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
fn random (seed : f32, resultUV : vec2<f32>) -> f32 {
let HASHSCALE1 = 443.8975;
let p = resultUV * seed;
var p3 = fract(vec3<f32>(p.xyx) * HASHSCALE1);
p3 = p3 + dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${K("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
let batch = coords[0];
let resUV = vec2<f32>(f32(coords[1]) / f32(uniforms.outShape[1]),
f32(coords[0]) / f32(uniforms.outShape[0]));
let r = random(uniforms.seed, resUV);
var cdf = 0.0;
for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {
cdf = cdf + getProbs(batch, i);
if (r < cdf) {
setOutputAtIndexI32(index, i);
return;
}
}
// If no other event happened, last event happened.
setOutputAtIndexI32(index, uniforms.numOutcomes - 1);
}
}
`;
}
};
var jx = class {
constructor(e) {
this.variableNames = ["logits"], this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = [this.outputShape[0], 1, 1], this.outputShape[1] >= 4096 ? this.workgroupSize = [256, 1, 1] : this.workgroupSize = [64, 1, 1], this.shaderKey = "softmax";
}
getUserCode() {
return `
var<workgroup> buf : array<f32, ${this.workgroupSize[0]}>;
var<workgroup> rowMaxShared : f32;
var<workgroup> rowSumShared : f32;
const blockSize = ${this.workgroupSize[0]};
${K("index")} {
let row = index / blockSize;
let tid = i32(localId.x);
let cols = uniforms.outShape[1];
var threadMax = -3.402823e+38f;
for (var col = tid; col < cols; col += blockSize) {
let value = getLogits(row, col);
threadMax = max(threadMax, value);
}
if (tid < cols) {
buf[tid] = threadMax;
}
workgroupBarrier();
var reduceSize = min(cols, blockSize);
for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {
reduceSize = currSize + (reduceSize & 1);
if (tid < currSize) {
buf[tid] = max(buf[tid], buf[tid + reduceSize]);
}
workgroupBarrier();
}
if (tid == 0) {
rowMaxShared = buf[0];
}
workgroupBarrier();
var threadSum = 0.0;
for (var col = tid; col < cols; col += blockSize) {
let subExp = exp(getLogits(row, col) - rowMaxShared);
threadSum += subExp;
}
buf[tid] = threadSum;
workgroupBarrier();
for (var currSize = blockSize >> 1; currSize > 0; currSize = currSize >> 1) {
if (tid < currSize) {
buf[tid] = buf[tid] + buf[tid + currSize];
}
workgroupBarrier();
}
if (tid == 0) {
rowSumShared = buf[0];
}
workgroupBarrier();
for (var col = tid; col < cols; col += blockSize) {
let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared;
setOutputAtCoords(row, col, value);
}
}
`;
}
};
function Kv(r) {
let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [y.sizeFromShape(n.shape) / n.shape[s], n.shape[s]] } }), i = new jx(a.shape), p = t10.runWebGPUProgram(i, [a], n.dtype), u = pe({ inputs: { x: p }, backend: t10, attrs: { shape: n.shape } });
return t10.disposeData(a.dataId), t10.disposeData(p.dataId), u;
}
var nW = { kernelName: bs, backendName: "webgpu", kernelFunc: Kv };
function Vue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : Kv({ inputs: { logits: n }, backend: t10, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new qx(u, s), m = [{ type: "float32", data: [a] }, { type: "int32", data: [c] }], d = t10.runWebGPUProgram(l, [p], "int32", m);
return i || t10.disposeData(p.dataId), d;
}
var sW = { kernelName: Hn, backendName: "webgpu", kernelFunc: Vue };
function Wue(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
if (t10.shouldExecuteOnCPU([o])) {
let s = t10.tensorMap.get(o.dataId), [a, i] = KB(s.values, o.shape, o.dtype);
return t10.makeTensorInfo(i, o.dtype, a);
}
let n = new Xr(o.shape, Q.NEG);
return t10.runWebGPUProgram(n, [o], o.dtype);
}
var aW = { kernelName: oa, backendName: "webgpu", kernelFunc: Wue };
function Uue(r) {
console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), { selectedIndices: l } = Wt.nonMaxSuppressionV3Impl(u, c, a, i, p);
return t10.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var iW = { kernelName: jn, backendName: "webgpu", kernelFunc: Uue };
function Gue(r) {
console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Wt.nonMaxSuppressionV5Impl(c, l, m, d, f, h);
return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))];
}
var uW = { kernelName: Xn, backendName: "webgpu", kernelFunc: Gue };
var Xx = class {
constructor(e, t10) {
this.variableNames = ["x"], this.uniforms = "onValue : f32, offValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t10], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "onehot";
}
getUserCode() {
return `
${K("index")} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,
f32(i32(round(getX(coords.x))) == coords.y)));
}
}
`;
}
};
function Hue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new Xx(u, a), l = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [u] } }), m = [{ type: "float32", data: [i] }, { type: "float32", data: [p] }], d = t10.runWebGPUProgram(c, [l], s, m);
t10.disposeData(l.dataId);
let f = [...n.shape, a], h = pe({ inputs: { x: d }, backend: t10, attrs: { shape: f } });
return t10.disposeData(d.dataId), h;
}
var pW = { kernelName: Yn, backendName: "webgpu", kernelFunc: Hue };
function hm(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
if (o.dtype === "complex64") {
let n = Ci({ inputs: { input: o }, backend: t10 }), s = hm({ inputs: { x: n }, backend: t10 }), a = Ep({ inputs: { input: o }, backend: t10 }), i = hm({ inputs: { x: a }, backend: t10 }), p = fo({ inputs: { real: s, imag: i }, backend: t10 });
return t10.disposeData(n.dataId), t10.disposeData(s.dataId), t10.disposeData(a.dataId), t10.disposeData(i.dataId), p;
} else
return Vt({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t10 });
}
var cW = { kernelName: fa, backendName: "webgpu", kernelFunc: hm };
function lW(r) {
let { inputs: e, backend: t10 } = r, { x: o } = e;
if (o.dtype === "string")
throw new Error("onesLike is not supported under string dtype");
if (o.dtype === "complex64") {
let n = Ci({ inputs: { input: o }, backend: t10 }), s = lW({ inputs: { x: n }, backend: t10 }), a = Ep({ inputs: { input: o }, backend: t10 }), i = hm({ inputs: { x: a }, backend: t10 }), p = fo({ inputs: { real: s, imag: i }, backend: t10 });
return t10.disposeData(n.dataId), t10.disposeData(s.dataId), t10.disposeData(a.dataId), t10.disposeData(i.dataId), p;
} else
return Vt({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t10 });
}
var mW = { kernelName: na, backendName: "webgpu", kernelFunc: lW };
function Kue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
if (e.length === 1)
return Ax({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
let s = e[0].shape, a = e[0].dtype;
e.forEach((c) => {
y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
});
let i = [], p = e.map((c) => {
let l = Ax({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
return i.push(l), l;
}), u = Vv({ inputs: p, backend: t10, attrs: { axis: n } });
return i.forEach((c) => t10.disposeData(c.dataId)), u;
}
var dW = { kernelName: sa, backendName: "webgpu", kernelFunc: Kue };
var Yx = class {
constructor(e, t10) {
this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10.map((o, n) => o[0] + e[n] + o[1]), this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), t10.map((o, n) => {
this.uniforms += ` pad${n} : vec2<i32>,`;
}), this.xShape = e, this.shaderKey = "pad";
}
getUserCode() {
let e = this.xShape.length, t10 = Nt(e), o = this.xShape.map((l, m) => `uniforms.pad${m}[0]`).join(","), n = this.xShape.map((l, m) => `uniforms.pad${m}[0] + uniforms.xShape${e > 1 ? `[${m}]` : ""}`).join(","), s = e > 1 ? `${t10}(${o})` : `${o}`, a = e > 1 ? `${t10}(${n})` : `${n}`, i = e > 1 ? "any(outC < start)" : "outC < start", p = e > 1 ? "any(outC >= end)" : "outC >= end", u = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
return `
${K("index")} {
if (index < uniforms.size) {
let start = ${s};
let end = ${a};
let outC = getCoordsFromIndex(index);
if (${i} || ${p}) {
setOutputAtIndex(index, uniforms.constantValue);
} else {
let coords = outC - start;
setOutputAtIndex(index, getX(${u}));
}
}
}
`;
}
};
var qv = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o;
if (s.every((u) => y.arraysEqual(u, [0, 0])))
return At({ inputs: { x: n }, backend: t10 });
if (y.sizeFromShape(n.shape) === 0) {
let u = s.map((c, l) => c[0] + n.shape[l] + c[1]);
return Vt({ backend: t10, attrs: { shape: u, value: a, dtype: n.dtype } });
}
let i = [{ type: "float32", data: [a] }];
s.map((u) => i.push({ type: "int32", data: [u[0], u[1]] }));
let p = new Yx(n.shape, s);
return t10.runWebGPUProgram(p, [n], n.dtype, i);
};
var fW = { kernelName: Qn, backendName: "webgpu", kernelFunc: qv };
var que = et({ opType: fe.POW });
var hW = { kernelName: Zn, backendName: "webgpu", kernelFunc: que };
function jue(r) {
let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = new bi(fe.PRELU, o.shape, n.shape);
return t10.runWebGPUProgram(s, [o, n], "float32");
}
var gW = { kernelName: Jn, backendName: "webgpu", kernelFunc: jue };
function Xue(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
return Yr(n, s, a, "prod", t10);
}
var xW = { kernelName: es, backendName: "webgpu", kernelFunc: Xue };
var Yue = (r) => {
let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = XB(o, n, s, a);
return e.makeTensorInfo([i.length], a, i);
};
var yW = { kernelName: aa, backendName: "webgpu", kernelFunc: Yue };
var Que = et({ opType: fe.DIV });
var bW = { kernelName: dn, backendName: "webgpu", kernelFunc: Que };
var Zue = xe({ opType: Q.RECIPROCAL });
var CW = { kernelName: ts, backendName: "webgpu", kernelFunc: Zue };
var Jue = xe({ opType: Q.RELU });
var wW = { kernelName: rs, backendName: "webgpu", kernelFunc: Jue };
var epe = xe({ opType: Q.RELU6 });
var SW = { kernelName: ss, backendName: "webgpu", kernelFunc: epe };
var Qx = class {
constructor(e, t10, o) {
this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t10, o, e[3]], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "resizeBilinear";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC =
(vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
// Compute the four integer indices.
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
let sourceCeilRC = vec2<i32>(
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
let top = topLeft + (topRight - topLeft) * fracRC.y;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
let newValue = top + (bottom - top) * fracRC.x;
setOutputAtIndex(index, newValue);
}
}
`;
}
};
function tpe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, size: a, halfPixelCenters: i } = o, [p, u] = a, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [i ? 0.5 : 0] }], f = new Qx(n.shape, p, u);
return t10.runWebGPUProgram(f, [n], "float32", d);
}
var IW = { kernelName: ns, backendName: "webgpu", kernelFunc: tpe };
var Zx = class {
constructor(e, t10) {
this.variableNames = ["dy"], this.uniforms = `effectiveXSize : vec2<i32>, effectiveYSize : vec2<i32>, heightScale : f32, widthScale : f32,
invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.alignCorners = t10, this.shaderKey = `resizeBilinearBackprop_${t10}`;
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
let b = coords[0];
let d = coords[3];
let r = coords[1];
let c = coords[2];
var accumulator = 0.0;
// Compute bounds for where in dy we will look
let startRLerp = floor(f32(r) * uniforms.invHeightScale);
let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2));
let startCLerp = floor(f32(c) * uniforms.invWidthScale);
let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2));
// Loop over dy
for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {
let dyR = startDyR + dyROffset;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= uniforms.dyShape[1]) {
continue;
}
for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {
let dyC = startDyC + dyCOffset;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= uniforms.dyShape[2]) {
continue;
}
let dxR = f32(dyR) * uniforms.heightScale;
let topDxRIndex = i32(floor(dxR));
let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1)));
let dxRLerp = dxR - f32(topDxRIndex);
let inverseDxRLerp = 1.0 - dxRLerp;
let dxC = f32(dyC) * uniforms.widthScale;
let leftDxCIndex = i32(floor(dxC));
let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1)));
let dxCLerp = dxC - f32(leftDxCIndex);
let 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
setOutputAtIndex(index, accumulator);
}
}
`;
}
};
function rpe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, [, i, p] = n.shape, [, u, c] = s.shape, l = [a && u > 1 ? i - 1 : i, a && c > 1 ? p - 1 : p], m = [a && u > 1 ? u - 1 : u, a && c > 1 ? c - 1 : c], d = l[0] / m[0], f = l[1] / m[1], h = 1 / d, g = 1 / f, x = Math.ceil(h) * 2 + 2, b = Math.ceil(g) * 2 + 2, w = new Zx(n.shape, a), S = [{ type: "int32", data: l }, { type: "int32", data: m }, { type: "float32", data: [d] }, { type: "float32", data: [f] }, { type: "float32", data: [h] }, { type: "float32", data: [g] }, { type: "int32", data: [x] }, { type: "int32", data: [b] }];
return t10.runWebGPUProgram(w, [s], s.dtype, S);
}
var vW = { kernelName: qa, backendName: "webgpu", kernelFunc: rpe };
var Jx = class {
constructor(e, t10, o, n) {
this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, roundBase : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t10, o, e[3]], this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.halfPixelCenters = n, this.shaderKey = `resizeNearest_${n}`;
}
getUserCode() {
let e;
return this.halfPixelCenters ? e = "max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))" : e = "vec2<f32>(rc) * effectiveInputOverOutputRatioRC", `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC = ${e};
// Compute the coordinators of nearest neighbor point.
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
let sourceNearestRC = vec2<i32>(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutputAtIndex(index, newValue);
}
}
`;
}
};
function ope(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [s ? 0.5 : 0] }], f = new Jx(n.shape, p, u, a);
return t10.runWebGPUProgram(f, [n], n.dtype, d);
}
var kW = { kernelName: os, backendName: "webgpu", kernelFunc: ope };
var ey = class {
constructor(e, t10) {
this.variableNames = ["dy"], this.uniforms = `effectiveXSize : vec2<i32>, effectiveYSize : vec2<i32>, invHeightScale : f32, invWidthScale : f32,
winHeight : i32, winWidth : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.alignCorners = t10, this.shaderKey = `resizeNearestNeigborBackprop_${t10}`;
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getOutputCoords();
let b = coords[0];
let d = coords[3];
let r = coords[1];
let c = coords[2];
var accumulator = 0.0;
// Compute bounds for where in dy we will look
let startRLerp = floor(f32(r) * uniforms.invHeightScale);
let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2)));
let startCLerp = floor(f32(c) * uniforms.invWidthScale);
let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2)));
// Loop over dy
for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {
let dyR = startDyR + dyROffset;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= uniforms.dyShape[1]) {
continue;
}
for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {
let dyC = startDyC + dyCOffset;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= uniforms.dyShape[2]) {
continue;
}
let sourceFracRow = f32(uniforms.effectiveXSize[0]) *
(f32(dyR) / f32(uniforms.effectiveYSize[0]));
let sourceFracCol = f32(uniforms.effectiveXSize[1]) *
(f32(dyC) / f32(uniforms.effectiveYSize[1]));
let sourceNearestRow =
i32(min(f32(uniforms.outShape[1] - 1),
${this.alignCorners ? "floor(sourceFracRow + 0.5)" : "floor(sourceFracRow)"}));
let sourceNearestCol =
i32(min(f32(uniforms.outShape[2] - 1),
${this.alignCorners ? "floor(sourceFracCol + 0.5)" : "floor(sourceFracCol)"}));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutputAtIndex(index, accumulator);
}
}
`;
}
};
function npe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, [, i, p] = n.shape, [, u, c] = s.shape, l = [a && u > 1 ? i - 1 : i, a && c > 1 ? p - 1 : p], m = [a && u > 1 ? u - 1 : u, a && c > 1 ? c - 1 : c], d = l[0] / m[0], f = l[1] / m[1], h = 1 / d, g = 1 / f, x = Math.ceil(h) * 2 + 2, b = Math.ceil(g) * 2 + 2, w = new ey(n.shape, a), S = [{ type: "int32", data: l }, { type: "int32", data: m }, { type: "float32", data: [h] }, { type: "float32", data: [g] }, { type: "int32", data: [x] }, { type: "int32", data: [b] }];
return t10.runWebGPUProgram(w, [s], s.dtype, S);
}
var NW = { kernelName: Ka, backendName: "webgpu", kernelFunc: npe };
var ty = class {
constructor(e) {
this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = " axis : vec4<i32>,", this.shaderKey = "reverse";
}
getUserCode() {
return `
// Using uniform variables as judging conditions, so the function has
// coherent execution within all threads.
fn getReverseCoords(coords : vec4<i32>) -> vec4<i32> {
var reverseCoords = coords;
if (uniforms.axis[0] == 1) {
reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;
}
if (uniforms.axis[1] == 1) {
reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;
}
if (uniforms.axis[2] == 1) {
reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;
}
if (uniforms.axis[3] == 1) {
reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;
}
return reverseCoords;
}
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let reverseCoords = getReverseCoords(coords);
setOutputAtIndex(index, getX(reverseCoords[0],
reverseCoords[1], reverseCoords[2], reverseCoords[3]));
}
}
`;
}
};
function spe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length;
if (a === 0)
return At({ inputs: { x: n }, backend: t10 });
let i = n.shape, p = [1, 1, 1, 1];
i.forEach((g, x) => {
let b = x + 4 - a;
p[b] = g;
});
let u = y.parseAxisParam(s, n.shape), c = [0, 0, 0, 0];
u.forEach((g) => {
let x = g + 4 - a;
c[x] = 1;
});
let l = [{ type: "int32", data: c }], m = pe({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), d = new ty(p), f = t10.runWebGPUProgram(d, [m], m.dtype, l);
t10.disposeData(m.dataId);
let h = pe({ inputs: { x: f }, backend: t10, attrs: { shape: i } });
return t10.disposeData(f.dataId), h;
}
var TW = { kernelName: as, backendName: "webgpu", kernelFunc: spe };
var ry = class {
constructor(e, t10) {
this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t10 == "number" ? (this.uniforms += " fillValue : f32,", this.fillSnippet = "var outputValue = uniforms.fillValue;", this.shaderKey += "_float") : (this.uniforms += " fillValue : vec3<f32>,", this.fillSnippet = "var outputValue = uniforms.fillValue[coords[3]];", this.shaderKey += "_vec3");
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
uniforms.sinRadians;
let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
uniforms.cosRadians;
let coordX = i32(round(coordXFloat + uniforms.centerX));
let coordY = i32(round(coordYFloat + uniforms.centerY));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputAtIndex(index, outputValue);
}
}
`;
}
};
var _W = { kernelName: _s, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = new ry(o.shape, s), [u, c] = C.getImageCenter(a, o.shape[1], o.shape[2]), l = [{ type: "float32", data: [u] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(n)] }, { type: "float32", data: [Math.cos(n)] }];
return typeof s == "number" ? l.push({ type: "float32", data: [Number.parseFloat(s.toFixed(2))] }) : l.push({ type: "float32", data: s }), i.runWebGPUProgram(p, [o], o.dtype, l);
} };
var ape = xe({ opType: Q.ROUND });
var $W = { kernelName: is, backendName: "webgpu", kernelFunc: ape };
var ipe = xe({ opType: Q.RSQRT, cpuKernelImpl: YB });
var EW = { kernelName: us, backendName: "webgpu", kernelFunc: ipe };
var Aa = class {
constructor(e, t10, o, n, s, a, i, p = true) {
this.variableNames = ["updates", "indices"], this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.sumDupeIndices = p, this.dispatchLayout = Z(e), this.dispatch = q(this.dispatchLayout, e, this.workgroupSize), this.sliceDimGreaterThanOne = t10 > 1, this.shaderKey = `scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;
let u = Nt(s.length);
this.uniforms = `sliceDim : i32, strides: ${u}, updatesSize: i32,`, this.updatesRank = n, this.indicesRank = o;
}
getUserCode() {
let e = "";
this.indicesRank === 1 ? e = "coords[0]" : this.indicesRank === 2 && (e = "coords[0], j");
let t10 = `getIndices(${e})`, o = this.sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides", n = "", s = "";
this.dispatchLayout.x.length === 1 ? (n = "flattenedIndex", s = `
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
return index;
}
`) : this.dispatchLayout.x.length === 2 && (n = "vec2<i32>(flattenedIndex, coords[1])", s = `
fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
// N.B. |updates| could be a scalar tensor, conceptually representing a
// 2D tensor with all values equal to that. By design, its size must be
// the same as |outShape[1]| in one dimension, and |indicesShape[0]|
// gives the other.
let sliceSize = uniforms.outShape[1];
let d0 = index / sliceSize;
let d1 = index - d0 * sliceSize;
return vec2<i32>(d0, d1);
}
`);
let i = `getUpdates(${Array.from({ length: this.updatesRank }, (u, c) => `coords[${c}]`).join(", ")})`;
return `
${s}
${K("index")} {
if (index < uniforms.updatesSize) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t10}));
flattenedIndex = flattenedIndex + indexInside * ${o};
}
let updateValue =
${kp(this.type)}(${i});
let flatIndex = getOutputIndexFromCoords(${n});
${this.sumDupeIndices ? Bs("&result[flatIndex]", "updateValue", this.type) : "atomicStore(&result[flatIndex], bitcast<i32>(updateValue));"}
}
}`;
}
};
function upe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = C.calculateShapes(s, n, a), m = [l / u, u];
if (l === 0)
return t10.makeTensorInfo(a, n.dtype);
let d = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [p, i] } }), f = pe({ inputs: { x: s }, backend: t10, attrs: { shape: [p, u] } }), h = f.dtype, g = Vt({ backend: t10, attrs: { shape: m, value: 0, dtype: h } }), x = y.sizeFromShape(f.shape), b = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [x] }], w = new Aa(f.shape, i, d.shape.length, f.shape.length, c, m, h), S = t10.runWebGPUProgram(w, [f, d], h, b, g), k = pe({ inputs: { x: S }, backend: t10, attrs: { shape: a } });
return t10.disposeData(d.dataId), t10.disposeData(f.dataId), t10.disposeData(S.dataId), k;
}
var RW = { kernelName: ps, backendName: "webgpu", kernelFunc: upe };
var oy = class {
constructor(e, t10) {
this.outputShape = [], this.variableNames = ["sortedSequence", "values"], this.uniforms = "numInputs : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.side = t10, this.shaderKey = `search_sorted_${t10}`;
}
getUserCode() {
return `
fn findBound(batch: i32, value: f32) -> i32 {
var left = i32(0);
var right = uniforms.numInputs;
while (left < right) {
var mid = (left + right) / 2;
if (getSortedSequence(batch, mid) ${this.side === "left" ? "<" : "<="} value) {
left = mid + 1;
} else {
right = mid;
}
}
return right;
}
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let value = getValuesByOutputIndex(index);
setOutputAtIndexI32(index, findBound(coords[0], value));
}
}
`;
}
};
function ppe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new oy([s.shape[0], s.shape[1]], a), p = [{ type: "int32", data: [n.shape[1]] }];
return t10.runWebGPUProgram(i, [n, s], "int32", p);
}
var DW = { kernelName: ls, backendName: "webgpu", kernelFunc: ppe };
var ny = class {
constructor(e, t10, o) {
this.variableNames = ["c", "a", "b"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.cRank = e, this.rank = o, this.shaderKey = "select";
}
getUserCode() {
let e, t10;
if (this.rank > 4)
throw Error(`Where for rank ${this.rank} is not yet supported`);
if (this.rank === 1)
t10 = "resRC", e = "resRC";
else {
let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [], a = [];
for (let i = 0; i < this.outputShape.length; i++)
a.push(`${n[i]}`), i < this.cRank && s.push(`${n[i]}`);
e = s.join(), t10 = a.join();
}
return `
${K("index")} {
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputAtIndex(index, getA(${t10}));
} else {
setOutputAtIndex(index, getB(${t10}));
}
}
}
`;
}
};
function cpe(r) {
let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = new ny(o.shape.length, n.shape, n.shape.length);
return t10.runWebGPUProgram(a, [o, n, s], dt(n.dtype, s.dtype));
}
var AW = { kernelName: ua, backendName: "webgpu", kernelFunc: cpe };
var lpe = xe({ opType: Q.SELU });
var FW = { kernelName: ms, backendName: "webgpu", kernelFunc: lpe };
var mpe = xe({ opType: Q.SIGMOID });
var PW = { kernelName: hs, backendName: "webgpu", kernelFunc: mpe };
var dpe = xe({ opType: Q.SIGN });
var OW = { kernelName: fs, backendName: "webgpu", kernelFunc: dpe };
var fpe = xe({ opType: Q.SIN });
var MW = { kernelName: ds, backendName: "webgpu", kernelFunc: fpe };
var hpe = xe({ opType: Q.SINH });
var LW = { kernelName: ja, backendName: "webgpu", kernelFunc: hpe };
var gpe = xe({ opType: Q.SOFTPLUS });
var BW = { kernelName: gs, backendName: "webgpu", kernelFunc: gpe };
var xpe = (r) => {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o;
y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");
let i = s.reduce((x, b) => x * b), p = [[0, 0]];
p.push(...a);
for (let x = 1 + s.length; x < n.shape.length; ++x)
p.push([0, 0]);
let u = [], c = qv({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), l = C.getReshaped(c.shape, s, i, false), m = C.getPermuted(l.length, s.length, false), d = C.getReshapedPermuted(c.shape, s, i, false), f = pe({ inputs: { x: c }, backend: t10, attrs: { shape: l } }), h = rr({ inputs: { x: f }, backend: t10, attrs: { perm: m } }), g = pe({ inputs: { x: h }, backend: t10, attrs: { shape: d } });
return u.push(c), u.push(f), u.push(h), u.forEach((x) => t10.disposeData(x.dataId)), g;
};
var zW = { kernelName: ca, backendName: "webgpu", kernelFunc: xpe };
var sy = class {
constructor(e, t10) {
this.variableNames = ["A"], this.workgroupSize = [64, 1, 1], this.size = true;
let o = new Array(e.length);
for (let n = 0; n < o.length; n++)
o[n] = e[n] * t10[n];
this.outputShape = o, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile";
}
getUserCode() {
let e = ype(this.rank, "uniforms.");
return `
${K("index")} {
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`;
}
};
function ype(r, e = "") {
if (r >= 5)
throw Error(`Tile for rank ${r} is not yet supported`);
if (r === 1)
return `(resRC % ${e}aShape)`;
let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = [];
for (let n = 0; n < r; n++)
o.push(`(${t10[n]} % ${e}aShape[${n}])`);
return o.join();
}
function gm(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o;
if (t10.shouldExecuteOnCPU([n]) || n.dtype === "string" || n.shape.length >= 5) {
let p = t10.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = me(n.shape, n.dtype, u), l = oz(c, s);
return t10.makeTensorInfo(l.shape, l.dtype, l.values);
}
let a = new sy(n.shape, s);
return t10.runWebGPUProgram(a, [n], n.dtype);
}
var VW = { kernelName: so, backendName: "webgpu", kernelFunc: gm };
function bpe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = C.calculateShapes(s, n, i), d = false;
if (s.dtype === "string") {
let R = t10.bufferSync(n), D = t10.bufferSync(s), F = y.decodeString(t10.readSync(a.dataId)[0]), O = QB(R, D, i, m, c, u, p, l, F, d);
return t10.makeTensorInfo(i, O.dtype, O.values);
}
let f = [m / c, c], h = pe({ inputs: { x: n }, backend: t10, attrs: { shape: [u, p] } }), g = s.shape.length ? pe({ inputs: { x: s }, backend: t10, attrs: { shape: [u, c] } }) : At({ inputs: { x: s }, backend: t10 }), x = g.dtype, b = t10.makeTensorInfo([], x, y.makeZerosTypedArray(1, x)), w = pe({ inputs: { x: a }, backend: t10, attrs: { shape: Array(f.length).fill(1) } }), S = gm({ inputs: { x: w }, backend: t10, attrs: { reps: f } }), k = y.sizeFromShape([u, c]), _ = [{ type: "int32", data: [p] }, { type: "int32", data: l }, { type: "int32", data: [k] }];
switch (u) {
case 0:
break;
case 1:
{
let R = new Aa([u, c], p, h.shape.length, g.shape.length, l, f, x, d);
t10.runWebGPUProgram(R, [g, h], x, _, S);
}
break;
default:
{
let R = new Aa([u, c], p, h.shape.length, b.shape.length, l, f, x, d);
t10.runWebGPUProgram(R, [b, h], x, _, S);
}
{
let R = new Aa([u, c], p, h.shape.length, g.shape.length, l, f, x);
t10.runWebGPUProgram(R, [g, h], x, _, S);
}
}
let E = pe({ inputs: { x: S }, backend: t10, attrs: { shape: i } });
return t10.disposeData(h.dataId), t10.disposeData(g.dataId), t10.disposeData(w.dataId), t10.disposeData(b.dataId), t10.disposeData(S.dataId), E;
}
var WW = { kernelName: Cs, backendName: "webgpu", kernelFunc: bpe };
function Cpe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = C.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice();
return p.map((m) => {
let d = [...l];
d[i] = m;
let f = zs({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: d } });
return c[i] += m, f;
});
}
var UW = { kernelName: la, backendName: "webgpu", kernelFunc: Cpe };
var wpe = xe({ opType: Q.SQRT });
var GW = { kernelName: xs, backendName: "webgpu", kernelFunc: wpe };
var HW = { kernelName: Gi, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => {
let { x: t10 } = r, o = e, n = new Xr(t10.shape, Q.SQUARE);
return o.runWebGPUProgram(n, [t10], t10.dtype);
} };
var Spe = et({ opType: fe.SQUARED_DIFFERENCE });
var KW = { kernelName: ws, backendName: "webgpu", kernelFunc: Spe };
function Ipe({ inputs: r, attrs: e, backend: t10 }) {
let { x: o } = r, n = new Xr(o.shape, Q.STEP, "stepAlpha : f32,"), s = [{ type: "float32", data: [e.alpha] }];
return t10.runWebGPUProgram(n, [o], o.dtype, s);
}
var qW = { kernelName: yo, backendName: "webgpu", kernelFunc: Ipe };
var ay = class {
constructor(e) {
this.variableNames = ["x"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]);
let t10 = Nt(this.outputShape.length);
this.uniforms = `begin : ${t10}, strides : ${t10}, `, this.shaderKey = "stridedSlice";
}
getUserCode() {
let e = this.outputShape.length, t10 = "";
if (e === 1)
t10 = "coords * uniforms.strides + uniforms.begin";
else {
let n = 0;
t10 = this.outputShape.map((s, a) => (n++, this.outputShape.length === 1 ? `coords * uniforms.strides[${a}] + uniforms.begin[${a}]` : `coords[${n - 1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",");
}
return `
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
setOutputAtIndex(index, getX(${t10}));
}
}
`;
}
};
function vpe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: w, strides: S } = ct.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
if (h)
k = pe({ inputs: { x: n }, backend: t10, attrs: { shape: f } });
else if (g || x) {
y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
let _ = ct.computeOutShape(b, w, S), E = zs({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: _ } });
k = pe({ inputs: { x: E }, backend: t10, attrs: { shape: f } }), t10.disposeData(E.dataId);
} else if (t10.shouldExecuteOnCPU([n])) {
let E = t10.readSync(n.dataId), R = me(n.shape, n.dtype, E), D = ez(d, R, S, b);
k = t10.makeTensorInfo(f, n.dtype, D.values);
} else {
let E = new ay(d), R = [{ type: "int32", data: b }, { type: "int32", data: S }], D = t10.runWebGPUProgram(E, [n], n.dtype, R);
k = pe({ inputs: { x: D }, backend: t10, attrs: { shape: f } }), t10.disposeData(D.dataId);
}
return k;
}
var jW = { kernelName: Ss, backendName: "webgpu", kernelFunc: vpe };
function kpe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.readSync(c.dataId), d = t10.readSync(l.dataId), [f, h] = tz(m, d, n, s, a, i, p, u);
return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)];
}
var XW = { kernelName: ma, backendName: "webgpu", kernelFunc: kpe };
var Npe = et({ opType: fe.SUB, cpuKernelImpl: rz, supportsComplex: true });
var YW = { kernelName: Is, backendName: "webgpu", kernelFunc: Npe };
var Tpe = xe({ opType: Q.TAN });
var QW = { kernelName: vs, backendName: "webgpu", kernelFunc: Tpe };
var _pe = xe({ opType: Q.TANH });
var ZW = { kernelName: ks, backendName: "webgpu", kernelFunc: _pe };
function $pe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { tensor: n, indices: s, updates: a } = e, {} = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = C.calculateShapes(a, s, n.shape), m = [l / u, u];
if (l === 0)
return t10.makeTensorInfo(n.shape, s.dtype);
let d = [], f = pe({ inputs: { x: s }, backend: t10, attrs: { shape: [p, i] } });
d.push(f);
let h = pe({ inputs: { x: a }, backend: t10, attrs: { shape: [p, u] } });
d.push(h);
let g = pe({ inputs: { x: n }, backend: t10, attrs: { shape: m } });
d.push(g);
let x = gm({ inputs: { x: g }, backend: t10, attrs: { reps: Array(m.length).fill(1) } }), b = new Aa([p, u], i, f.shape.length, h.shape.length, c, m, n.dtype, false), w = y.sizeFromShape([p, u]), S = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [w] }], k = t10.runWebGPUProgram(b, [h, f], g.dtype, S, x);
d.push(k);
let _ = pe({ inputs: { x: k }, backend: t10, attrs: { shape: n.shape } });
return d.forEach((E) => t10.disposeData(E.dataId)), _;
}
var JW = { kernelName: cs, backendName: "webgpu", kernelFunc: $pe };
var iy = class {
constructor(e) {
this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
dir : i32, inc : i32,`, this.shaderKey = "swap";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let outC = getCoordsFromIndex(index);
let batch = outC[0];
let elemIdx = outC[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.
let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
var i = 0;
if (isFirstInPair) {
i = elemIdx;
} else {
i = elemIdx - uniforms.inc;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.inc;
} else {
i1 = i32(getIndices(batch, i + uniforms.inc));
}
var x0 = f32(0.0);
var x1 = f32(0.0);
if (i0 < uniforms.inputSize) {
x0 = getX(batch, i0);
} else {
x0 = uniforms.negativeInf;
}
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = uniforms.negativeInf;
}
let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) {
// Elements in opposite order of direction
let iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutputAtIndex(index, f32(i0));
} else {
setOutputAtIndex(index, f32(i1));
}
}
}
`;
}
};
var uy = class {
constructor(e) {
this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.size) {
let outC = getCoordsFromIndex(index);
let batch = outC[0];
let elemIdx = outC[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.
var i = 0;
if (elemIdx < uniforms.k) {
i = elemIdx;
} else {
i = elemIdx * 2 - elemIdx % uniforms.k;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.k;
} else {
i1 = i32(getIndices(batch, i + uniforms.k));
}
let x0 = getX(batch, i0);
var x1 = f32(0.0);
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = x0;
}
if (x0 >= x1) {
setOutputAtIndex(index, f32(i0));
} else {
setOutputAtIndex(index, f32(i1));
}
}
}
`;
}
};
function Jc(r, e) {
e !== null && r.disposeData(e.dataId);
}
function eU(r) {
let e = 1;
for (; e < r; )
e *= 2;
return e;
}
function Epe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = n.shape, p = i[i.length - 1];
if (t10.shouldExecuteOnCPU([n])) {
let k = t10.readSync(n.dataId), [_, E] = nz(k, i, n.dtype, s, a);
return [t10.makeTensorInfo(_.shape, _.dtype, _.values), t10.makeTensorInfo(E.shape, E.dtype, E.values)];
}
if (s === 0)
return i[i.length - 1] = 0, [t10.makeTensorInfo(i, n.dtype, []), t10.makeTensorInfo(i, "int32", [])];
if (p === 1)
return [n, Vt({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: t10 })];
let c = y.sizeFromShape(i) / p, l = pe({ inputs: { x: n }, attrs: { shape: [c, p] }, backend: t10 }), m = eU(s), d = eU(p), f = null, h = () => f === null ? [l, l] : [l, f], g = (k, _, E) => {
let R = h(), D = new iy(E), O = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [_] }], M = f;
f = t10.runWebGPUProgram(D, R, "int32", O), Jc(t10, M);
};
for (let k = 1; k < m; k *= 2) {
let _ = k * 2;
for (let E = k; E >= 1; E /= 2)
g(_, E, [c, d]);
}
for (let k = d; k > m; k /= 2) {
let _ = h(), E = new uy([c, k / 2]), D = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [m] }], F = f;
f = t10.runWebGPUProgram(E, _, "int32", D), Jc(t10, F);
let O = m / 2, M = O * 2;
for (let L = O; L >= 1; L /= 2)
g(M, L, f.shape);
}
let x = f;
f = zs({ inputs: { x: f }, backend: t10, attrs: { begin: 0, size: [c, s] } }), Jc(t10, x);
let b = Hv({ inputs: { x: l, indices: f }, backend: t10, attrs: { axis: 1, batchDims: 1 } });
Jc(t10, l);
let w = i.slice(0, -1);
w.push(s), x = f, f = pe({ inputs: { x: f }, attrs: { shape: w }, backend: t10 }), Jc(t10, x);
let S = b;
return b = pe({ inputs: { x: b }, attrs: { shape: w }, backend: t10 }), Jc(t10, S), [b, f];
}
var tU = { kernelName: Ns, backendName: "webgpu", kernelFunc: Epe };
var py = class {
constructor(e) {
this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Z(this.outputShape), this.dispatch = q(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "transform";
}
getUserCode() {
return `
fn mapCoord(outCoord : f32, len : f32) -> f32{
var inCoord = outCoord;
if(uniforms.fillModeId == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
inCoord;
}
if (inCoord < -len) {
inCoord = inCoord + sz2;
} else {
inCoord = -inCoord - 1.0;
}
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (uniforms.fillModeId == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (uniforms.fillModeId == 4) {
return clamp(outCoord, 0.0, len - 1.0);
}
return outCoord;
}
fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
channel : i32) -> f32 {
var outputValue : f32;
if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = uniforms.fillValue;
}
return outputValue;
}
${K("index")} {
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
var outputValue : f32;
let batch = coords[0];
let x = coords[2];
let y = coords[1];
let channel = coords[3];
let xf = f32(x);
let yf = f32(y);
let a1 = getTransforms(batch, 0);
let a2 = getTransforms(batch, 1);
let a3 = getTransforms(batch, 2);
let b1 = getTransforms(batch, 3);
let b2 = getTransforms(batch, 4);
let b3 = getTransforms(batch, 5);
let c1 = getTransforms(batch, 6);
let c2 = getTransforms(batch, 7);
let projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = uniforms.fillValue;
} else {
let inX = (a1 * xf + a2 * yf + a3) / projection;
let inY = (b1 * xf + b2 * yf + b3) / projection;
let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
if (uniforms.interpolationModeId == 1) {
let coordY = i32(round(mapY));
let coordX = i32(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
let yFloor = floor(mapY);
let xFloor = floor(mapX);
let yCeil = yFloor + 1.0;
let xCeil = xFloor + 1.0;
let valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
let valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutputAtIndex(index, outputValue);
}
}
`;
}
};
function Rpe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new py(g), b = a === "nearest" ? 1 : 2, w;
switch (i) {
case "constant":
w = 1;
break;
case "reflect":
w = 2;
break;
case "wrap":
w = 3;
break;
case "nearest":
w = 4;
break;
default:
w = 1;
break;
}
let S = [{ type: "int32", data: [b] }, { type: "int32", data: [w] }, { type: "float32", data: [p] }];
return t10.runWebGPUProgram(x, [n, s], "float32", S);
}
var rU = { kernelName: Ts, backendName: "webgpu", kernelFunc: Rpe };
function Dpe(r) {
let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o;
s < 0 && (s += n.shape.length);
let a = n, i = a.shape.length, p = n.shape[s], u = new Array(i - 1), c = 0;
for (let h = 0; h < i; h++)
h !== s && (u[c++] = a.shape[h]);
let l = [], m = new Array(i).fill(0), d = a.shape.slice();
d[s] = 1;
let f = new Array(p);
for (let h = 0; h < f.length; h++) {
m[s] = h;
let g = zs({ inputs: { x: a }, backend: t10, attrs: { begin: m, size: d } }), x = pe({ inputs: { x: g }, backend: t10, attrs: { shape: u } });
f[h] = x, l.push(g);
}
return l.forEach((h) => t10.disposeData(h.dataId)), f;
}
var oU = { kernelName: da, backendName: "webgpu", kernelFunc: Dpe };
var cy = class {
constructor(e, t10, o) {
if (this.outputShape = [], this.variableNames = ["x", "segmentIds"], this.uniforms = "numSegments : i32, xSize: i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = t10, this.dispatchLayout = Z(e), this.dispatch = q(this.dispatchLayout, e, this.workgroupSize), o !== "float32" && o !== "int32")
throw new Error(`UnsortedSegmentSum only supports float32 and int32
types, does not support ${o} type.`);
this.type = o, this.shaderKey = "unsortedSegmentSum";
}
getUserCode() {
return `
${K("index")} {
if (index < uniforms.xSize) {
let coords = getXCoordsFromIndex(index);
let b = coords[0];
let inCol = coords[1];
let segmentId = i32(getSegmentIds(inCol));
if (segmentId >= 0) {
let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments;
let value = getX(b, inCol);
${Bs("&result[flatIndex]", "value", this.type)}
}
}
}
`;
}
};
function Ape(r) {
let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = C.getAxesPermutation([u], i), l = n;
c != null && (l = rr({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), p.push(l), u = C.getInnerMostAxes(1, i)[0]);
let m = C.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = pe({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, d] } });
p.push(f);
let h = n.dtype, g = [f.shape[0], a], x = Vt({ backend: t10, attrs: { shape: g, value: 0, dtype: h } }), b = new cy(f.shape, g, h), w = [{ type: "int32", data: [a] }, { type: "int32", data: [y.sizeFromShape(f.shape)] }], S = t10.runWebGPUProgram(b, [f, s], h, w, x), k = pe({ inputs: { x: S }, backend: t10, attrs: { shape: m } });
p.push(S);
let _ = k;
if (c != null) {
p.push(k);
let E = C.getUndoAxesPermutation(c);
_ = rr({ inputs: { x: _ }, backend: t10, attrs: { perm: E } });
}
return p.forEach((E) => t10.disposeData(E.dataId)), _;
}
var nU = { kernelName: ji, backendName: "webgpu", kernelFunc: Ape };
var Fpe = [IB, az, iz, uz, pz, cz, mz, dz, fz, hz, gz, xz, yz, bz, Cz, Iz, vz, kz, Nz, Tz, $z, Ez, Rz, Pz, Oz, Mz, kB, Bz, Vz, Wz, Uz, Gz, Hz, Kz, qz, jz, Xz, Yz, Jz, eV, tV, rV, nV, sV, oV, aV, iV, uV, pV, mV, dV, fV, hV, gV, xV, yV, bV, CV, wB, wV, vV, SV, IV, kV, NV, TV, _V, $V, EV, RV, vB, DV, zz, AV, FV, PV, OV, MV, LV, BV, VV, zV, WV, UV, GV, KV, qV, wz, jV, XV, ZV, YV, QV, JV, Sz, eW, tW, rW, oW, sW, cV, aW, iW, uW, Dz, pW, mW, dW, fW, hW, gW, xW, yW, Az, bW, CW, wW, SW, SB, IW, vW, kW, NW, TW, _W, $W, EW, RW, DW, AW, FW, PW, OW, MW, LW, _z, qW, jW, XW, nW, BW, zW, WW, UW, GW, HW, KW, YW, lV, QW, ZW, JW, VW, tU, rU, lz, oU, nU, cW];
for (let r of Fpe)
Ya(r);
var sU = "4.5.0";
var Ppe = "4.5.0";
var Ope = "4.5.0";
var Mpe = "4.5.0";
var Lpe = "4.5.0";
var Bpe = "0.0.1-alpha.20";
var zpe = { tfjs: sU, "tfjs-core": sU, "tfjs-converter": Ppe, "tfjs-backend-cpu": Ope, "tfjs-backend-webgl": Mpe, "tfjs-backend-wasm": Lpe, "tfjs-backend-webgpu": Bpe };
// src/util/util.ts
function log(...msg) {
const dt2 = /* @__PURE__ */ new Date();
const ts2 = `${dt2.getHours().toString().padStart(2, "0")}:${dt2.getMinutes().toString().padStart(2, "0")}:${dt2.getSeconds().toString().padStart(2, "0")}.${dt2.getMilliseconds().toString().padStart(3, "0")}`;
if (msg)
console.log(ts2, "Human:", ...msg);
}
function join(folder, file) {
const separator = folder.endsWith("/") ? "" : "/";
const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:");
const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;
if (!path.toLocaleLowerCase().includes(".json"))
throw new Error(`modelpath error: expecting json file: ${path}`);
return path;
}
var now = () => {
if (typeof performance !== "undefined")
return performance.now();
return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString());
};
function validate(defaults, config3, parent = "config", msgs = []) {
for (const key of Object.keys(config3)) {
if (typeof config3[key] === "object") {
validate(defaults[key], config3[key], key, msgs);
} else {
const defined = defaults && typeof defaults[key] !== "undefined";
if (!defined)
msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` });
const same = defaults && typeof defaults[key] === typeof config3[key];
if (defined && !same)
msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] });
}
}
if (config3.debug && parent === "config" && msgs.length > 0)
log("invalid configuration", msgs);
return msgs;
}
function mergeDeep(...objects) {
const isObject = (obj) => obj && typeof obj === "object";
return objects.reduce((prev, obj) => {
Object.keys(obj || {}).forEach((key) => {
const pVal = prev[key];
const oVal = obj[key];
if (Array.isArray(pVal) && Array.isArray(oVal))
prev[key] = pVal.concat(...oVal);
else if (isObject(pVal) && isObject(oVal))
prev[key] = mergeDeep(pVal, oVal);
else
prev[key] = oVal;
});
return prev;
}, {});
}
// src/config.ts
var config = {
backend: "",
modelBasePath: "",
cacheModels: true,
validateModels: true,
wasmPath: "",
wasmPlatformFetch: false,
debug: false,
async: true,
warmup: "full",
cacheSensitivity: 0.7,
skipAllowed: false,
deallocate: false,
flags: {},
softwareKernels: false,
filter: {
enabled: true,
equalization: false,
width: 0,
height: 0,
flip: false,
return: true,
autoBrightness: true,
brightness: 0,
contrast: 0,
sharpness: 0,
blur: 0,
saturation: 0,
hue: 0,
negative: false,
sepia: false,
vintage: false,
kodachrome: false,
technicolor: false,
polaroid: false,
pixelate: 0
},
gesture: {
enabled: true
},
face: {
enabled: true,
detector: {
modelPath: "blazeface.json",
rotation: false,
maxDetected: 1,
skipFrames: 99,
skipTime: 2500,
minConfidence: 0.2,
minSize: 0,
iouThreshold: 0.1,
mask: false,
return: false
},
mesh: {
enabled: true,
modelPath: "facemesh.json",
keepInvalid: false
},
attention: {
enabled: false,
modelPath: "facemesh-attention.json"
},
iris: {
enabled: true,
modelPath: "iris.json"
},
emotion: {
enabled: true,
minConfidence: 0.1,
skipFrames: 99,
skipTime: 1500,
modelPath: "emotion.json"
},
description: {
enabled: true,
modelPath: "faceres.json",
skipFrames: 99,
skipTime: 3e3,
minConfidence: 0.1
},
antispoof: {
enabled: false,
skipFrames: 99,
skipTime: 4e3,
modelPath: "antispoof.json"
},
liveness: {
enabled: false,
skipFrames: 99,
skipTime: 4e3,
modelPath: "liveness.json"
}
},
body: {
enabled: true,
modelPath: "movenet-lightning.json",
maxDetected: -1,
minConfidence: 0.3,
skipFrames: 1,
skipTime: 200
},
hand: {
enabled: true,
rotation: true,
skipFrames: 99,
skipTime: 1e3,
minConfidence: 0.5,
iouThreshold: 0.2,
maxDetected: -1,
landmarks: true,
detector: {
modelPath: "handtrack.json"
},
skeleton: {
modelPath: "handlandmark-lite.json"
}
},
object: {
enabled: false,
modelPath: "centernet.json",
minConfidence: 0.2,
iouThreshold: 0.4,
maxDetected: 10,
skipFrames: 99,
skipTime: 2e3
},
segmentation: {
enabled: false,
modelPath: "rvm.json",
ratio: 0.5,
mode: "default"
}
};
// src/image/imagefxshaders.ts
var vertexIdentity = `
precision highp float;
attribute vec2 pos;
attribute vec2 uv;
varying vec2 vUv;
uniform float flipY;
void main(void) {
vUv = uv;
gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
}
`;
var colorMatrixWithAlpha = `
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
}
`;
var colorMatrixWithoutAlpha = `
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
gl_FragColor.a = c.a;
}
`;
var pixelate = `
precision highp float;
varying vec2 vUv;
uniform vec2 size;
uniform sampler2D texture;
vec2 pixelate(vec2 coord, vec2 size) {
return floor( coord / size ) * size;
}
void main(void) {
gl_FragColor = vec4(0.0);
vec2 coord = pixelate(vUv, size);
gl_FragColor += texture2D(texture, coord);
}
`;
var blur = `
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
void main(void) {
gl_FragColor = vec4(0.0);
gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv )*0.159576912161;
gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;
}
`;
var convolution = `
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
uniform float m[9];
void main(void) {
vec4 c11 = texture2D(texture, vUv - px); // top left
vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
vec4 c22 = texture2D(texture, vUv); // mid center
vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
vec4 c33 = texture2D(texture, vUv + px ); // bottom right
gl_FragColor =
c11 * m[0] + c12 * m[1] + c22 * m[2] +
c21 * m[3] + c22 * m[4] + c23 * m[5] +
c31 * m[6] + c32 * m[7] + c33 * m[8];
gl_FragColor.a = c22.a;
}
`;
// src/image/imagefx.ts
var collect = (source, prefix, collection) => {
const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
source.replace(r, (match2, name) => {
collection[name] = 0;
return match2;
});
};
var GLProgram = class {
constructor(gl2, vertexSource, fragmentSource) {
__publicField(this, "uniform", {});
__publicField(this, "attribute", {});
__publicField(this, "gl");
__publicField(this, "id");
__publicField(this, "compile", (source, type) => {
const shader = this.gl.createShader(type);
if (!shader) {
log("filter: could not create shader");
return null;
}
this.gl.shaderSource(shader, source);
this.gl.compileShader(shader);
if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {
log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`);
return null;
}
return shader;
});
this.gl = gl2;
const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);
const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);
this.id = this.gl.createProgram();
if (!vertexShader || !fragmentShader)
return;
if (!this.id) {
log("filter: could not create webgl program");
return;
}
this.gl.attachShader(this.id, vertexShader);
this.gl.attachShader(this.id, fragmentShader);
this.gl.linkProgram(this.id);
if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {
log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`);
return;
}
this.gl.useProgram(this.id);
collect(vertexSource, "attribute", this.attribute);
for (const a in this.attribute)
this.attribute[a] = this.gl.getAttribLocation(this.id, a);
collect(vertexSource, "uniform", this.uniform);
collect(fragmentSource, "uniform", this.uniform);
for (const u in this.uniform)
this.uniform[u] = this.gl.getUniformLocation(this.id, u);
}
};
function GLImageFilter() {
let drawCount = 0;
let sourceTexture = null;
let lastInChain = false;
let currentFramebufferIndex = -1;
let tempFramebuffers = [null, null];
let filterChain = [];
let vertexBuffer = null;
let currentProgram = null;
const fxcanvas = canvas(100, 100);
const shaderProgramCache = {};
const DRAW = { INTERMEDIATE: 1 };
const gl2 = fxcanvas.getContext("webgl");
if (!gl2) {
log("filter: cannot get webgl context");
return;
}
this.gl = gl2;
function resize(width, height) {
if (width === fxcanvas.width && height === fxcanvas.height)
return;
fxcanvas.width = width;
fxcanvas.height = height;
if (!vertexBuffer) {
const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]);
vertexBuffer = gl2.createBuffer();
gl2.bindBuffer(gl2.ARRAY_BUFFER, vertexBuffer);
gl2.bufferData(gl2.ARRAY_BUFFER, vertices, gl2.STATIC_DRAW);
gl2.pixelStorei(gl2.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
}
gl2.viewport(0, 0, fxcanvas.width, fxcanvas.height);
tempFramebuffers = [null, null];
}
function createFramebufferTexture(width, height) {
const fbo = gl2.createFramebuffer();
gl2.bindFramebuffer(gl2.FRAMEBUFFER, fbo);
const renderbuffer = gl2.createRenderbuffer();
gl2.bindRenderbuffer(gl2.RENDERBUFFER, renderbuffer);
const texture = gl2.createTexture();
gl2.bindTexture(gl2.TEXTURE_2D, texture);
gl2.texImage2D(gl2.TEXTURE_2D, 0, gl2.RGBA, width, height, 0, gl2.RGBA, gl2.UNSIGNED_BYTE, null);
gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MAG_FILTER, gl2.LINEAR);
gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MIN_FILTER, gl2.LINEAR);
gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_S, gl2.CLAMP_TO_EDGE);
gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_T, gl2.CLAMP_TO_EDGE);
gl2.framebufferTexture2D(gl2.FRAMEBUFFER, gl2.COLOR_ATTACHMENT0, gl2.TEXTURE_2D, texture, 0);
gl2.bindTexture(gl2.TEXTURE_2D, null);
gl2.bindFramebuffer(gl2.FRAMEBUFFER, null);
return { fbo, texture };
}
function getTempFramebuffer(index2) {
tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
return tempFramebuffers[index2];
}
function draw(flags = 0) {
if (!currentProgram)
return;
let source = null;
let target = null;
let flipY = false;
if (drawCount === 0)
source = sourceTexture;
else
source = getTempFramebuffer(currentFramebufferIndex).texture || null;
drawCount++;
if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
target = null;
flipY = drawCount % 2 === 0;
} else {
currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
}
gl2.bindTexture(gl2.TEXTURE_2D, source);
gl2.bindFramebuffer(gl2.FRAMEBUFFER, target);
gl2.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1);
gl2.drawArrays(gl2.TRIANGLES, 0, 6);
}
function compileShader(fragmentSource) {
if (shaderProgramCache[fragmentSource]) {
currentProgram = shaderProgramCache[fragmentSource];
gl2.useProgram((currentProgram ? currentProgram.id : null) || null);
return currentProgram;
}
currentProgram = new GLProgram(gl2, vertexIdentity, fragmentSource);
if (!currentProgram) {
log("filter: could not get webgl program");
return null;
}
const floatSize = Float32Array.BYTES_PER_ELEMENT;
const vertSize = 4 * floatSize;
gl2.enableVertexAttribArray(currentProgram.attribute["pos"]);
gl2.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl2.FLOAT, false, vertSize, 0 * floatSize);
gl2.enableVertexAttribArray(currentProgram.attribute["uv"]);
gl2.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl2.FLOAT, false, vertSize, 2 * floatSize);
shaderProgramCache[fragmentSource] = currentProgram;
return currentProgram;
}
const filter = {
colorMatrix: (matrix) => {
const m = new Float32Array(matrix);
m[4] /= 255;
m[9] /= 255;
m[14] /= 255;
m[19] /= 255;
const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
const program = compileShader(shader);
if (!program)
return;
gl2.uniform1fv(program.uniform["m"], m);
draw();
},
brightness: (brightness) => {
const b = (brightness || 0) + 1;
filter.colorMatrix([
b,
0,
0,
0,
0,
0,
b,
0,
0,
0,
0,
0,
b,
0,
0,
0,
0,
0,
1,
0
]);
},
saturation: (amount) => {
const x = (amount || 0) * 2 / 3 + 1;
const y5 = (x - 1) * -0.5;
filter.colorMatrix([
x,
y5,
y5,
0,
0,
y5,
x,
y5,
0,
0,
y5,
y5,
x,
0,
0,
0,
0,
0,
1,
0
]);
},
desaturate: () => {
filter.saturation(-1);
},
contrast: (amount) => {
const v5 = (amount || 0) + 1;
const o = -128 * (v5 - 1);
filter.colorMatrix([
v5,
0,
0,
0,
o,
0,
v5,
0,
0,
o,
0,
0,
v5,
0,
o,
0,
0,
0,
1,
0
]);
},
negative: () => {
filter.contrast(-2);
},
hue: (rotation) => {
rotation = (rotation || 0) / 180 * Math.PI;
const cos = Math.cos(rotation);
const sin = Math.sin(rotation);
const lumR = 0.213;
const lumG = 0.715;
const lumB = 0.072;
filter.colorMatrix([
lumR + cos * (1 - lumR) + sin * -lumR,
lumG + cos * -lumG + sin * -lumG,
lumB + cos * -lumB + sin * (1 - lumB),
0,
0,
lumR + cos * -lumR + sin * 0.143,
lumG + cos * (1 - lumG) + sin * 0.14,
lumB + cos * -lumB + sin * -0.283,
0,
0,
lumR + cos * -lumR + sin * -(1 - lumR),
lumG + cos * -lumG + sin * lumG,
lumB + cos * (1 - lumB) + sin * lumB,
0,
0,
0,
0,
0,
1,
0
]);
},
desaturateLuminance: () => {
filter.colorMatrix([
0.2764723,
0.929708,
0.0938197,
0,
-37.1,
0.2764723,
0.929708,
0.0938197,
0,
-37.1,
0.2764723,
0.929708,
0.0938197,
0,
-37.1,
0,
0,
0,
1,
0
]);
},
sepia: () => {
filter.colorMatrix([
0.393,
0.7689999,
0.18899999,
0,
0,
0.349,
0.6859999,
0.16799999,
0,
0,
0.272,
0.5339999,
0.13099999,
0,
0,
0,
0,
0,
1,
0
]);
},
brownie: () => {
filter.colorMatrix([
0.5997023498159715,
0.34553243048391263,
-0.2708298674538042,
0,
47.43192855600873,
-0.037703249837783157,
0.8609577587992641,
0.15059552388459913,
0,
-36.96841498319127,
0.24113635128153335,
-0.07441037908422492,
0.44972182064877153,
0,
-7.562075277591283,
0,
0,
0,
1,
0
]);
},
vintagePinhole: () => {
filter.colorMatrix([
0.6279345635605994,
0.3202183420819367,
-0.03965408211312453,
0,
9.651285835294123,
0.02578397704808868,
0.6441188644374771,
0.03259127616149294,
0,
7.462829176470591,
0.0466055556782719,
-0.0851232987247891,
0.5241648018700465,
0,
5.159190588235296,
0,
0,
0,
1,
0
]);
},
kodachrome: () => {
filter.colorMatrix([
1.1285582396593525,
-0.3967382283601348,
-0.03992559172921793,
0,
63.72958762196502,
-0.16404339962244616,
1.0835251566291304,
-0.05498805115633132,
0,
24.732407896706203,
-0.16786010706155763,
-0.5603416277695248,
1.6014850761964943,
0,
35.62982807460946,
0,
0,
0,
1,
0
]);
},
technicolor: () => {
filter.colorMatrix([
1.9125277891456083,
-0.8545344976951645,
-0.09155508482755585,
0,
11.793603434377337,
-0.3087833385928097,
1.7658908555458428,
-0.10601743074722245,
0,
-70.35205161461398,
-0.231103377548616,
-0.7501899197440212,
1.847597816108189,
0,
30.950940869491138,
0,
0,
0,
1,
0
]);
},
polaroid: () => {
filter.colorMatrix([
1.438,
-0.062,
-0.062,
0,
0,
-0.122,
1.378,
-0.122,
0,
0,
-0.016,
-0.016,
1.483,
0,
0,
0,
0,
0,
1,
0
]);
},
shiftToBGR: () => {
filter.colorMatrix([
0,
0,
1,
0,
0,
0,
1,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
1,
0
]);
},
convolution: (matrix) => {
const m = new Float32Array(matrix);
const pixelSizeX = 1 / fxcanvas.width;
const pixelSizeY = 1 / fxcanvas.height;
const program = compileShader(convolution);
if (!program)
return;
gl2.uniform1fv(program.uniform["m"], m);
gl2.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
draw();
},
detectEdges: () => {
filter.convolution.call(this, [
0,
1,
0,
1,
-4,
1,
0,
1,
0
]);
},
sobelX: () => {
filter.convolution.call(this, [
-1,
0,
1,
-2,
0,
2,
-1,
0,
1
]);
},
sobelY: () => {
filter.convolution.call(this, [
-1,
-2,
-1,
0,
0,
0,
1,
2,
1
]);
},
sharpen: (amount) => {
const a = amount || 1;
filter.convolution.call(this, [
0,
-1 * a,
0,
-1 * a,
1 + 4 * a,
-1 * a,
0,
-1 * a,
0
]);
},
emboss: (size2) => {
const s = size2 || 1;
filter.convolution.call(this, [
-2 * s,
-1 * s,
0,
-1 * s,
1,
1 * s,
0,
1 * s,
2 * s
]);
},
blur: (size2) => {
const blurSizeX = size2 / 7 / fxcanvas.width;
const blurSizeY = size2 / 7 / fxcanvas.height;
const program = compileShader(blur);
if (!program)
return;
gl2.uniform2f(program.uniform["px"], 0, blurSizeY);
draw(DRAW.INTERMEDIATE);
gl2.uniform2f(program.uniform["px"], blurSizeX, 0);
draw();
},
pixelate: (size2) => {
const blurSizeX = size2 / fxcanvas.width;
const blurSizeY = size2 / fxcanvas.height;
const program = compileShader(pixelate);
if (!program)
return;
gl2.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
draw();
}
};
this.add = function(name) {
const args = Array.prototype.slice.call(arguments, 1);
const func = filter[name];
filterChain.push({ func, args });
};
this.reset = function() {
filterChain = [];
};
this.get = function() {
return filterChain;
};
this.apply = function(image) {
resize(image.width, image.height);
drawCount = 0;
if (!sourceTexture)
sourceTexture = gl2.createTexture();
gl2.bindTexture(gl2.TEXTURE_2D, sourceTexture);
gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_S, gl2.CLAMP_TO_EDGE);
gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_T, gl2.CLAMP_TO_EDGE);
gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MIN_FILTER, gl2.NEAREST);
gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MAG_FILTER, gl2.NEAREST);
gl2.texImage2D(gl2.TEXTURE_2D, 0, gl2.RGBA, gl2.RGBA, gl2.UNSIGNED_BYTE, image);
for (let i = 0; i < filterChain.length; i++) {
lastInChain = i === filterChain.length - 1;
const f = filterChain[i];
f.func.apply(this, f.args || []);
}
return fxcanvas;
};
this.draw = function(image) {
this.add("brightness", 0);
return this.apply(image);
};
}
// src/image/enhance.ts
async function histogramEqualization(inputImage) {
const squeeze = inputImage.shape.length === 4 ? lc(inputImage) : inputImage;
const rgb3 = ai(squeeze, 3, 2);
const min = [vl(rgb3[0]), vl(rgb3[1]), vl(rgb3[2])];
const max = [Ia(rgb3[0]), Ia(rgb3[1]), Ia(rgb3[2])];
const absMax = await Promise.all(max.map((channel) => channel.data()));
const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
const maxRange = maxValue > 1 ? 255 : 1;
const factor = maxRange / maxValue;
let final;
if (factor > 1) {
const sub = [Te(rgb3[0], min[0]), Te(rgb3[1], min[1]), Te(rgb3[2], min[2])];
const range = [Te(max[0], min[0]), Te(max[1], min[1]), Te(max[2], min[2])];
const enh = [se(sub[0], factor), se(sub[1], factor), se(sub[2], factor)];
const stack = vr([enh[0], enh[1], enh[2]], 2);
final = W(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);
Ot([...sub, ...range, ...enh, stack]);
} else {
final = oi(squeeze, 0);
}
Ot([...rgb3, ...min, ...max, rgb3, squeeze, inputImage]);
return final;
}
// src/image/image.ts
var maxSize = 3840;
var inCanvas = null;
var outCanvas = null;
var tmpCanvas = null;
var fx2;
var last = {
inputSum: 0,
cacheDiff: 1,
sumMethod: 0,
inputTensor: void 0
};
function reset() {
last.inputSum = 0;
last.cacheDiff = 1;
last.sumMethod = 0;
last.inputTensor = void 0;
}
function canvas(width, height) {
let c;
if (env.browser) {
if (env.worker) {
if (typeof OffscreenCanvas === "undefined")
throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");
c = new OffscreenCanvas(width, height);
} else {
if (typeof document !== "undefined") {
c = document.createElement("canvas");
c.width = width;
c.height = height;
} else if (typeof navigator !== "undefined" && navigator.product === "ReactNative") {
if (typeof env.Canvas !== "undefined")
c = new env.Canvas(width, height);
else if (typeof globalThis.Canvas !== "undefined")
c = new globalThis.Canvas(width, height);
else
throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed");
} else {
throw new Error("canvas error: attempted to run in browser but DOM is not defined");
}
}
} else {
if (typeof env.Canvas !== "undefined")
c = new env.Canvas(width, height);
else if (typeof globalThis.Canvas !== "undefined")
c = new globalThis.Canvas(width, height);
}
return c;
}
function copy(input, output) {
const outputCanvas = output || canvas(input.width, input.height);
const ctx = outputCanvas.getContext("2d");
ctx.drawImage(input, 0, 0);
return outputCanvas;
}
async function process2(input, config3, getTensor = true) {
var _a2, _b, _c2;
if (!input) {
if (config3.debug)
log("input error: input is missing");
return { tensor: null, canvas: null };
}
if (!(input instanceof pt) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
throw new Error("input error: type not recognized");
}
if (input instanceof pt) {
let tensor2 = null;
if (input["isDisposedInternal"])
throw new Error("input error: attempted to use tensor but it is disposed");
if (!input.shape)
throw new Error("input error: attempted to use tensor without a shape");
if (input.shape.length === 3) {
if (input.shape[2] === 3) {
tensor2 = oi(input, 0);
} else if (input.shape[2] === 4) {
const rgb3 = g1(input, [0, 0, 0], [-1, -1, 3]);
tensor2 = oi(rgb3, 0);
Ot(rgb3);
}
} else if (input.shape.length === 4) {
if (input.shape[3] === 3) {
tensor2 = Vr(input);
} else if (input.shape[3] === 4) {
tensor2 = x1(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
}
}
if (tensor2 == null || tensor2.shape.length !== 4 || tensor2.shape[0] !== 1 || tensor2.shape[3] !== 3)
throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`);
if (tensor2.dtype === "int32") {
const cast = Ye(tensor2, "float32");
Ot(tensor2);
tensor2 = cast;
}
return { tensor: tensor2, canvas: config3.filter.return ? outCanvas : null };
}
if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) {
if (config3.debug)
log("input stream is not ready");
return { tensor: null, canvas: inCanvas };
}
const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0;
const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0;
if (!originalWidth || !originalHeight) {
if (config3.debug)
log("cannot determine input dimensions");
return { tensor: null, canvas: inCanvas };
}
let targetWidth = originalWidth;
let targetHeight = originalHeight;
if (targetWidth > maxSize) {
targetWidth = maxSize;
targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);
}
if (targetHeight > maxSize) {
targetHeight = maxSize;
targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);
}
if ((((_a2 = config3.filter) == null ? void 0 : _a2.width) || 0) > 0)
targetWidth = config3.filter.width;
else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0)
targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight);
if ((config3.filter.height || 0) > 0)
targetHeight = config3.filter.height;
else if ((config3.filter.width || 0) > 0)
targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
if (!targetWidth || !targetHeight)
throw new Error("input error: cannot determine dimension");
if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
inCanvas = canvas(targetWidth, targetHeight);
const inCtx = inCanvas.getContext("2d");
if (typeof ImageData !== "undefined" && input instanceof ImageData) {
inCtx.putImageData(input, 0, 0);
} else {
if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
inCtx.translate(originalWidth, 0);
inCtx.scale(-1, 1);
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
inCtx.setTransform(1, 0, 0, 1, 0, 0);
} else {
inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
}
}
if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
outCanvas = canvas(inCanvas.width, inCanvas.height);
if (config3.filter.enabled && env.webgl.supported) {
if (!fx2)
fx2 = env.browser ? new GLImageFilter() : null;
env.filter = !!fx2;
if (!(fx2 == null ? void 0 : fx2.add)) {
if (config3.debug)
log("input process error: cannot initialize filters");
env.webgl.supported = false;
config3.filter.enabled = false;
copy(inCanvas, outCanvas);
} else {
fx2.reset();
if (config3.filter.brightness !== 0)
fx2.add("brightness", config3.filter.brightness);
if (config3.filter.contrast !== 0)
fx2.add("contrast", config3.filter.contrast);
if (config3.filter.sharpness !== 0)
fx2.add("sharpen", config3.filter.sharpness);
if (config3.filter.blur !== 0)
fx2.add("blur", config3.filter.blur);
if (config3.filter.saturation !== 0)
fx2.add("saturation", config3.filter.saturation);
if (config3.filter.hue !== 0)
fx2.add("hue", config3.filter.hue);
if (config3.filter.negative)
fx2.add("negative");
if (config3.filter.sepia)
fx2.add("sepia");
if (config3.filter.vintage)
fx2.add("brownie");
if (config3.filter.sepia)
fx2.add("sepia");
if (config3.filter.kodachrome)
fx2.add("kodachrome");
if (config3.filter.technicolor)
fx2.add("technicolor");
if (config3.filter.polaroid)
fx2.add("polaroid");
if (config3.filter.pixelate !== 0)
fx2.add("pixelate", config3.filter.pixelate);
if (((_c2 = fx2.get()) == null ? void 0 : _c2.length) > 1)
outCanvas = fx2.apply(inCanvas);
else
outCanvas = fx2.draw(inCanvas);
}
} else {
copy(inCanvas, outCanvas);
if (fx2)
fx2 = null;
env.filter = !!fx2;
}
if (!getTensor)
return { tensor: null, canvas: outCanvas };
if (!outCanvas)
throw new Error("canvas error: cannot create output");
let pixels;
let depth = 3;
if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) {
if (env.browser && MN) {
pixels = MN ? MN.fromPixels(input) : null;
} else {
depth = input.data.length / input.height / input.width;
const arr = new Uint8Array(input.data.buffer);
pixels = ir(arr, [input.height, input.width, depth], "int32");
}
} else {
if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
tmpCanvas = canvas(outCanvas.width, outCanvas.height);
if (MN && env.browser) {
if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
pixels = MN.fromPixels(outCanvas);
} else {
tmpCanvas = copy(outCanvas);
pixels = MN.fromPixels(tmpCanvas);
}
} else {
const tempCanvas = copy(outCanvas);
const tempCtx = tempCanvas.getContext("2d");
const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
depth = tempData.data.length / targetWidth / targetHeight;
const arr = new Uint8Array(tempData.data.buffer);
pixels = ir(arr, [targetWidth, targetHeight, depth]);
}
}
if (depth === 4) {
const rgb3 = g1(pixels, [0, 0, 0], [-1, -1, 3]);
Ot(pixels);
pixels = rgb3;
}
if (!pixels)
throw new Error("input error: cannot create tensor");
const casted = Ye(pixels, "float32");
const tensor = config3.filter.equalization ? await histogramEqualization(casted) : oi(casted, 0);
Ot([pixels, casted]);
if (config3.filter.autoBrightness) {
const max = Ia(tensor);
const maxVal = await max.data();
config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0];
Ot(max);
}
return { tensor, canvas: config3.filter.return ? outCanvas : null };
}
async function skip(config3, input) {
let skipFrame = false;
if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160)
return skipFrame;
if (!last.inputTensor) {
last.inputTensor = Vr(input);
} else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
Ot(last.inputTensor);
last.inputTensor = Vr(input);
} else {
const t10 = {};
t10.diff = Te(input, last.inputTensor);
t10.squared = se(t10.diff, t10.diff);
t10.sum = ot(t10.squared);
const diffSum = await t10.sum.data();
const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
Ot([last.inputTensor, t10.diff, t10.squared, t10.sum]);
last.inputTensor = Vr(input);
skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
}
return skipFrame;
}
async function compare(config3, input1, input2) {
const t10 = {};
if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
if (!config3.debug)
log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
return 0;
}
if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
if (!config3.debug)
log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
return 0;
}
t10.input1 = Vr(input1);
t10.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? uj.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : Vr(input2);
t10.diff = Te(t10.input1, t10.input2);
t10.squared = se(t10.diff, t10.diff);
t10.sum = ot(t10.squared);
const diffSum = await t10.sum.data();
const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
Ot([t10.input1, t10.input2, t10.diff, t10.squared, t10.sum]);
return diffRelative;
}
// src/util/env.ts
var _canvas, _image, _imageData;
var Env = class {
constructor() {
/** Running in Browser */
__publicField(this, "browser");
/** Running in NodeJS */
__publicField(this, "node");
/** Running in WebWorker thread */
__publicField(this, "worker");
/** Detected platform */
__publicField(this, "platform", "");
/** Detected agent */
__publicField(this, "agent", "");
/** List of supported backends */
__publicField(this, "backends", []);
/** Has any work been performed so far */
__publicField(this, "initial");
/** Are image filters supported? */
__publicField(this, "filter");
/** TFJS instance details */
__publicField(this, "tfjs");
/** Is offscreenCanvas supported? */
__publicField(this, "offscreen");
/** Are performance counter instant values or additive */
__publicField(this, "perfadd", false);
/** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */
__publicField(this, "tensorflow", {
version: void 0,
gpu: void 0
});
/** WASM detected capabilities */
__publicField(this, "wasm", {
supported: void 0,
backend: void 0,
simd: void 0,
multithread: void 0
});
/** WebGL detected capabilities */
__publicField(this, "webgl", {
supported: void 0,
backend: void 0,
version: void 0,
renderer: void 0,
shader: void 0,
vendor: void 0
});
/** WebGPU detected capabilities */
__publicField(this, "webgpu", {
supported: void 0,
backend: void 0,
adapter: void 0
});
/** CPU info */
__publicField(this, "cpu", {
model: void 0,
flags: []
});
/** List of supported kernels for current backend */
__publicField(this, "kernels", []);
/** MonkeyPatch for Canvas/Image/ImageData */
__privateAdd(this, _canvas, void 0);
__privateAdd(this, _image, void 0);
__privateAdd(this, _imageData, void 0);
this.browser = typeof navigator !== "undefined";
this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
this.tfjs = { version: zpe["tfjs-core"] };
this.offscreen = typeof OffscreenCanvas !== "undefined";
this.initial = true;
this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
if (typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined") {
const agent = navigator.userAgent || "";
const raw = agent.match(/\(([^()]+)\)/g);
if (raw == null ? void 0 : raw[0]) {
const platformMatch = raw[0].match(/\(([^()]+)\)/g);
this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : "";
this.agent = agent.replace(raw[0], "");
if (this.platform[1])
this.agent = this.agent.replace(raw[1], "");
this.agent = this.agent.replace(/ /g, " ");
}
} else if (typeof process !== "undefined") {
this.platform = `${process.platform} ${process.arch}`;
this.agent = `NodeJS ${process.version}`;
}
}
get Canvas() {
return __privateGet(this, _canvas);
}
set Canvas(val) {
__privateSet(this, _canvas, val);
globalThis.Canvas = val;
}
get Image() {
return __privateGet(this, _image);
}
// @ts-ignore monkey-patch;
set Image(val) {
__privateSet(this, _image, val);
globalThis.Image = val;
}
get ImageData() {
return __privateGet(this, _imageData);
}
// @ts-ignore monkey-patch;
set ImageData(val) {
__privateSet(this, _imageData, val);
globalThis.ImageData = val;
}
/** update backend information */
async updateBackend() {
this.backends = Object.keys(ur().registryFactory);
try {
this.tensorflow = {
version: Tme()["binding"] ? Tme()["binding"].TF_Version : void 0,
gpu: Tme()["binding"] ? Tme()["binding"].isUsingGpuDevice() : void 0
};
} catch (e) {
}
this.wasm.supported = typeof WebAssembly !== "undefined";
this.wasm.backend = this.backends.includes("wasm");
if (this.wasm.supported && this.wasm.backend) {
this.wasm.simd = await P().getAsync("WASM_HAS_SIMD_SUPPORT");
this.wasm.multithread = await P().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
}
const c = canvas(100, 100);
const gl2 = c ? c.getContext("webgl2") : void 0;
this.webgl.supported = typeof gl2 !== "undefined";
this.webgl.backend = this.backends.includes("webgl");
if (this.webgl.supported && this.webgl.backend && gl2) {
this.webgl.version = gl2.getParameter(gl2.VERSION);
this.webgl.vendor = gl2.getParameter(gl2.VENDOR);
this.webgl.renderer = gl2.getParameter(gl2.RENDERER);
this.webgl.shader = gl2.getParameter(gl2.SHADING_LANGUAGE_VERSION);
}
this.webgpu.supported = this.browser && typeof navigator !== "undefined" && typeof navigator.gpu !== "undefined";
this.webgpu.backend = this.backends.includes("webgpu");
try {
if (this.webgpu.supported) {
const adapter = await navigator.gpu.requestAdapter();
this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo());
}
} catch (e) {
this.webgpu.supported = false;
}
try {
this.kernels = Km(Ime()).map((kernel) => kernel.kernelName.toLowerCase());
} catch (e) {
}
}
/** update cpu information */
updateCPU() {
const cpu = { model: "", flags: [] };
if (this.node && this.platform.startsWith("linux")) {
}
if (!this.cpu)
Object.defineProperty(this, "cpu", { value: cpu });
else
this.cpu = cpu;
}
};
_canvas = new WeakMap();
_image = new WeakMap();
_imageData = new WeakMap();
var env = new Env();
// src/util/webcam.ts
var WebCam = class {
constructor() {
// eslint-disable-line @typescript-eslint/no-extraneous-class
/** current webcam configuration */
__publicField(this, "config");
/** instance of dom element associated with webcam stream */
__publicField(this, "element");
/** active webcam stream */
__publicField(this, "stream");
/** enumerated video devices */
__publicField(this, "devices", []);
__publicField(this, "enumerate", async () => {
try {
const devices = await navigator.mediaDevices.enumerateDevices();
this.devices = devices.filter((device) => device.kind === "videoinput");
} catch (e) {
this.devices = [];
}
return this.devices;
});
/** start method initializizes webcam stream and associates it with a dom video element */
__publicField(this, "start", async (webcamConfig) => {
var _a2, _b;
if (webcamConfig == null ? void 0 : webcamConfig.debug)
this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug;
if (webcamConfig == null ? void 0 : webcamConfig.crop)
this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop;
if (webcamConfig == null ? void 0 : webcamConfig.mode)
this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode;
if (webcamConfig == null ? void 0 : webcamConfig.width)
this.config.width = webcamConfig == null ? void 0 : webcamConfig.width;
if (webcamConfig == null ? void 0 : webcamConfig.height)
this.config.height = webcamConfig == null ? void 0 : webcamConfig.height;
if (webcamConfig == null ? void 0 : webcamConfig.id)
this.config.id = webcamConfig == null ? void 0 : webcamConfig.id;
if (webcamConfig == null ? void 0 : webcamConfig.element) {
if (typeof webcamConfig.element === "string") {
const el = document.getElementById(webcamConfig.element);
if (el && el instanceof HTMLVideoElement) {
this.element = el;
} else {
if (this.config.debug)
log("webcam", "cannot get dom element", webcamConfig.element);
return;
}
} else if (webcamConfig.element instanceof HTMLVideoElement) {
this.element = webcamConfig.element;
} else {
if (this.config.debug)
log("webcam", "unknown dom element", webcamConfig.element);
return;
}
} else {
this.element = document.createElement("video");
}
const requestedConstraints = {
audio: false,
video: {
facingMode: this.config.mode === "front" ? "user" : "environment",
// @ts-ignore // resizeMode is still not defined in tslib
resizeMode: this.config.crop ? "crop-and-scale" : "none"
}
};
if (((_a2 = this.config) == null ? void 0 : _a2.width) > 0)
requestedConstraints.video.width = { ideal: this.config.width };
if (((_b = this.config) == null ? void 0 : _b.height) > 0)
requestedConstraints.video.height = { ideal: this.config.height };
if (this.config.id)
requestedConstraints.video.deviceId = this.config.id;
this.element.addEventListener("play", () => {
if (this.config.debug)
log("webcam", "play");
});
this.element.addEventListener("pause", () => {
if (this.config.debug)
log("webcam", "pause");
});
this.element.addEventListener("click", async () => {
if (!this.element || !this.stream)
return;
if (this.element.paused)
await this.element.play();
else
this.element.pause();
});
if (!(navigator == null ? void 0 : navigator.mediaDevices)) {
if (this.config.debug)
log("webcam", "no devices");
return;
}
try {
this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints);
} catch (err) {
log("webcam", err);
return;
}
if (!this.stream) {
if (this.config.debug)
log("webcam", "no stream");
return;
}
this.element.srcObject = this.stream;
const ready = new Promise((resolve) => {
if (!this.element)
resolve(false);
else
this.element.onloadeddata = () => resolve(true);
});
await ready;
await this.element.play();
if (this.config.debug) {
log("webcam", {
width: this.width,
height: this.height,
label: this.label,
stream: this.stream,
track: this.track,
settings: this.settings,
constraints: this.constraints,
capabilities: this.capabilities
});
}
});
/** pause webcam video method */
__publicField(this, "pause", () => {
if (this.element)
this.element.pause();
});
/** play webcam video method */
__publicField(this, "play", async () => {
if (this.element)
await this.element.play();
});
/** stop method stops active webcam stream track and disconnects webcam */
__publicField(this, "stop", () => {
if (this.config.debug)
log("webcam", "stop");
if (this.track)
this.track.stop();
});
this.config = {
element: void 0,
debug: true,
mode: "front",
crop: false,
width: 0,
height: 0
};
}
/** get active webcam stream track */
get track() {
if (!this.stream)
return void 0;
return this.stream.getVideoTracks()[0];
}
/** get webcam capabilities */
get capabilities() {
if (!this.track)
return void 0;
return this.track.getCapabilities ? this.track.getCapabilities() : void 0;
}
/** get webcam constraints */
get constraints() {
if (!this.track)
return void 0;
return this.track.getConstraints ? this.track.getConstraints() : void 0;
}
/** get webcam settings */
get settings() {
if (!this.stream)
return void 0;
const track = this.stream.getVideoTracks()[0];
return track.getSettings ? track.getSettings() : void 0;
}
/** get webcam label */
get label() {
if (!this.track)
return "";
return this.track.label;
}
/** is webcam paused */
get paused() {
var _a2;
return ((_a2 = this.element) == null ? void 0 : _a2.paused) || false;
}
/** webcam current width */
get width() {
var _a2;
return ((_a2 = this.element) == null ? void 0 : _a2.videoWidth) || 0;
}
/** webcam current height */
get height() {
var _a2;
return ((_a2 = this.element) == null ? void 0 : _a2.videoHeight) || 0;
}
};
// models/models.json
var models_exports = {};
__export(models_exports, {
"affectnet-mobilenet": () => affectnet_mobilenet,
age: () => age,
"anti-spoofing": () => anti_spoofing,
antispoof: () => antispoof,
blazeface: () => blazeface,
"blazeface-back": () => blazeface_back,
"blazeface-front": () => blazeface_front,
"blazepose-detector": () => blazepose_detector,
"blazepose-full": () => blazepose_full,
"blazepose-heavy": () => blazepose_heavy,
"blazepose-lite": () => blazepose_lite,
centernet: () => centernet,
default: () => models_default,
efficientpose: () => efficientpose,
"efficientpose-i-lite": () => efficientpose_i_lite,
"efficientpose-ii-lite": () => efficientpose_ii_lite,
"efficientpose-iv": () => efficientpose_iv,
emotion: () => emotion,
faceboxes: () => faceboxes,
facemesh: () => facemesh,
"facemesh-attention": () => facemesh_attention,
"facemesh-attention-pinto": () => facemesh_attention_pinto,
"facemesh-detection-full": () => facemesh_detection_full,
"facemesh-detection-short": () => facemesh_detection_short,
faceres: () => faceres,
"faceres-deep": () => faceres_deep,
gear: () => gear,
"gear-e1": () => gear_e1,
"gear-e2": () => gear_e2,
gender: () => gender,
"gender-ssrnet-imdb": () => gender_ssrnet_imdb,
handdetect: () => handdetect,
"handlandmark-full": () => handlandmark_full,
"handlandmark-lite": () => handlandmark_lite,
"handlandmark-sparse": () => handlandmark_sparse,
handskeleton: () => handskeleton,
handtrack: () => handtrack,
"insightface-efficientnet-b0": () => insightface_efficientnet_b0,
"insightface-ghostnet-strides1": () => insightface_ghostnet_strides1,
"insightface-ghostnet-strides2": () => insightface_ghostnet_strides2,
"insightface-mobilenet-emore": () => insightface_mobilenet_emore,
"insightface-mobilenet-swish": () => insightface_mobilenet_swish,
iris: () => iris,
liveness: () => liveness,
meet: () => meet,
mobileface: () => mobileface,
mobilefacenet: () => mobilefacenet,
models: () => models,
"movenet-lightning": () => movenet_lightning,
"movenet-multipose": () => movenet_multipose,
"movenet-thunder": () => movenet_thunder,
nanodet: () => nanodet,
"nanodet-e": () => nanodet_e,
"nanodet-g": () => nanodet_g,
"nanodet-m": () => nanodet_m,
"nanodet-t": () => nanodet_t,
posenet: () => posenet,
rvm: () => rvm,
selfie: () => selfie
});
var antispoof = 853098;
var blazeface = 538928;
var centernet = 4030290;
var emotion = 820516;
var facemesh = 1477958;
var faceres = 6978814;
var handlandmark_lite = 2023432;
var handtrack = 2964837;
var iris = 2599092;
var liveness = 592976;
var models = 0;
var movenet_lightning = 4650216;
var affectnet_mobilenet = 6920630;
var age = 161240;
var blazeface_back = 538928;
var blazeface_front = 402048;
var blazepose_detector = 5928856;
var blazepose_full = 6339202;
var blazepose_heavy = 27502466;
var blazepose_lite = 2726402;
var efficientpose = 5651240;
var faceboxes = 2013002;
var facemesh_attention_pinto = 2387598;
var facemesh_attention = 2382414;
var facemesh_detection_full = 1026192;
var facemesh_detection_short = 201268;
var faceres_deep = 13957620;
var gear_e1 = 112438;
var gear_e2 = 112438;
var gear = 1498916;
var gender_ssrnet_imdb = 161236;
var gender = 201808;
var handdetect = 3515612;
var handlandmark_full = 5431368;
var handlandmark_sparse = 5286322;
var handskeleton = 5502280;
var meet = 372228;
var mobileface = 2183192;
var mobilefacenet = 5171976;
var movenet_multipose = 9448838;
var movenet_thunder = 12477112;
var nanodet = 7574558;
var posenet = 5032780;
var rvm = 3739355;
var selfie = 212886;
var anti_spoofing = 853098;
var efficientpose_i_lite = 2269064;
var efficientpose_ii_lite = 5651240;
var efficientpose_iv = 25643252;
var insightface_efficientnet_b0 = 13013224;
var insightface_ghostnet_strides1 = 8093408;
var insightface_ghostnet_strides2 = 8049584;
var insightface_mobilenet_emore = 6938536;
var insightface_mobilenet_swish = 12168584;
var nanodet_e = 12319156;
var nanodet_g = 7574558;
var nanodet_m = 1887474;
var nanodet_t = 5294216;
var models_default = {
antispoof,
blazeface,
centernet,
emotion,
facemesh,
faceres,
"handlandmark-lite": handlandmark_lite,
handtrack,
iris,
liveness,
models,
"movenet-lightning": movenet_lightning,
"affectnet-mobilenet": affectnet_mobilenet,
age,
"blazeface-back": blazeface_back,
"blazeface-front": blazeface_front,
"blazepose-detector": blazepose_detector,
"blazepose-full": blazepose_full,
"blazepose-heavy": blazepose_heavy,
"blazepose-lite": blazepose_lite,
efficientpose,
faceboxes,
"facemesh-attention-pinto": facemesh_attention_pinto,
"facemesh-attention": facemesh_attention,
"facemesh-detection-full": facemesh_detection_full,
"facemesh-detection-short": facemesh_detection_short,
"faceres-deep": faceres_deep,
"gear-e1": gear_e1,
"gear-e2": gear_e2,
gear,
"gender-ssrnet-imdb": gender_ssrnet_imdb,
gender,
handdetect,
"handlandmark-full": handlandmark_full,
"handlandmark-sparse": handlandmark_sparse,
handskeleton,
meet,
mobileface,
mobilefacenet,
"movenet-multipose": movenet_multipose,
"movenet-thunder": movenet_thunder,
nanodet,
posenet,
rvm,
selfie,
"anti-spoofing": anti_spoofing,
"efficientpose-i-lite": efficientpose_i_lite,
"efficientpose-ii-lite": efficientpose_ii_lite,
"efficientpose-iv": efficientpose_iv,
"insightface-efficientnet-b0": insightface_efficientnet_b0,
"insightface-ghostnet-strides1": insightface_ghostnet_strides1,
"insightface-ghostnet-strides2": insightface_ghostnet_strides2,
"insightface-mobilenet-emore": insightface_mobilenet_emore,
"insightface-mobilenet-swish": insightface_mobilenet_swish,
"nanodet-e": nanodet_e,
"nanodet-g": nanodet_g,
"nanodet-m": nanodet_m,
"nanodet-t": nanodet_t
};
// src/tfjs/load.ts
var options = {
cacheModels: true,
cacheSupported: true,
verbose: true,
debug: false,
modelBasePath: ""
};
var modelStats = {};
async function httpHandler(url, init4) {
if (options.debug)
log("load model fetch:", url, init4);
return fetch(url, init4);
}
function setModelLoadOptions(config3) {
options.cacheModels = config3.cacheModels;
options.verbose = config3.debug;
options.modelBasePath = config3.modelBasePath;
}
async function loadModel(modelPath) {
var _a2, _b, _c2, _d2;
let modelUrl = join(options.modelBasePath, modelPath || "");
if (!modelUrl.toLowerCase().endsWith(".json"))
modelUrl += ".json";
const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\");
const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
const cachedModelName = "indexeddb://" + shortModelName;
modelStats[shortModelName] = {
name: shortModelName,
sizeFromManifest: 0,
sizeLoadedWeights: 0,
sizeDesired: models_exports[shortModelName],
inCache: false,
url: ""
};
options.cacheSupported = typeof indexedDB !== "undefined";
let cachedModels = {};
try {
cachedModels = options.cacheSupported && options.cacheModels ? await pi.listModels() : {};
} catch (e) {
options.cacheSupported = false;
}
modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;
const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) };
let model23 = new Ol(modelStats[shortModelName].url, tfLoadOptions);
let loaded = false;
try {
model23.findIOHandler();
if (options.debug)
log("model load handler:", model23["handler"]);
} catch (err) {
log("error finding model i/o handler:", modelUrl, err);
}
try {
const artifacts = await ((_a2 = model23.handler) == null ? void 0 : _a2.load()) || null;
modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0;
if (artifacts)
model23.loadSync(artifacts);
else
model23 = await W5(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
modelStats[shortModelName].sizeLoadedWeights = ((_d2 = (_c2 = model23.artifacts) == null ? void 0 : _c2.weightData) == null ? void 0 : _d2.byteLength) || 0;
if (options.verbose)
log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights });
loaded = true;
} catch (err) {
log("error loading model:", modelUrl, err);
}
if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) {
try {
const saveResult = await model23.save(cachedModelName);
if (options.debug)
log("model saved:", cachedModelName, saveResult);
} catch (err) {
log("error saving model:", modelUrl, err);
}
}
return model23;
}
// package.json
var version = "3.0.6";
// src/tfjs/humangl.ts
var config2 = {
name: "humangl",
priority: 999,
canvas: null,
gl: null,
extensions: [],
webGLattr: {
// https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2
alpha: false,
antialias: false,
premultipliedAlpha: false,
preserveDrawingBuffer: false,
depth: false,
stencil: false,
failIfMajorPerformanceCaveat: false,
// default=true
desynchronized: true
// default=undefined
}
};
function extensions() {
const gl2 = config2.gl;
if (!gl2)
return;
config2.extensions = gl2.getSupportedExtensions();
}
function register(instance) {
var _a2;
if (instance.config.backend !== "humangl")
return;
if (config2.name in ur().registry && !((_a2 = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a2.getParameter(config2.gl.VERSION))) {
log("humangl error: backend invalid context");
instance.models.reset();
}
if (!kme(config2.name)) {
try {
config2.canvas = canvas(100, 100);
} catch (err) {
log("humangl error: cannot create canvas:", err);
return;
}
try {
config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr);
if (!config2.gl) {
log("humangl error: cannot get webgl context");
return;
}
const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0");
if (!glv2) {
log("backend override: using fallback webgl backend as webgl 2.0 is not detected");
instance.config.backend = "webgl";
return;
}
if (config2.canvas) {
config2.canvas.addEventListener("webglcontextlost", (e) => {
log("humangl error:", e.type);
log("possible browser memory leak using webgl or conflict with multiple backend registrations");
instance.emit("error");
throw new Error("backend error: webgl context lost");
});
config2.canvas.addEventListener("webglcontextrestored", (e) => {
log("humangl error: context restored:", e);
});
config2.canvas.addEventListener("webglcontextcreationerror", (e) => {
log("humangl error: context create:", e);
});
}
} catch (err) {
log("humangl error: cannot get webgl context:", err);
return;
}
try {
iI(2, config2.gl);
} catch (err) {
log("humangl error: cannot set webgl context:", err);
return;
}
try {
const ctx = new xp(config2.gl);
eu(config2.name, () => new hu(ctx), config2.priority);
} catch (err) {
log("humangl error: cannot register webgl backend:", err);
return;
}
try {
const kernels = Km("webgl");
kernels.forEach((kernelConfig) => {
const newKernelConfig = { ...kernelConfig, backendName: config2.name };
Ya(newKernelConfig);
});
} catch (err) {
log("humangl error: cannot update webgl backend registration:", err);
return;
}
try {
if (P().flagRegistry.WEBGL_VERSION)
P().set("WEBGL_VERSION", 2);
} catch (err) {
log("humangl error: cannot set WebGL backend flags:", err);
return;
}
extensions();
const backend = Tme();
const current = typeof backend["gpgpu"] !== "undefined" ? backend["getGPGPUContext"]().gl : null;
if (current) {
if (instance.config.debug)
log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) });
} else {
log("humangl error: no current gl context:", current, config2.gl);
}
}
}
// src/tfjs/constants.ts
var constants = {
tf255: 255,
tf1: 1,
tf2: 2,
tf05: 0.5,
tf127: 127.5,
rgb: [0.2989, 0.587, 0.114]
};
function init() {
constants.tf255 = ke(255, "float32");
constants.tf1 = ke(1, "float32");
constants.tf2 = ke(2, "float32");
constants.tf05 = ke(0.5, "float32");
constants.tf127 = ke(127.5, "float32");
constants.rgb = xr([0.2989, 0.587, 0.114], "float32");
}
// src/tfjs/backend.ts
async function getBestBackend() {
var _a2;
await env.updateBackend();
if ((_a2 = env.tensorflow) == null ? void 0 : _a2.version)
return "tensorflow";
if (env.webgpu.supported && env.webgpu.backend)
return "webgpu";
if (env.webgl.supported && env.webgl.backend)
return "webgl";
if (env.wasm.supported && env.wasm.backend)
return "wasm";
return "cpu";
}
function registerCustomOps(config3) {
const newKernels = [];
if (!env.kernels.includes("mod")) {
const kernelMod = {
kernelName: "Mod",
backendName: Ime(),
kernelFunc: (op2) => De(() => Te(op2.inputs.a, se(Ke(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
};
Ya(kernelMod);
env.kernels.push("mod");
newKernels.push("mod");
}
if (!env.kernels.includes("floormod")) {
const kernelFloorMod = {
kernelName: "FloorMod",
backendName: Ime(),
kernelFunc: (op2) => De(() => be(se(pd(op2.inputs.a, op2.inputs.b), op2.inputs.b), x2(op2.inputs.a, op2.inputs.b)))
};
Ya(kernelFloorMod);
env.kernels.push("floormod");
newKernels.push("floormod");
}
if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) {
const kernelRotateWithOffset = {
kernelName: "RotateWithOffset",
backendName: Ime(),
kernelFunc: (op2) => De(() => {
const backend = Ime();
wme("cpu");
const t10 = uj.rotateWithOffset(op2.inputs.image, op2.attrs.radians, op2.attrs.fillValue, op2.attrs.center);
wme(backend);
return t10;
})
};
Ya(kernelRotateWithOffset);
env.kernels.push("rotatewithoffset");
newKernels.push("rotatewithoffset");
}
if (newKernels.length > 0 && config3.debug)
log("registered kernels:", newKernels);
}
var defaultFlags = {};
async function check(instance, force = false) {
var _a2, _b;
instance.state = "backend";
if (((_a2 = instance.config.backend) == null ? void 0 : _a2.length) === 0)
instance.config.backend = await getBestBackend();
if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && Ime() !== instance.config.backend) {
const timeStamp = now();
if (instance.config.backend && instance.config.backend.length > 0) {
if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) {
if (instance.config.debug)
log("running inside web worker");
}
if (typeof navigator !== "undefined" && ((_b = navigator == null ? void 0 : navigator.userAgent) == null ? void 0 : _b.toLowerCase().includes("electron"))) {
if (instance.config.debug)
log("running inside electron");
}
let available = Object.keys(ur().registryFactory);
if (instance.config.backend === "humangl" && !available.includes("humangl")) {
register(instance);
available = Object.keys(ur().registryFactory);
}
if (instance.config.debug)
log("available backends:", available);
if (env.browser && !env.node && instance.config.backend === "tensorflow" && available.includes("webgl")) {
if (instance.config.debug)
log("override: backend set to tensorflow while running in browser");
instance.config.backend = "webgl";
}
if (env.node && !env.browser && (instance.config.backend === "webgl" || instance.config.backend === "humangl") && available.includes("tensorflow")) {
if (instance.config.debug)
log(`override: backend set to ${instance.config.backend} while running in nodejs`);
instance.config.backend = "tensorflow";
}
if (env.browser && instance.config.backend === "webgpu") {
if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") {
log("override: backend set to webgpu but browser does not support webgpu");
instance.config.backend = "webgl";
} else {
const adapter = await navigator.gpu.requestAdapter();
if (instance.config.debug)
log("enumerated webgpu adapter:", adapter);
if (!adapter) {
log("override: backend set to webgpu but browser reports no available gpu");
instance.config.backend = "webgl";
} else {
const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0;
log("webgpu adapter info:", adapterInfo);
}
}
}
if (!available.includes(instance.config.backend)) {
log(`error: backend ${instance.config.backend} not found in registry`);
instance.config.backend = env.node ? "tensorflow" : "webgl";
if (instance.config.debug)
log(`override: setting backend ${instance.config.backend}`);
}
if (instance.config.debug)
log("setting backend:", [instance.config.backend]);
if (instance.config.backend === "wasm") {
if (P().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY)
P().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
if (instance.config.debug)
log("wasm path:", instance.config.wasmPath);
if (typeof Zne !== "undefined")
Zne(instance.config.wasmPath, instance.config.wasmPlatformFetch);
else
throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
let mt = false;
let simd = false;
try {
mt = await P().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
simd = await P().getAsync("WASM_HAS_SIMD_SUPPORT");
if (instance.config.debug)
log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`);
if (instance.config.debug && !simd)
log("warning: wasm simd support is not enabled");
} catch (e) {
log("wasm detection failed");
}
}
try {
await wme(instance.config.backend);
await Sme();
} catch (err) {
log("error: cannot set backend:", instance.config.backend, err);
return false;
}
if (instance.config.debug)
defaultFlags = JSON.parse(JSON.stringify(P().flags));
}
if (Ime() === "humangl" || Ime() === "webgl") {
if (P().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
P().set("WEBGL_USE_SHAPES_UNIFORMS", true);
if (P().flagRegistry.WEBGL_EXP_CONV)
P().set("WEBGL_EXP_CONV", true);
if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) {
log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
P().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
}
}
if (Ime() === "webgpu") {
}
if (instance.config.debug) {
const newFlags = P().flags;
const updatedFlags = {};
for (const key of Object.keys(newFlags)) {
if (defaultFlags[key] === newFlags[key])
continue;
updatedFlags[key] = newFlags[key];
}
if (instance.config.debug && Object.keys(updatedFlags).length > 0)
log("backend:", Ime(), "flags:", updatedFlags);
}
if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {
if (instance.config.debug)
log("flags:", instance.config["flags"]);
for (const [key, val] of Object.entries(instance.config.flags)) {
P().set(key, val);
}
}
fme();
init();
instance.performance.initBackend = Math.trunc(now() - timeStamp);
instance.config.backend = Ime();
await env.updateBackend();
registerCustomOps(instance.config);
}
return true;
}
function fakeOps(kernelNames, config3) {
for (const kernelName of kernelNames) {
const kernelConfig = {
kernelName,
backendName: config3.backend,
kernelFunc: (param) => {
var _a2;
if (config3.debug)
log("kernelFunc", kernelName, config3.backend, param);
return (_a2 = param == null ? void 0 : param.inputs) == null ? void 0 : _a2.info;
}
// setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },
// disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },
};
Ya(kernelConfig);
}
env.kernels = Km(Ime()).map((kernel) => kernel.kernelName.toLowerCase());
}
// src/draw/draw.ts
var draw_exports = {};
__export(draw_exports, {
all: () => all,
body: () => body,
canvas: () => canvas2,
face: () => face,
gesture: () => gesture,
hand: () => hand,
init: () => init2,
object: () => object,
options: () => options2,
person: () => person
});
// src/draw/primitives.ts
var getCanvasContext = (input) => {
if (!input)
log("draw error: invalid canvas");
else if (!input.getContext)
log("draw error: canvas context not defined");
else {
const ctx = input.getContext("2d");
if (!ctx)
log("draw error: cannot get canvas context");
else
return ctx;
}
return null;
};
var rad2deg = (theta) => Math.round(theta * 180 / Math.PI);
var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target);
var colorDepth = (z, opt) => {
if (!opt.useDepth || typeof z === "undefined")
return opt.color;
const rgb3 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]);
return `rgba(${rgb3[0]}, ${rgb3[1]}, ${rgb3[2]}, ${opt.alpha})`;
};
function labels(ctx, str, startX, startY, localOptions2) {
const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim());
const x = Math.max(0, startX);
for (let i = line.length - 1; i >= 0; i--) {
const y5 = i * localOptions2.lineHeight + startY;
if (localOptions2.shadowColor && localOptions2.shadowColor !== "") {
ctx.fillStyle = localOptions2.shadowColor;
ctx.fillText(line[i], x + 5, y5 + 16);
}
ctx.fillStyle = localOptions2.labelColor;
ctx.fillText(line[i], x + 4, y5 + 15);
}
}
function point(ctx, x, y5, z, localOptions2) {
ctx.fillStyle = colorDepth(z, localOptions2);
ctx.beginPath();
ctx.arc(x, y5, localOptions2.pointSize, 0, 2 * Math.PI);
ctx.fill();
}
function rect(ctx, x, y5, width, height, localOptions2) {
ctx.beginPath();
ctx.lineWidth = localOptions2.lineWidth;
if (localOptions2.useCurves) {
const cx2 = (x + x + width) / 2;
const cy2 = (y5 + y5 + height) / 2;
ctx.ellipse(cx2, cy2, width / 2, height / 2, 0, 0, 2 * Math.PI);
} else {
ctx.moveTo(x + localOptions2.roundRect, y5);
ctx.lineTo(x + width - localOptions2.roundRect, y5);
ctx.quadraticCurveTo(x + width, y5, x + width, y5 + localOptions2.roundRect);
ctx.lineTo(x + width, y5 + height - localOptions2.roundRect);
ctx.quadraticCurveTo(x + width, y5 + height, x + width - localOptions2.roundRect, y5 + height);
ctx.lineTo(x + localOptions2.roundRect, y5 + height);
ctx.quadraticCurveTo(x, y5 + height, x, y5 + height - localOptions2.roundRect);
ctx.lineTo(x, y5 + localOptions2.roundRect);
ctx.quadraticCurveTo(x, y5, x + localOptions2.roundRect, y5);
ctx.closePath();
}
ctx.stroke();
}
function lines(ctx, points, localOptions2) {
if (points.length < 2)
return;
ctx.beginPath();
ctx.moveTo(points[0][0], points[0][1]);
for (const pt2 of points) {
ctx.strokeStyle = colorDepth(pt2[2] || 0, localOptions2);
ctx.lineTo(Math.trunc(pt2[0]), Math.trunc(pt2[1]));
}
ctx.stroke();
if (localOptions2.fillPolygons) {
ctx.closePath();
ctx.fill();
}
}
function curves(ctx, points, localOptions2) {
if (points.length < 2)
return;
ctx.lineWidth = localOptions2.lineWidth;
if (!localOptions2.useCurves || points.length <= 2) {
lines(ctx, points, localOptions2);
return;
}
ctx.moveTo(points[0][0], points[0][1]);
for (let i = 0; i < points.length - 2; i++) {
const xc2 = (points[i][0] + points[i + 1][0]) / 2;
const yc2 = (points[i][1] + points[i + 1][1]) / 2;
ctx.quadraticCurveTo(points[i][0], points[i][1], xc2, yc2);
}
ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);
ctx.stroke();
if (localOptions2.fillPolygons) {
ctx.closePath();
ctx.fill();
}
}
function arrow(ctx, from, to, radius = 5) {
let angle;
let x;
let y5;
ctx.beginPath();
ctx.moveTo(from[0], from[1]);
ctx.lineTo(to[0], to[1]);
angle = Math.atan2(to[1] - from[1], to[0] - from[0]);
x = radius * Math.cos(angle) + to[0];
y5 = radius * Math.sin(angle) + to[1];
ctx.moveTo(x, y5);
angle += 1 / 3 * (2 * Math.PI);
x = radius * Math.cos(angle) + to[0];
y5 = radius * Math.sin(angle) + to[1];
ctx.lineTo(x, y5);
angle += 1 / 3 * (2 * Math.PI);
x = radius * Math.cos(angle) + to[0];
y5 = radius * Math.sin(angle) + to[1];
ctx.lineTo(x, y5);
ctx.closePath();
ctx.stroke();
ctx.fill();
}
// src/draw/options.ts
var options2 = {
color: "rgba(173, 216, 230, 0.6)",
// 'lightblue' with light alpha channel
labelColor: "rgba(173, 216, 230, 1)",
// 'lightblue' with dark alpha channel
shadowColor: "black",
alpha: 0.5,
font: 'small-caps 16px "Segoe UI"',
lineHeight: 18,
lineWidth: 4,
pointSize: 2,
roundRect: 8,
drawPoints: false,
drawLabels: true,
drawBoxes: true,
drawAttention: true,
drawGestures: true,
drawPolygons: true,
drawGaze: true,
fillPolygons: false,
useDepth: true,
useCurves: false,
faceLabels: "",
bodyLabels: "",
bodyPartLabels: "",
objectLabels: "",
handLabels: "",
fingerLabels: "",
gestureLabels: ""
};
// src/face/facemeshcoords.ts
var meshAnnotations = {
silhouette: [
10,
338,
297,
332,
284,
251,
389,
356,
454,
323,
361,
288,
397,
365,
379,
378,
400,
377,
152,
148,
176,
149,
150,
136,
172,
58,
132,
93,
234,
127,
162,
21,
54,
103,
67,
109
],
// lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11
// lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10
// lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11
// lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11
lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],
lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],
lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],
lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],
lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],
lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],
rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
// 7
rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
// 9
rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
// 7
rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
// 9
rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
// 7
rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
// 9
rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
// 9
rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
// 8
rightEyebrowLower: [35, 124, 46, 53, 52, 65],
// 6
rightEyeIris: [473, 474, 475, 476, 477],
// 5
leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
leftEyebrowLower: [265, 353, 276, 283, 282, 295],
leftEyeIris: [468, 469, 470, 471, 472],
midwayBetweenEyes: [168],
noseTip: [1],
noseBottom: [2],
noseRightCorner: [98],
noseLeftCorner: [327],
rightCheek: [205],
leftCheek: [425]
};
var meshLandmarks = {
count: 468,
mouth: 13,
symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]]
};
var blazeFaceLandmarks = {
leftEye: 0,
rightEye: 1,
nose: 2,
mouth: 3,
leftEar: 4,
rightEar: 5,
symmetryLine: [3, 2]
};
var irisIndices = [
// A mapping from facemesh model keypoints to iris model keypoints.
{ key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] },
// 7 x 3d
{ key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] },
// 7 x 3d
{ key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] },
// 7 x 3d
{ key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
// 7 x 3d
{ key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
// 9 x 3d
{ key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
// 9 x 3d
{ key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },
// 9 x 3d
{ key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] },
// 8 x 3d
{ key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] }
// 6 x 3d
];
var UV468 = [
[0.499976992607117, 0.652534008026123],
[0.500025987625122, 0.547487020492554],
[0.499974012374878, 0.602371990680695],
[0.482113003730774, 0.471979022026062],
[0.500150978565216, 0.527155995368958],
[0.499909996986389, 0.498252987861633],
[0.499523013830185, 0.40106201171875],
[0.289712011814117, 0.380764007568359],
[0.499954998493195, 0.312398016452789],
[0.499987006187439, 0.269918978214264],
[0.500023007392883, 0.107050001621246],
[0.500023007392883, 0.666234016418457],
[0.5000159740448, 0.679224014282227],
[0.500023007392883, 0.692348003387451],
[0.499976992607117, 0.695277988910675],
[0.499976992607117, 0.70593398809433],
[0.499976992607117, 0.719385027885437],
[0.499976992607117, 0.737019002437592],
[0.499967992305756, 0.781370997428894],
[0.499816000461578, 0.562981009483337],
[0.473773002624512, 0.573909997940063],
[0.104906998574734, 0.254140973091125],
[0.365929991006851, 0.409575998783112],
[0.338757991790771, 0.41302502155304],
[0.311120003461838, 0.409460008144379],
[0.274657994508743, 0.389131009578705],
[0.393361985683441, 0.403706014156342],
[0.345234006643295, 0.344011008739471],
[0.370094001293182, 0.346076011657715],
[0.319321990013123, 0.347265005111694],
[0.297903001308441, 0.353591024875641],
[0.24779200553894, 0.410809993743896],
[0.396889001131058, 0.842755019664764],
[0.280097991228104, 0.375599980354309],
[0.106310002505779, 0.399955987930298],
[0.2099249958992, 0.391353011131287],
[0.355807989835739, 0.534406006336212],
[0.471751004457474, 0.65040397644043],
[0.474155008792877, 0.680191993713379],
[0.439785003662109, 0.657229006290436],
[0.414617002010345, 0.66654098033905],
[0.450374007225037, 0.680860996246338],
[0.428770989179611, 0.682690978050232],
[0.374971002340317, 0.727805018424988],
[0.486716985702515, 0.547628998756409],
[0.485300987958908, 0.527395009994507],
[0.257764995098114, 0.314490020275116],
[0.401223003864288, 0.455172002315521],
[0.429818987846375, 0.548614978790283],
[0.421351999044418, 0.533740997314453],
[0.276895999908447, 0.532056987285614],
[0.483370006084442, 0.499586999416351],
[0.33721199631691, 0.282882988452911],
[0.296391993761063, 0.293242990970612],
[0.169294998049736, 0.193813979625702],
[0.447580009698868, 0.302609980106354],
[0.392390012741089, 0.353887975215912],
[0.354490011930466, 0.696784019470215],
[0.067304998636246, 0.730105042457581],
[0.442739009857178, 0.572826027870178],
[0.457098007202148, 0.584792017936707],
[0.381974011659622, 0.694710969924927],
[0.392388999462128, 0.694203019142151],
[0.277076005935669, 0.271932005882263],
[0.422551989555359, 0.563233017921448],
[0.385919004678726, 0.281364023685455],
[0.383103013038635, 0.255840003490448],
[0.331431001424789, 0.119714021682739],
[0.229923993349075, 0.232002973556519],
[0.364500999450684, 0.189113974571228],
[0.229622006416321, 0.299540996551514],
[0.173287004232407, 0.278747975826263],
[0.472878992557526, 0.666198015213013],
[0.446828007698059, 0.668527007102966],
[0.422762006521225, 0.673889994621277],
[0.445307999849319, 0.580065965652466],
[0.388103008270264, 0.693961024284363],
[0.403039008378983, 0.706539988517761],
[0.403629004955292, 0.693953037261963],
[0.460041999816895, 0.557139039039612],
[0.431158006191254, 0.692366003990173],
[0.452181994915009, 0.692366003990173],
[0.475387006998062, 0.692366003990173],
[0.465828001499176, 0.779190003871918],
[0.472328990697861, 0.736225962638855],
[0.473087012767792, 0.717857003211975],
[0.473122000694275, 0.704625964164734],
[0.473033010959625, 0.695277988910675],
[0.427942007780075, 0.695277988910675],
[0.426479011774063, 0.703539967536926],
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[0.4183090031147, 0.720062971115112],
[0.390094995498657, 0.639572978019714],
[0.013953999616206, 0.560034036636353],
[0.499913990497589, 0.58014702796936],
[0.413199990987778, 0.69539999961853],
[0.409626007080078, 0.701822996139526],
[0.468080013990402, 0.601534962654114],
[0.422728985548019, 0.585985004901886],
[0.463079988956451, 0.593783974647522],
[0.37211999297142, 0.47341400384903],
[0.334562003612518, 0.496073007583618],
[0.411671012639999, 0.546965003013611],
[0.242175996303558, 0.14767599105835],
[0.290776997804642, 0.201445996761322],
[0.327338010072708, 0.256527006626129],
[0.399509996175766, 0.748921036720276],
[0.441727995872498, 0.261676013469696],
[0.429764986038208, 0.187834024429321],
[0.412198007106781, 0.108901023864746],
[0.288955003023148, 0.398952007293701],
[0.218936994671822, 0.435410976409912],
[0.41278201341629, 0.398970007896423],
[0.257135003805161, 0.355440020561218],
[0.427684992551804, 0.437960982322693],
[0.448339998722076, 0.536936044692993],
[0.178560003638268, 0.45755398273468],
[0.247308000922203, 0.457193970680237],
[0.286267012357712, 0.467674970626831],
[0.332827985286713, 0.460712015628815],
[0.368755996227264, 0.447206974029541],
[0.398963987827301, 0.432654976844788],
[0.476410001516342, 0.405806005001068],
[0.189241006970406, 0.523923993110657],
[0.228962004184723, 0.348950982093811],
[0.490725994110107, 0.562400996685028],
[0.404670000076294, 0.485132992267609],
[0.019469000399113, 0.401564002037048],
[0.426243007183075, 0.420431017875671],
[0.396993011236191, 0.548797011375427],
[0.266469985246658, 0.376977026462555],
[0.439121007919312, 0.51895797252655],
[0.032313998788595, 0.644356966018677],
[0.419054001569748, 0.387154996395111],
[0.462783008813858, 0.505746960639954],
[0.238978996872902, 0.779744982719421],
[0.198220998048782, 0.831938028335571],
[0.107550002634525, 0.540755033493042],
[0.183610007166862, 0.740257024765015],
[0.134409993886948, 0.333683013916016],
[0.385764002799988, 0.883153975009918],
[0.490967005491257, 0.579378008842468],
[0.382384985685349, 0.508572995662689],
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272,
311,
248,
195,
281,
375,
273,
291,
175,
396,
199,
312,
311,
268,
276,
283,
445,
390,
373,
339,
295,
282,
296,
448,
449,
346,
356,
264,
454,
337,
336,
299,
337,
338,
151,
294,
278,
455,
308,
292,
415,
429,
358,
355,
265,
340,
372,
388,
390,
466,
352,
346,
280,
295,
442,
282,
354,
19,
370,
285,
441,
295,
195,
248,
197,
457,
440,
274,
301,
300,
368,
417,
351,
465,
251,
301,
389,
385,
380,
386,
394,
395,
379,
399,
412,
419,
410,
436,
322,
387,
373,
388,
326,
2,
393,
354,
370,
461,
393,
164,
267,
268,
302,
12,
386,
374,
387,
312,
268,
13,
298,
293,
301,
265,
446,
340,
380,
385,
381,
280,
330,
425,
322,
426,
391,
420,
429,
437,
393,
391,
326,
344,
440,
438,
458,
459,
461,
364,
434,
394,
428,
396,
262,
274,
354,
457,
317,
316,
402,
316,
315,
403,
315,
314,
404,
314,
313,
405,
313,
421,
406,
323,
366,
361,
292,
306,
407,
306,
291,
408,
291,
287,
409,
287,
432,
410,
427,
434,
411,
372,
264,
383,
459,
309,
457,
366,
352,
401,
1,
274,
4,
418,
421,
262,
331,
294,
358,
435,
433,
367,
392,
289,
439,
328,
462,
326,
94,
2,
370,
289,
305,
455,
339,
254,
448,
359,
255,
446,
254,
253,
449,
253,
252,
450,
252,
256,
451,
256,
341,
452,
414,
413,
463,
286,
441,
414,
286,
258,
441,
258,
257,
442,
257,
259,
443,
259,
260,
444,
260,
467,
445,
309,
459,
250,
305,
289,
290,
305,
290,
460,
401,
376,
435,
309,
250,
392,
376,
411,
433,
453,
341,
464,
357,
453,
465,
343,
357,
412,
437,
343,
399,
344,
360,
440,
420,
437,
456,
360,
420,
363,
361,
401,
288,
265,
372,
353,
390,
339,
249,
339,
448,
255
];
var VTX68 = [
/* cont */
127,
234,
132,
58,
172,
150,
149,
148,
152,
377,
378,
379,
397,
288,
361,
454,
356,
/* brows */
70,
63,
105,
66,
107,
336,
296,
334,
293,
300,
/* nose */
168,
6,
195,
4,
98,
97,
2,
326,
327,
/* eyes */
33,
160,
158,
133,
153,
144,
362,
385,
387,
263,
373,
380,
/* lip */
57,
40,
37,
0,
267,
270,
287,
321,
314,
17,
84,
91,
/* mouth */
78,
81,
13,
311,
308,
402,
14,
178
];
var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];
var VTX7 = [33, 133, 362, 263, 1, 78, 308];
var UV68 = VTX68.map((x) => UV468[x]);
var UV33 = VTX33.map((x) => UV468[x]);
var UV7 = VTX7.map((x) => UV468[x]);
function connectionsToIndices(connections) {
const indices = connections.map((connection) => connection[0]);
indices.push(connections[connections.length - 1][1]);
return indices;
}
var pairsLips = [
[61, 146],
[146, 91],
[91, 181],
[181, 84],
[84, 17],
[17, 314],
[314, 405],
[405, 321],
[321, 375],
[375, 291],
[61, 185],
[185, 40],
[40, 39],
[39, 37],
[37, 0],
[0, 267],
[267, 269],
[269, 270],
[270, 409],
[409, 291],
[78, 95],
[95, 88],
[88, 178],
[178, 87],
[87, 14],
[14, 317],
[317, 402],
[402, 318],
[318, 324],
[324, 308],
[78, 191],
[191, 80],
[80, 81],
[81, 82],
[82, 13],
[13, 312],
[312, 311],
[311, 310],
[310, 415],
[415, 308]
];
var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]];
var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]];
var pairsFaceContour = [
[10, 338],
[338, 297],
[297, 332],
[332, 284],
[284, 251],
[251, 389],
[389, 356],
[356, 454],
[454, 323],
[323, 361],
[361, 288],
[288, 397],
[397, 365],
[365, 379],
[379, 378],
[378, 400],
[400, 377],
[377, 152],
[152, 148],
[148, 176],
[176, 149],
[149, 150],
[150, 136],
[136, 172],
[172, 58],
[58, 132],
[132, 93],
[93, 234],
[234, 127],
[127, 162],
[162, 21],
[21, 54],
[54, 103],
[103, 67],
[67, 109],
[109, 10]
];
var contourKeypoints = {
lips: connectionsToIndices(pairsLips),
leftEye: connectionsToIndices(pairsLeftEye),
leftEyebrow: connectionsToIndices(pairsLeftEyebrow),
leftIris: connectionsToIndices(pairsLeftIris),
rightEye: connectionsToIndices(pairsRightEye),
rightEyebrow: connectionsToIndices(pairsRightEyebrow),
rightIris: connectionsToIndices(pairsRightIris),
faceOval: connectionsToIndices(pairsFaceContour)
};
// src/face/constants.ts
var LIPS_CONNECTIONS = [
[61, 146],
[146, 91],
[91, 181],
[181, 84],
[84, 17],
[17, 314],
[314, 405],
[405, 321],
[321, 375],
[375, 291],
[61, 185],
[185, 40],
[40, 39],
[39, 37],
[37, 0],
[0, 267],
[267, 269],
[269, 270],
[270, 409],
[409, 291],
[78, 95],
[95, 88],
[88, 178],
[178, 87],
[87, 14],
[14, 317],
[317, 402],
[402, 318],
[318, 324],
[324, 308],
[78, 191],
[191, 80],
[80, 81],
[81, 82],
[82, 13],
[13, 312],
[312, 311],
[311, 310],
[310, 415],
[415, 308]
];
var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]];
var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]];
var FACE_OVAL_CONNECTIONS = [
[10, 338],
[338, 297],
[297, 332],
[332, 284],
[284, 251],
[251, 389],
[389, 356],
[356, 454],
[454, 323],
[323, 361],
[361, 288],
[288, 397],
[397, 365],
[365, 379],
[379, 378],
[378, 400],
[400, 377],
[377, 152],
[152, 148],
[148, 176],
[176, 149],
[149, 150],
[150, 136],
[136, 172],
[172, 58],
[58, 132],
[132, 93],
[93, 234],
[234, 127],
[127, 162],
[162, 21],
[21, 54],
[54, 103],
[103, 67],
[67, 109],
[109, 10]
];
function connectionsToIndices2(connections) {
const indices = connections.map((connection) => connection[0]);
indices.push(connections[connections.length - 1][1]);
return indices;
}
var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {
lips: connectionsToIndices2(LIPS_CONNECTIONS),
leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS),
leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS),
leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS),
rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS),
rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS),
rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS),
faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS)
};
var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat();
var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);
var LANDMARKS_REFINEMENT_LIPS_CONFIG = [
61,
146,
91,
181,
84,
17,
314,
405,
321,
375,
291,
// Lower outer.
185,
40,
39,
37,
0,
267,
269,
270,
409,
// Upper outer(excluding corners).
78,
95,
88,
178,
87,
14,
317,
402,
318,
324,
308,
// Lower inner.
191,
80,
81,
82,
13,
312,
311,
310,
415,
// Upper inner(excluding corners).
76,
77,
90,
180,
85,
16,
315,
404,
320,
307,
306,
// Lower semi - outer.
184,
74,
73,
72,
11,
302,
303,
304,
408,
// Upper semi - outer(excluding corners).
62,
96,
89,
179,
86,
15,
316,
403,
319,
325,
292,
// Lower semi - inner.
183,
42,
41,
38,
12,
268,
271,
272,
407
// Upper semi - inner(excluding corners).
];
var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [
33,
7,
163,
144,
145,
153,
154,
155,
133,
// Lower contour.
246,
161,
160,
159,
158,
157,
173,
// upper contour (excluding corners).
130,
25,
110,
24,
23,
22,
26,
112,
243,
// Halo x2 lower contour.
247,
30,
29,
27,
28,
56,
190,
// Halo x2 upper contour (excluding corners).
226,
31,
228,
229,
230,
231,
232,
233,
244,
// Halo x3 lower contour.
113,
225,
224,
223,
222,
221,
189,
// Halo x3 upper contour (excluding corners).
35,
124,
46,
53,
52,
65,
// Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.
143,
111,
117,
118,
119,
120,
121,
128,
245,
// Halo x5 lower contour.
156,
70,
63,
105,
66,
107,
55,
193
// Halo x5 upper contour (excluding corners) or eyebrow outer contour.
];
var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
263,
249,
390,
373,
374,
380,
381,
382,
362,
// Lower contour.
466,
388,
387,
386,
385,
384,
398,
// Upper contour (excluding corners).
359,
255,
339,
254,
253,
252,
256,
341,
463,
// Halo x2 lower contour.
467,
260,
259,
257,
258,
286,
414,
// Halo x2 upper contour (excluding corners).
446,
261,
448,
449,
450,
451,
452,
453,
464,
// Halo x3 lower contour.
342,
445,
444,
443,
442,
441,
413,
// Halo x3 upper contour (excluding corners).
265,
353,
276,
283,
282,
295,
// Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.
372,
340,
346,
347,
348,
349,
350,
357,
465,
// Halo x5 lower contour.
383,
300,
293,
334,
296,
336,
285,
417
// Halo x5 upper contour (excluding corners) or eyebrow outer contour.
];
// src/draw/face.ts
var localOptions;
function drawLabels(f, ctx) {
var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2;
if (!localOptions.drawLabels || ((_a2 = localOptions.faceLabels) == null ? void 0 : _a2.length) === 0)
return;
let l = localOptions.faceLabels.slice();
l = replace(l, "[id]", f.id.toFixed(0));
if (f.score)
l = replace(l, "[score]", 100 * f.score);
if (f.gender)
l = replace(l, "[gender]", f.gender);
if (f.genderScore)
l = replace(l, "[genderScore]", 100 * f.genderScore);
if (f.age)
l = replace(l, "[age]", f.age);
if (f.distance)
l = replace(l, "[distance]", 100 * f.distance);
if (f.real)
l = replace(l, "[real]", 100 * f.real);
if (f.live)
l = replace(l, "[live]", 100 * f.live);
if (f.emotion && f.emotion.length > 0) {
const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
if (emotion2.length > 3)
emotion2.length = 3;
l = replace(l, "[emotions]", emotion2.join(" "));
}
if ((_c2 = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c2.roll)
l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll));
if ((_e = (_d2 = f.rotation) == null ? void 0 : _d2.angle) == null ? void 0 : _e.yaw)
l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw));
if ((_g2 = (_f2 = f.rotation) == null ? void 0 : _f2.angle) == null ? void 0 : _g2.pitch)
l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch));
if ((_i2 = (_h2 = f.rotation) == null ? void 0 : _h2.gaze) == null ? void 0 : _i2.bearing)
l = replace(l, "[gaze]", rad2deg(f.rotation.gaze.bearing));
labels(ctx, l, f.box[0], f.box[1], localOptions);
}
function drawIrisElipse(f, ctx) {
var _a2, _b, _c2, _d2;
if (((_a2 = f.annotations) == null ? void 0 : _a2.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) {
ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color;
ctx.beginPath();
const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;
const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;
ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
ctx.stroke();
if (localOptions.fillPolygons) {
ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color;
ctx.fill();
}
}
if (((_c2 = f.annotations) == null ? void 0 : _c2.rightEyeIris) && ((_d2 = f.annotations) == null ? void 0 : _d2.rightEyeIris[0])) {
ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color;
ctx.beginPath();
const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;
const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;
ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
ctx.stroke();
if (localOptions.fillPolygons) {
ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color;
ctx.fill();
}
}
}
function drawGazeSpheres(f, ctx) {
var _a2;
if (localOptions.drawGaze && ((_a2 = f.rotation) == null ? void 0 : _a2.angle) && typeof Path2D !== "undefined") {
ctx.strokeStyle = "pink";
const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
const pathV = new Path2D(`
M ${f.box[0] + f.box[2] / 2} ${f.box[1]}
C
${valX} ${f.box[1]},
${valX} ${f.box[1] + f.box[3]},
${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}
`);
const pathH = new Path2D(`
M ${f.box[0]} ${f.box[1] + f.box[3] / 2}
C
${f.box[0]} ${valY},
${f.box[0] + f.box[2]} ${valY},
${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}
`);
ctx.stroke(pathH);
ctx.stroke(pathV);
}
}
function drawGazeArrows(f, ctx) {
var _a2;
if (localOptions.drawGaze && ((_a2 = f.rotation) == null ? void 0 : _a2.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {
ctx.strokeStyle = "pink";
ctx.fillStyle = "pink";
const leftGaze = [
f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
];
arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);
const rightGaze = [
f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
];
arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);
}
}
function drawFacePolygons(f, ctx) {
if (localOptions.drawPolygons && f.mesh.length >= 468) {
ctx.lineWidth = 1;
for (let i = 0; i < TRI468.length / 3; i++) {
const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]);
lines(ctx, points, localOptions);
}
drawIrisElipse(f, ctx);
}
}
function drawFacePoints(f, ctx) {
if (localOptions.drawPoints) {
if ((f == null ? void 0 : f.mesh.length) >= 468) {
for (let i = 0; i < f.mesh.length; i++) {
point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);
if (localOptions.drawAttention) {
if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i))
point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions);
if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i))
point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions);
if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i))
point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions);
}
}
} else {
for (const [k, v5] of Object.entries((f == null ? void 0 : f.annotations) || {})) {
if (!(v5 == null ? void 0 : v5[0]))
continue;
const pt2 = v5[0];
point(ctx, pt2[0], pt2[1], 0, localOptions);
if (localOptions.drawLabels)
labels(ctx, k, pt2[0], pt2[1], localOptions);
}
}
}
}
function drawFaceBoxes(f, ctx) {
if (localOptions.drawBoxes) {
rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
}
}
function face(inCanvas2, result, drawOptions) {
localOptions = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.font = localOptions.font;
ctx.strokeStyle = localOptions.color;
ctx.fillStyle = localOptions.color;
for (const f of result) {
drawFaceBoxes(f, ctx);
drawLabels(f, ctx);
if (f.mesh && f.mesh.length > 0) {
drawFacePoints(f, ctx);
drawFacePolygons(f, ctx);
drawGazeSpheres(f, ctx);
drawGazeArrows(f, ctx);
}
}
}
// src/draw/body.ts
function body(inCanvas2, result, drawOptions) {
var _a2, _b;
const localOptions2 = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.lineJoin = "round";
for (let i = 0; i < result.length; i++) {
ctx.strokeStyle = localOptions2.color;
ctx.fillStyle = localOptions2.color;
ctx.lineWidth = localOptions2.lineWidth;
ctx.font = localOptions2.font;
if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) {
rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2);
if (localOptions2.drawLabels && ((_a2 = localOptions2.bodyLabels) == null ? void 0 : _a2.length) > 0) {
let l = localOptions2.bodyLabels.slice();
l = replace(l, "[id]", result[i].id.toFixed(0));
l = replace(l, "[score]", 100 * result[i].score);
labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2);
}
}
if (localOptions2.drawPoints && result[i].keypoints) {
for (let pt2 = 0; pt2 < result[i].keypoints.length; pt2++) {
if (!result[i].keypoints[pt2].score || result[i].keypoints[pt2].score === 0)
continue;
ctx.fillStyle = colorDepth(result[i].keypoints[pt2].position[2], localOptions2);
point(ctx, result[i].keypoints[pt2].position[0], result[i].keypoints[pt2].position[1], 0, localOptions2);
}
}
if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) {
ctx.font = localOptions2.font;
for (const pt2 of result[i].keypoints) {
if (!pt2.score || pt2.score === 0)
continue;
let l = localOptions2.bodyPartLabels.slice();
l = replace(l, "[label]", pt2.part);
l = replace(l, "[score]", 100 * pt2.score);
labels(ctx, l, pt2.position[0], pt2.position[1], localOptions2);
}
}
if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) {
for (const part of Object.values(result[i].annotations)) {
for (const connected4 of part)
curves(ctx, connected4, localOptions2);
}
}
}
}
// src/draw/hand.ts
function hand(inCanvas2, result, drawOptions) {
var _a2, _b;
const localOptions2 = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.lineJoin = "round";
ctx.font = localOptions2.font;
for (const h of result) {
if (localOptions2.drawBoxes) {
ctx.strokeStyle = localOptions2.color;
ctx.fillStyle = localOptions2.color;
rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2);
if (localOptions2.drawLabels && ((_a2 = localOptions2.handLabels) == null ? void 0 : _a2.length) > 0) {
let l = localOptions2.handLabels.slice();
l = replace(l, "[id]", h.id.toFixed(0));
l = replace(l, "[label]", h.label);
l = replace(l, "[score]", 100 * h.score);
labels(ctx, l, h.box[0], h.box[1], localOptions2);
}
ctx.stroke();
}
if (localOptions2.drawPoints) {
if (h.keypoints && h.keypoints.length > 0) {
for (const pt2 of h.keypoints) {
ctx.fillStyle = colorDepth(pt2[2], localOptions2);
point(ctx, pt2[0], pt2[1], 0, localOptions2);
}
}
}
if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) {
for (const [part, pt2] of Object.entries(h.annotations)) {
let l = localOptions2.fingerLabels.slice();
l = replace(l, "[label]", part);
labels(ctx, l, pt2[pt2.length - 1][0], pt2[pt2.length - 1][1], localOptions2);
}
}
if (localOptions2.drawPolygons && h.annotations) {
const addHandLine = (part) => {
if (!part || part.length === 0 || !part[0])
return;
for (let i = 0; i < part.length; i++) {
ctx.beginPath();
const z = part[i][2] || 0;
ctx.strokeStyle = colorDepth(i * z, localOptions2);
ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);
ctx.lineTo(part[i][0], part[i][1]);
ctx.stroke();
}
};
ctx.lineWidth = localOptions2.lineWidth;
addHandLine(h.annotations.index);
addHandLine(h.annotations.middle);
addHandLine(h.annotations.ring);
addHandLine(h.annotations.pinky);
addHandLine(h.annotations.thumb);
}
}
}
// src/draw/object.ts
function object(inCanvas2, result, drawOptions) {
var _a2;
const localOptions2 = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.lineJoin = "round";
ctx.font = localOptions2.font;
for (const h of result) {
if (localOptions2.drawBoxes) {
ctx.strokeStyle = localOptions2.color;
ctx.fillStyle = localOptions2.color;
rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2);
if (localOptions2.drawLabels && ((_a2 = localOptions2.objectLabels) == null ? void 0 : _a2.length) > 0) {
let l = localOptions2.objectLabels.slice();
l = replace(l, "[id]", h.id.toFixed(0));
l = replace(l, "[label]", h.label);
l = replace(l, "[score]", 100 * h.score);
labels(ctx, l, h.box[0], h.box[1], localOptions2);
}
ctx.stroke();
}
}
}
// src/draw/gesture.ts
function gesture(inCanvas2, result, drawOptions) {
var _a2;
const localOptions2 = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
if (localOptions2.drawGestures && ((_a2 = localOptions2.gestureLabels) == null ? void 0 : _a2.length) > 0) {
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.font = localOptions2.font;
ctx.fillStyle = localOptions2.color;
let i = 1;
for (let j = 0; j < result.length; j++) {
const [where, what] = Object.entries(result[j]);
if (what.length > 1 && what[1].length > 0) {
const who = where[1] > 0 ? `#${where[1]}` : "";
let l = localOptions2.gestureLabels.slice();
l = replace(l, "[where]", where[0]);
l = replace(l, "[who]", who);
l = replace(l, "[what]", what[1]);
labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2);
i += 1;
}
}
}
}
// src/draw/labels.ts
var defaultLabels = {
face: `face
confidence: [score]%
[gender] [genderScore]%
age: [age] years
distance: [distance]cm
real: [real]%
live: [live]%
[emotions]
roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0
gaze: [gaze]\xB0`,
body: "body [score]%",
bodyPart: "[label] [score]%",
object: "[label] [score]%",
hand: "[label] [score]%",
finger: "[label]",
gesture: "[where] [who]: [what]"
};
// src/draw/draw.ts
var drawTime = 0;
function person(inCanvas2, result, drawOptions) {
const localOptions2 = mergeDeep(options2, drawOptions);
if (!result || !inCanvas2)
return;
const ctx = getCanvasContext(inCanvas2);
if (!ctx)
return;
ctx.lineJoin = "round";
ctx.font = localOptions2.font;
for (let i = 0; i < result.length; i++) {
if (localOptions2.drawBoxes) {
ctx.strokeStyle = localOptions2.color;
ctx.fillStyle = localOptions2.color;
rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2);
if (localOptions2.drawLabels) {
const label = `person #${i}`;
if (localOptions2.shadowColor && localOptions2.shadowColor !== "") {
ctx.fillStyle = localOptions2.shadowColor;
ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]);
}
ctx.fillStyle = localOptions2.labelColor;
ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]);
}
ctx.stroke();
}
}
}
function canvas2(input, output) {
if (!input || !output)
return;
const ctx = getCanvasContext(output);
if (!ctx)
return;
ctx.drawImage(input, 0, 0);
}
async function all(inCanvas2, result, drawOptions) {
if (!(result == null ? void 0 : result.performance) || !inCanvas2)
return null;
const timeStamp = now();
const localOptions2 = mergeDeep(options2, drawOptions);
const promise = Promise.all([
face(inCanvas2, result.face, localOptions2),
body(inCanvas2, result.body, localOptions2),
hand(inCanvas2, result.hand, localOptions2),
object(inCanvas2, result.object, localOptions2),
gesture(inCanvas2, result.gesture, localOptions2)
// gestures do not have buffering
// person(inCanvas, result.persons, localOptions); // already included above
]);
drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);
result.performance.draw = drawTime;
return promise;
}
function init2() {
options2.faceLabels = defaultLabels.face;
options2.bodyLabels = defaultLabels.body;
options2.bodyPartLabels = defaultLabels.bodyPart;
options2.handLabels = defaultLabels.hand;
options2.fingerLabels = defaultLabels.finger;
options2.objectLabels = defaultLabels.object;
options2.gestureLabels = defaultLabels.gesture;
}
// src/body/blazeposecoords.ts
var blazeposecoords_exports = {};
__export(blazeposecoords_exports, {
connected: () => connected,
kpt: () => kpt
});
var kpt = [
"nose",
// 0
"leftEyeInside",
// 1
"leftEye",
// 2
"leftEyeOutside",
// 3
"rightEyeInside",
// 4
"rightEye",
// 5
"rightEyeOutside",
// 6
"leftEar",
// 7
"rightEar",
// 8
"leftMouth",
// 9
"rightMouth",
// 10
"leftShoulder",
// 11
"rightShoulder",
// 12
"leftElbow",
// 13
"rightElbow",
// 14
"leftWrist",
// 15
"rightWrist",
// 16
"leftPinky",
// 17
"rightPinky",
// 18
"leftIndex",
// 19
"rightIndex",
// 20
"leftThumb",
// 21
"rightThumb",
// 22
"leftHip",
// 23
"rightHip",
// 24
"leftKnee",
// 25
"rightKnee",
// 26
"leftAnkle",
// 27
"rightAnkle",
// 28
"leftHeel",
// 29
"rightHeel",
// 30
"leftFoot",
// 31
"rightFoot",
// 32
"bodyCenter",
// 33
"bodyTop",
// 34
"leftPalm",
// 35 // z-coord not ok
"leftHand",
// 36 // similar to wrist but z-coord not ok
"rightPalm",
// 37 // z-coord not ok
"rightHand"
// 38 // similar to wrist but z-coord not ok
];
var connected = {
shoulders: ["leftShoulder", "rightShoulder"],
hips: ["rightHip", "leftHip"],
mouth: ["leftMouth", "rightMouth"],
leftLegUpper: ["leftHip", "leftKnee"],
leftLegLower: ["leftKnee", "leftAnkle"],
leftFoot: ["leftAnkle", "leftHeel", "leftFoot"],
leftTorso: ["leftShoulder", "leftHip"],
leftArmUpper: ["leftShoulder", "leftElbow"],
leftArmLower: ["leftElbow", "leftWrist"],
leftHand: ["leftWrist", "leftPalm"],
leftHandPinky: ["leftPalm", "leftPinky"],
leftHandIndex: ["leftPalm", "leftIndex"],
leftHandThumb: ["leftPalm", "leftThumb"],
leftEyeOutline: ["leftEyeInside", "leftEyeOutside"],
rightLegUpper: ["rightHip", "rightKnee"],
rightLegLower: ["rightKnee", "rightAnkle"],
rightFoot: ["rightAnkle", "rightHeel", "rightFoot"],
rightTorso: ["rightShoulder", "rightHip"],
rightArmUpper: ["rightShoulder", "rightElbow"],
rightArmLower: ["rightElbow", "rightWrist"],
rightHand: ["rightWrist", "rightPalm"],
rightHandPinky: ["rightPalm", "rightPinky"],
rightHandIndex: ["rightPalm", "rightIndex"],
rightHandThumb: ["rightPalm", "rightThumb"],
rightEyeOutline: ["rightEyeInside", "rightEyeOutside"]
};
// src/body/blazeposedetector.ts
var model;
var inputSize = 224;
var anchorTensor;
var numLayers = 5;
var strides = [8, 16, 32, 32, 32];
function createAnchors() {
const anchors3 = [];
let layerId = 0;
while (layerId < numLayers) {
let anchorCount = 0;
let lastSameStrideLayer = layerId;
while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {
anchorCount += 2;
lastSameStrideLayer++;
}
const stride = strides[layerId];
const featureMapHeight = Math.ceil(inputSize / stride);
const featureMapWidth = Math.ceil(inputSize / stride);
for (let y5 = 0; y5 < featureMapHeight; ++y5) {
for (let x = 0; x < featureMapWidth; ++x) {
for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y5 + 0.5) / featureMapHeight });
}
}
}
layerId = lastSameStrideLayer;
}
anchorTensor = { x: xr(anchors3.map((a) => a.x)), y: xr(anchors3.map((a) => a.y)) };
}
async function loadDetector(config3) {
if (env.initial)
model = null;
if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") {
model = await loadModel(config3.body["detector"].modelPath);
const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0;
inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
} else if (config3.debug && model)
log("cached model:", model["modelUrl"]);
createAnchors();
return model;
}
var cropFactor = [5, 5];
function decodeBoxes(boxesTensor, anchor) {
return De(() => {
const split = ai(boxesTensor, 12, 1);
let xCenter = lc(split[0]);
let yCenter = lc(split[1]);
let width = lc(split[2]);
let height = lc(split[3]);
xCenter = be(Ke(xCenter, inputSize), anchor.x);
yCenter = be(Ke(yCenter, inputSize), anchor.y);
width = se(Ke(width, inputSize), cropFactor[0]);
height = se(Ke(height, inputSize), cropFactor[1]);
const xMin = Te(xCenter, Ke(width, 2));
const yMin = Te(yCenter, Ke(height, 2));
const xMax = be(xMin, width);
const yMax = be(yMin, height);
const boxes = vr([xMin, yMin, xMax, yMax], 1);
return boxes;
});
}
async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) {
var _a2, _b;
const detectedBoxes = [];
const t10 = {};
t10.boxes = decodeBoxes(boxesTensor, anchorTensor);
t10.scores = wa(logitsTensor);
t10.nms = await uj.nonMaxSuppressionAsync(t10.boxes, t10.scores, 1, ((_a2 = config3.body["detector"]) == null ? void 0 : _a2.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1);
const nms = await t10.nms.data();
const scores = await t10.scores.data();
const boxes = await t10.boxes.array();
for (const i of Array.from(nms)) {
const score = scores[i];
const boxRaw = boxes[i];
const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])];
const detectedBox = { score, boxRaw, box };
detectedBoxes.push(detectedBox);
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return detectedBoxes;
}
async function detectBoxes(input, config3, outputSize2) {
const t10 = {};
t10.res = model == null ? void 0 : model.execute(input, ["Identity"]);
t10.logitsRaw = qe(t10.res, [0, 0, 0], [1, -1, 1]);
t10.boxesRaw = qe(t10.res, [0, 0, 1], [1, -1, -1]);
t10.logits = lc(t10.logitsRaw);
t10.boxes = lc(t10.boxesRaw);
const boxes = await decodeResults(t10.boxes, t10.logits, config3, outputSize2);
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return boxes;
}
// src/util/box.ts
function calc(keypoints, outputSize2 = [1, 1]) {
const coords = [keypoints.map((pt2) => pt2[0]), keypoints.map((pt2) => pt2[1])];
const min = [Math.min(...coords[0]), Math.min(...coords[1])];
const max = [Math.max(...coords[0]), Math.max(...coords[1])];
const box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];
const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
return { box, boxRaw };
}
function square(keypoints, outputSize2 = [1, 1]) {
const coords = [keypoints.map((pt2) => pt2[0]), keypoints.map((pt2) => pt2[1])];
const min = [Math.min(...coords[0]), Math.min(...coords[1])];
const max = [Math.max(...coords[0]), Math.max(...coords[1])];
const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2];
const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]);
const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];
const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
return { box, boxRaw };
}
function scale(box, scaleFact) {
const dist = [box[2] * scaleFact, box[3] * scaleFact];
const newBox = [
box[0] - (dist[0] - box[2]) / 2,
box[1] - (dist[1] - box[3]) / 2,
dist[0],
dist[1]
];
return newBox;
}
// src/body/blazepose.ts
var model2;
var inputSize2 = 256;
var skipped = Number.MAX_SAFE_INTEGER;
var outputNodes = {
landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
detector: []
};
var cache = [];
var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
var lastTime = 0;
var sigmoid = (x) => 1 - 1 / (1 + Math.exp(x));
var loadDetect = (config3) => loadDetector(config3);
async function loadPose(config3) {
if (env.initial)
model2 = null;
if (!model2) {
model2 = await loadModel(config3.body.modelPath);
const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0;
inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
} else if (config3.debug)
log("cached model:", model2["modelUrl"]);
return model2;
}
function prepareImage(input, size2, cropBox) {
var _a2, _b;
const t10 = {};
if (!((_a2 = input == null ? void 0 : input.shape) == null ? void 0 : _a2[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
return input;
let final;
if (cropBox) {
t10.cropped = uj.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
}
if (input.shape[1] !== input.shape[2]) {
const height = [
input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,
input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0
];
const width = [
input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,
input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0
];
padding = [
[0, 0],
// dont touch batch
height,
// height before&after
width,
// width before&after
[0, 0]
// dont touch rbg
];
t10.pad = ka(t10.cropped || input, padding);
t10.resize = uj.resizeBilinear(t10.pad, [size2, size2]);
final = Ke(t10.resize, constants.tf255);
} else if (input.shape[1] !== size2) {
t10.resize = uj.resizeBilinear(t10.cropped || input, [size2, size2]);
final = Ke(t10.resize, constants.tf255);
} else {
final = Ke(t10.cropped || input, constants.tf255);
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return final;
}
function rescaleKeypoints(keypoints, outputSize2, cropBox) {
for (const kpt4 of keypoints) {
kpt4.position = [
Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]),
Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]),
kpt4.position[2]
];
kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])];
}
if (cropBox) {
const width = cropBox[2] - cropBox[0];
const height = cropBox[3] - cropBox[1];
for (const kpt4 of keypoints) {
kpt4.positionRaw = [
kpt4.positionRaw[0] / height + cropBox[1],
// correct offset due to crop
kpt4.positionRaw[1] / width + cropBox[0],
// correct offset due to crop
kpt4.positionRaw[2]
];
kpt4.position = [
Math.trunc(kpt4.positionRaw[0] * outputSize2[0]),
Math.trunc(kpt4.positionRaw[1] * outputSize2[1]),
kpt4.positionRaw[2]
];
}
}
return keypoints;
}
function fixKeypoints(keypoints) {
const leftPalm = keypoints.find((k) => k.part === "leftPalm");
const leftWrist = keypoints.find((k) => k.part === "leftWrist");
const leftIndex = keypoints.find((k) => k.part === "leftIndex");
leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;
const rightPalm = keypoints.find((k) => k.part === "rightPalm");
const rightWrist = keypoints.find((k) => k.part === "rightWrist");
const rightIndex = keypoints.find((k) => k.part === "rightIndex");
rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;
}
async function detectLandmarks(input, config3, outputSize2) {
if (!(model2 == null ? void 0 : model2["executor"]))
return null;
const t10 = {};
[
t10.ld,
t10.segmentation,
t10.heatmap,
t10.world,
t10.poseflag
/* 1,1 */
] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks);
const poseScore = (await t10.poseflag.data())[0];
const points = await t10.ld.data();
const distances = await t10.world.data();
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
const keypointsRelative = [];
const depth = 5;
for (let i = 0; i < points.length / depth; i++) {
const score = sigmoid(points[depth * i + 3]);
const presence = sigmoid(points[depth * i + 4]);
const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0];
const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];
keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore });
}
if (poseScore < (config3.body.minConfidence || 0))
return null;
fixKeypoints(keypointsRelative);
const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
const kpts = keypoints.map((k) => k.position);
const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected)) {
const pt2 = [];
for (let i = 0; i < indexes.length - 1; i++) {
const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]);
const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
if (pt0 && pt1)
pt2.push([pt0.position, pt1.position]);
}
annotations2[name] = pt2;
}
const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
return body4;
}
async function predict(input, config3) {
var _a2, _b, _c2;
const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
const skipTime = (config3.body.skipTime || 0) > now() - lastTime;
const skipFrame = skipped < (config3.body.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
skipped++;
} else {
let boxes = [];
if ((_b = (_a2 = config3.body) == null ? void 0 : _a2["detector"]) == null ? void 0 : _b["enabled"]) {
const preparedImage = prepareImage(input, 224);
boxes = await detectBoxes(preparedImage, config3, outputSize2);
Ot(preparedImage);
} else {
boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }];
}
for (let i = 0; i < boxes.length; i++) {
const preparedBox = prepareImage(input, 256, (_c2 = boxes[i]) == null ? void 0 : _c2.boxRaw);
cache.length = 0;
const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2);
Ot(preparedBox);
if (!bodyResult)
continue;
bodyResult.id = i;
cache.push(bodyResult);
}
lastTime = now();
skipped = 0;
}
return cache;
}
// src/object/labels.ts
var labels2 = [
{ class: 1, label: "person" },
{ class: 2, label: "bicycle" },
{ class: 3, label: "car" },
{ class: 4, label: "motorcycle" },
{ class: 5, label: "airplane" },
{ class: 6, label: "bus" },
{ class: 7, label: "train" },
{ class: 8, label: "truck" },
{ class: 9, label: "boat" },
{ class: 10, label: "traffic light" },
{ class: 11, label: "fire hydrant" },
{ class: 12, label: "stop sign" },
{ class: 13, label: "parking meter" },
{ class: 14, label: "bench" },
{ class: 15, label: "bird" },
{ class: 16, label: "cat" },
{ class: 17, label: "dog" },
{ class: 18, label: "horse" },
{ class: 19, label: "sheep" },
{ class: 20, label: "cow" },
{ class: 21, label: "elephant" },
{ class: 22, label: "bear" },
{ class: 23, label: "zebra" },
{ class: 24, label: "giraffe" },
{ class: 25, label: "backpack" },
{ class: 26, label: "umbrella" },
{ class: 27, label: "handbag" },
{ class: 28, label: "tie" },
{ class: 29, label: "suitcase" },
{ class: 30, label: "frisbee" },
{ class: 31, label: "skis" },
{ class: 32, label: "snowboard" },
{ class: 33, label: "sports ball" },
{ class: 34, label: "kite" },
{ class: 35, label: "baseball bat" },
{ class: 36, label: "baseball glove" },
{ class: 37, label: "skateboard" },
{ class: 38, label: "surfboard" },
{ class: 39, label: "tennis racket" },
{ class: 40, label: "bottle" },
{ class: 41, label: "wine glass" },
{ class: 42, label: "cup" },
{ class: 43, label: "fork" },
{ class: 44, label: "knife" },
{ class: 45, label: "spoon" },
{ class: 46, label: "bowl" },
{ class: 47, label: "banana" },
{ class: 48, label: "apple" },
{ class: 49, label: "sandwich" },
{ class: 50, label: "orange" },
{ class: 51, label: "broccoli" },
{ class: 52, label: "carrot" },
{ class: 53, label: "hot dog" },
{ class: 54, label: "pizza" },
{ class: 55, label: "donut" },
{ class: 56, label: "cake" },
{ class: 57, label: "chair" },
{ class: 58, label: "couch" },
{ class: 59, label: "potted plant" },
{ class: 60, label: "bed" },
{ class: 61, label: "dining table" },
{ class: 62, label: "toilet" },
{ class: 63, label: "tv" },
{ class: 64, label: "laptop" },
{ class: 65, label: "mouse" },
{ class: 66, label: "remote" },
{ class: 67, label: "keyboard" },
{ class: 68, label: "cell phone" },
{ class: 69, label: "microwave" },
{ class: 70, label: "oven" },
{ class: 71, label: "toaster" },
{ class: 72, label: "sink" },
{ class: 73, label: "refrigerator" },
{ class: 74, label: "book" },
{ class: 75, label: "clock" },
{ class: 76, label: "vase" },
{ class: 77, label: "scissors" },
{ class: 78, label: "teddy bear" },
{ class: 79, label: "hair drier" },
{ class: 80, label: "toothbrush" }
];
// src/object/centernet.ts
var model3;
var inputSize3 = 0;
var last2 = [];
var lastTime2 = 0;
var skipped2 = Number.MAX_SAFE_INTEGER;
async function load(config3) {
if (env.initial)
model3 = null;
if (!model3) {
model3 = await loadModel(config3.object.modelPath);
const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0;
inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
} else if (config3.debug)
log("cached model:", model3["modelUrl"]);
return model3;
}
async function process3(res, outputShape, config3) {
if (!res)
return [];
const t10 = {};
const results = [];
const detections = await res.array();
t10.squeeze = lc(res);
const arr = ai(t10.squeeze, 6, 1);
t10.stack = vr([arr[1], arr[0], arr[3], arr[2]], 1);
t10.boxes = lc(t10.stack);
t10.scores = lc(arr[4]);
t10.classes = lc(arr[5]);
Ot([res, ...arr]);
t10.nms = await uj.nonMaxSuppressionAsync(t10.boxes, t10.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0);
const nms = await t10.nms.data();
let i = 0;
for (const id2 of Array.from(nms)) {
const score = Math.trunc(100 * detections[0][id2][4]) / 100;
const classVal = detections[0][id2][5];
if (Number.isNaN(classVal))
continue;
const label = labels2[classVal].label;
const [x, y5] = [
detections[0][id2][0] / inputSize3,
detections[0][id2][1] / inputSize3
];
const boxRaw = [
x,
y5,
detections[0][id2][2] / inputSize3 - x,
detections[0][id2][3] / inputSize3 - y5
];
const box = [
Math.trunc(boxRaw[0] * outputShape[0]),
Math.trunc(boxRaw[1] * outputShape[1]),
Math.trunc(boxRaw[2] * outputShape[0]),
Math.trunc(boxRaw[3] * outputShape[1])
];
results.push({ id: i++, score, class: classVal, label, box, boxRaw });
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return results;
}
async function predict2(input, config3) {
if (!(model3 == null ? void 0 : model3["executor"]))
return [];
const skipTime = (config3.object.skipTime || 0) > now() - lastTime2;
const skipFrame = skipped2 < (config3.object.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
skipped2++;
return last2;
}
skipped2 = 0;
return new Promise(async (resolve) => {
const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
const resize = uj.resizeBilinear(input, [inputSize3, inputSize3]);
const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null;
lastTime2 = now();
Ot(resize);
const obj = await process3(objectT, outputSize2, config3);
last2 = obj;
resolve(obj);
});
}
// src/body/efficientposecoords.ts
var efficientposecoords_exports = {};
__export(efficientposecoords_exports, {
connected: () => connected2,
kpt: () => kpt2
});
var kpt2 = [
"head",
"neck",
"rightShoulder",
"rightElbow",
"rightWrist",
"chest",
"leftShoulder",
"leftElbow",
"leftWrist",
"bodyCenter",
"rightHip",
"rightKnee",
"rightAnkle",
"leftHip",
"leftKnee",
"leftAnkle"
];
var connected2 = {
leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
head: []
};
// src/body/efficientpose.ts
var model4;
var lastTime3 = 0;
var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
var skipped3 = Number.MAX_SAFE_INTEGER;
async function load2(config3) {
if (env.initial)
model4 = null;
if (!model4)
model4 = await loadModel(config3.body.modelPath);
else if (config3.debug)
log("cached model:", model4["modelUrl"]);
return model4;
}
async function max2d(inputs, minScore) {
const [width, height] = inputs.shape;
const reshaped = W(inputs, [height * width]);
const max = Ia(reshaped, 0);
const newScore = (await max.data())[0];
if (newScore > minScore) {
const coordinates = mk(reshaped, 0);
const mod = x2(coordinates, width);
const x = (await mod.data())[0];
const div = Ke(coordinates, width);
const y5 = (await div.data())[0];
Ot([reshaped, max, coordinates, mod, div]);
return [x, y5, newScore];
}
Ot([reshaped, max]);
return [0, 0, newScore];
}
async function predict3(image, config3) {
if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape))
return [];
const skipTime = (config3.body.skipTime || 0) > now() - lastTime3;
const skipFrame = skipped3 < (config3.body.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
skipped3++;
return [cache2];
}
skipped3 = 0;
return new Promise(async (resolve) => {
const tensor = De(() => {
var _a2, _b;
const resize = uj.resizeBilinear(image, [((_a2 = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a2[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false);
const enhance2 = se(resize, constants.tf2);
const norm = Te(enhance2, constants.tf1);
return norm;
});
let resT;
if (config3.body.enabled)
resT = model4 == null ? void 0 : model4.execute(tensor);
lastTime3 = now();
Ot(tensor);
if (resT) {
cache2.keypoints.length = 0;
const squeeze = lc(resT);
Ot(resT);
const stack = po(squeeze, 2);
Ot(squeeze);
for (let id2 = 0; id2 < stack.length; id2++) {
const [x5, y10, partScore] = await max2d(stack[id2], config3.body.minConfidence);
if (partScore > (config3.body.minConfidence || 0)) {
cache2.keypoints.push({
score: Math.round(100 * partScore) / 100,
part: kpt2[id2],
positionRaw: [
// normalized to 0..1
// @ts-ignore model is not undefined here
x5 / model4.inputs[0].shape[2],
y10 / model4.inputs[0].shape[1]
],
position: [
// normalized to input image size
// @ts-ignore model is not undefined here
Math.round(image.shape[2] * x5 / model4.inputs[0].shape[2]),
Math.round(image.shape[1] * y10 / model4.inputs[0].shape[1])
]
});
}
}
stack.forEach((s) => Ot(s));
}
cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const x = cache2.keypoints.map((a) => a.position[0]);
const y5 = cache2.keypoints.map((a) => a.position[1]);
cache2.box = [
Math.min(...x),
Math.min(...y5),
Math.max(...x) - Math.min(...x),
Math.max(...y5) - Math.min(...y5)
];
const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]);
const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]);
cache2.boxRaw = [
Math.min(...xRaw),
Math.min(...yRaw),
Math.max(...xRaw) - Math.min(...xRaw),
Math.max(...yRaw) - Math.min(...yRaw)
];
for (const [name, indexes] of Object.entries(connected2)) {
const pt2 = [];
for (let i = 0; i < indexes.length - 1; i++) {
const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]);
const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
pt2.push([pt0.position, pt1.position]);
}
cache2.annotations[name] = pt2;
}
resolve([cache2]);
});
}
// src/face/facemeshutil.ts
var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];
var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];
var clampBox = (box, input) => box ? [
Math.trunc(Math.max(0, box.startPoint[0])),
Math.trunc(Math.max(0, box.startPoint[1])),
Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])),
Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1]))
] : [0, 0, 0, 0];
var getRawBox = (box, input) => box ? [
box.startPoint[0] / (input.shape[2] || 0),
box.startPoint[1] / (input.shape[1] || 0),
(box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),
(box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0)
] : [0, 0, 0, 0];
var scaleBoxCoordinates = (box, factor, anchor) => {
const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
const landmarks = box.landmarks.map((pt2) => [(pt2[0] + anchor[0]) * factor[0], (pt2[1] + anchor[1]) * factor[1]]);
return { startPoint, endPoint, landmarks, confidence: box.confidence };
};
var cutAndResize = (box, image, cropSize) => {
const h = image.shape[1];
const w = image.shape[2];
const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];
const crop = uj.cropAndResize(image, [cutBox], [0], cropSize);
const norm = Ke(crop, constants.tf255);
Ot(crop);
return norm;
};
var enlargeBox = (box, factor) => {
const center = getBoxCenter(box);
const size2 = getBoxSize(box);
const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
return {
startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]],
endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]],
landmarks: box.landmarks,
confidence: box.confidence,
size: size2
};
};
var squarifyBox = (box) => {
const centers = getBoxCenter(box);
const size2 = getBoxSize(box);
const halfSize = Math.max(...size2) / 2;
return {
startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)],
endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)],
landmarks: box.landmarks,
confidence: box.confidence,
size: [Math.round(size2[0]), Math.round(size2[1])]
};
};
var calculateLandmarksBoundingBox = (landmarks) => {
const x = landmarks.map((d) => d[0]);
const y5 = landmarks.map((d) => d[1]);
return {
startPoint: [Math.min(...x), Math.min(...y5)],
endPoint: [Math.max(...x), Math.max(...y5)],
landmarks
};
};
var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));
var buildTranslationMatrix = (x, y5) => [[1, 0, x], [0, 1, y5], [0, 0, 1]];
var dot = (v12, v22) => {
let product = 0;
for (let i = 0; i < v12.length; i++)
product += v12[i] * v22[i];
return product;
};
var getColumnFrom2DArr = (arr, columnIndex) => {
const column = [];
for (let i = 0; i < arr.length; i++)
column.push(arr[i][columnIndex]);
return column;
};
var multiplyTransformMatrices = (mat1, mat2) => {
const product = [];
const size2 = mat1.length;
for (let row = 0; row < size2; row++) {
product.push([]);
for (let col = 0; col < size2; col++)
product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));
}
return product;
};
var buildRotationMatrix = (rotation, center) => {
const cosA = Math.cos(rotation);
const sinA = Math.sin(rotation);
const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
const translationMatrix = buildTranslationMatrix(center[0], center[1]);
const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);
const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);
return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);
};
var invertTransformMatrix = (matrix) => {
const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
const translationComponent = [matrix[0][2], matrix[1][2]];
const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];
return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];
};
var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];
function generateAnchors(inputSize10) {
const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] };
const anchors3 = [];
for (let i = 0; i < spec.strides.length; i++) {
const stride = spec.strides[i];
const gridRows = Math.floor((inputSize10 + stride - 1) / stride);
const gridCols = Math.floor((inputSize10 + stride - 1) / stride);
const anchorsNum = spec.anchors[i];
for (let gridY = 0; gridY < gridRows; gridY++) {
const anchorY = stride * (gridY + 0.5);
for (let gridX = 0; gridX < gridCols; gridX++) {
const anchorX = stride * (gridX + 0.5);
for (let n = 0; n < anchorsNum; n++)
anchors3.push([anchorX, anchorY]);
}
}
}
return anchors3;
}
function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) {
const boxSize = getBoxSize(box);
const coordsScaled = coordsRaw.map((coord) => [
// scaled around zero-point
boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2),
boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2),
coord[2] || 0
]);
const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;
const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled;
const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;
const boxCenter = getBoxCenter(box);
const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];
return coordsRotated.map((coord) => [
Math.trunc(coord[0] + offsets[0]),
Math.trunc(coord[1] + offsets[1]),
Math.trunc(coord[2] || 0)
]);
}
function correctFaceRotation(rotate, box, input, inputSize10) {
const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
let angle = 0;
let rotationMatrix = fixedRotationMatrix;
let face4;
if (rotate && env.kernels.includes("rotatewithoffset")) {
angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);
const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
if (largeAngle) {
const center = getBoxCenter(box);
const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]];
const rotated = uj.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);
rotationMatrix = buildRotationMatrix(-angle, center);
face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
Ot(rotated);
} else {
face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
}
} else {
face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
}
return [angle, rotationMatrix, face4];
}
var findFaceCenter = (mesh) => {
const x = mesh.map((m) => m[0]);
const y5 = mesh.map((m) => m[1]);
return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y5) + (Math.max(...y5) - Math.min(...y5)) / 2];
};
var calculateFaceBox = (mesh, previousBox) => {
const center = findFaceCenter(mesh);
const boxSize = getBoxSize(previousBox);
const calculatedBox = {
startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2],
endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2]
};
return calculatedBox;
};
// src/face/blazeface.ts
var keypointsCount = 6;
var faceBoxScaleFactor = 1.4;
var model5;
var anchors = null;
var inputSize4 = 0;
var inputSizeT = null;
var size = () => inputSize4;
async function load3(config3) {
var _a2;
if (env.initial)
model5 = null;
if (!model5)
model5 = await loadModel((_a2 = config3.face.detector) == null ? void 0 : _a2.modelPath);
else if (config3.debug)
log("cached model:", model5["modelUrl"]);
inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256;
inputSizeT = ke(inputSize4, "int32");
anchors = uu(generateAnchors(inputSize4));
return model5;
}
function decodeBoxes2(boxOutputs) {
if (!anchors || !inputSizeT)
return Wr([0, 0]);
const t10 = {};
t10.boxStarts = qe(boxOutputs, [0, 1], [-1, 2]);
t10.centers = be(t10.boxStarts, anchors);
t10.boxSizes = qe(boxOutputs, [0, 3], [-1, 2]);
t10.boxSizesNormalized = Ke(t10.boxSizes, inputSizeT);
t10.centersNormalized = Ke(t10.centers, inputSizeT);
t10.halfBoxSize = Ke(t10.boxSizesNormalized, constants.tf2);
t10.starts = Te(t10.centersNormalized, t10.halfBoxSize);
t10.ends = be(t10.centersNormalized, t10.halfBoxSize);
t10.startNormalized = se(t10.starts, inputSizeT);
t10.endNormalized = se(t10.ends, inputSizeT);
const boxes = Dk([t10.startNormalized, t10.endNormalized], 1);
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return boxes;
}
async function getBoxes(inputImage, config3) {
var _a2, _b, _c2, _d2, _e, _f2;
if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return [];
const t10 = {};
t10.resized = uj.resizeBilinear(inputImage, [inputSize4, inputSize4]);
t10.div = Ke(t10.resized, constants.tf127);
t10.normalized = Te(t10.div, constants.tf05);
const res = model5 == null ? void 0 : model5.execute(t10.normalized);
if (Array.isArray(res) && res.length > 2) {
const sorted = res.sort((a, b) => a.size - b.size);
t10.concat384 = yt([sorted[0], sorted[2]], 2);
t10.concat512 = yt([sorted[1], sorted[3]], 2);
t10.concat = yt([t10.concat512, t10.concat384], 1);
t10.batch = lc(t10.concat, [0]);
} else if (Array.isArray(res)) {
t10.batch = lc(res[0]);
} else {
t10.batch = lc(res);
}
Ot(res);
t10.boxes = decodeBoxes2(t10.batch);
t10.logits = qe(t10.batch, [0, 0], [-1, 1]);
t10.sigmoid = wa(t10.logits);
t10.scores = lc(t10.sigmoid);
t10.nms = await uj.nonMaxSuppressionAsync(t10.boxes, t10.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c2 = config3.face.detector) == null ? void 0 : _c2.minConfidence) || 0);
const nms = await t10.nms.array();
const boxes = [];
const scores = await t10.scores.data();
for (let i = 0; i < nms.length; i++) {
const confidence = scores[nms[i]];
if (confidence > (((_d2 = config3.face.detector) == null ? void 0 : _d2.minConfidence) || 0)) {
const b = {};
b.bbox = qe(t10.boxes, [nms[i], 0], [1, -1]);
b.slice = qe(t10.batch, [nms[i], keypointsCount - 1], [1, -1]);
b.squeeze = lc(b.slice);
b.landmarks = W(b.squeeze, [keypointsCount, -1]);
const points = await b.bbox.data();
const rawBox = {
startPoint: [points[0], points[1]],
endPoint: [points[2], points[3]],
landmarks: await b.landmarks.array(),
confidence
};
b.anchor = qe(anchors, [nms[i], 0], [1, 2]);
const anchor = await b.anchor.data();
const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4], anchor);
const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
const squaredBox = squarifyBox(enlargedBox);
if (squaredBox.size[0] > (((_e = config3.face.detector) == null ? void 0 : _e["minSize"]) || 0) && squaredBox.size[1] > (((_f2 = config3.face.detector) == null ? void 0 : _f2["minSize"]) || 0))
boxes.push(squaredBox);
Object.keys(b).forEach((tensor) => Ot(b[tensor]));
}
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return boxes;
}
// src/face/iris.ts
var model6;
var inputSize5 = 0;
var irisEnlarge = 2.3;
var leftOutline = meshAnnotations.leftEyeLower0;
var rightOutline = meshAnnotations.rightEyeLower0;
var eyeLandmarks = {
leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],
rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]]
};
var irisLandmarks = {
upperCenter: 3,
lowerCenter: 4,
index: 71,
numCoordinates: 76
};
async function load4(config3) {
var _a2, _b;
if (env.initial)
model6 = null;
if (!model6)
model6 = await loadModel((_a2 = config3.face.iris) == null ? void 0 : _a2.modelPath);
else if (config3.debug)
log("cached model:", model6["modelUrl"]);
inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0;
if (inputSize5 === -1)
inputSize5 = 64;
return model6;
}
function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {
for (let i = 0; i < irisIndices.length; i++) {
const { key, indices } = irisIndices[i];
const originalIndices = meshAnnotations[`${prefix}${key}`];
if (!keys || keys.includes(key)) {
for (let j = 0; j < indices.length; j++) {
const index2 = indices[j];
rawCoords[originalIndices[j]] = [
newCoords[index2][0],
newCoords[index2][1],
(newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2
];
}
}
}
}
var getLeftToRightEyeDepthDifference = (rawCoords) => {
const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
return leftEyeZ - rightEyeZ;
};
var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
const boxSize = getBoxSize(box);
let crop = uj.cropAndResize(face4, [[
box.startPoint[1] / meshSize,
box.startPoint[0] / meshSize,
box.endPoint[1] / meshSize,
box.endPoint[0] / meshSize
]], [0], [inputSize5, inputSize5]);
if (flip && env.kernels.includes("flipleftright")) {
const flipped = uj.flipLeftRight(crop);
Ot(crop);
crop = flipped;
}
return { box, boxSize, crop };
};
var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
const eyeRawCoords = [];
for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
const x = eyeData[i * 3];
const y5 = eyeData[i * 3 + 1];
const z = eyeData[i * 3 + 2];
eyeRawCoords.push([
(flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
y5 / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
z
]);
}
return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
};
var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
const averageZ = (upperCenterZ + lowerCenterZ) / 2;
return irisCoords.map((coord, i) => {
let z = averageZ;
if (i === 2) {
z = upperCenterZ;
} else if (i === 4) {
z = lowerCenterZ;
}
return [coord[0], coord[1], z];
});
};
async function augmentIris(rawCoords, face4, meshSize) {
if (!(model6 == null ? void 0 : model6["executor"]))
return rawCoords;
const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);
const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);
const combined = yt([leftEyeCrop, rightEyeCrop]);
Ot(leftEyeCrop);
Ot(rightEyeCrop);
const eyePredictions = model6.execute(combined);
Ot(combined);
const eyePredictionsData = await eyePredictions.data();
Ot(eyePredictions);
const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);
const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);
if (Math.abs(leftToRightEyeDepthDifference) < 30) {
replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null);
replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null);
} else if (leftToRightEyeDepthDifference < 1) {
replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
} else {
replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]);
}
const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left");
const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right");
const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
return newCoords;
}
// src/face/attention.ts
async function augment(rawCoords, results) {
var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2;
const t10 = {
// all attention models produce 2d results so it needs to be later augmented with correct z-coords
// mesh: results[0], // already have it in rawCoords // output_mesh_identity
// flag: results[1], // already processed in parent // conv_faceflag
lips: await ((_b = (_a2 = results.filter((r) => r.size === 160)) == null ? void 0 : _a2[0]) == null ? void 0 : _b.data()),
// 80 x 2d = 160 // output_lips
irisL: await ((_d2 = (_c2 = results.filter((r) => r.size === 10)) == null ? void 0 : _c2[0]) == null ? void 0 : _d2.data()),
// 5 x 2d = 10 // output_right_iris
eyeL: await ((_f2 = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f2.data()),
// 71 x 2d = 142 // output_right_eye
irisR: await ((_h2 = (_g2 = results.filter((r) => r.size === 10)) == null ? void 0 : _g2[1]) == null ? void 0 : _h2.data()),
// 5 x 2d = 10 // output_left_iris
eyeR: await ((_j2 = (_i2 = results.filter((r) => r.size === 142)) == null ? void 0 : _i2[1]) == null ? void 0 : _j2.data())
// 71 x 2d = 142// output_left_eye
};
for (const val of Object.values(t10)) {
if (!val)
return rawCoords;
}
const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length;
for (let i = 0; i < t10.irisL.length / 2; i++)
rawCoords.push([t10.irisL[2 * i + 0], t10.irisL[2 * i + 1], irisLDepth]);
const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length;
for (let i = 0; i < t10.irisR.length / 2; i++)
rawCoords.push([t10.irisR[2 * i + 0], t10.irisR[2 * i + 1], irisRDepth]);
for (let i = 0; i < t10.eyeL.length / 2; i++)
rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t10.eyeL[2 * i + 0], t10.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];
for (let i = 0; i < t10.eyeR.length / 2; i++)
rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t10.eyeR[2 * i + 0], t10.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];
for (let i = 0; i < t10.lips.length / 2; i++)
rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t10.lips[2 * i + 0], t10.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];
return rawCoords;
}
// src/face/facemesh.ts
var cache3 = {
boxes: [],
skipped: Number.MAX_SAFE_INTEGER,
timestamp: 0
};
var model7 = null;
var inputSize6 = 0;
async function predict4(input, config3) {
var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2;
const skipTime = (((_a2 = config3.face.detector) == null ? void 0 : _a2.skipTime) || 0) > now() - cache3.timestamp;
const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
cache3.boxes = await getBoxes(input, config3);
cache3.timestamp = now();
cache3.skipped = 0;
} else {
cache3.skipped++;
}
const faces = [];
const newCache = [];
let id2 = 0;
const size2 = inputSize6;
for (let i = 0; i < cache3.boxes.length; i++) {
const box = cache3.boxes[i];
let angle = 0;
let rotationMatrix;
const face4 = {
// init face result
id: id2++,
mesh: [],
meshRaw: [],
box: [0, 0, 0, 0],
boxRaw: [0, 0, 0, 0],
score: 0,
boxScore: 0,
faceScore: 0,
size: [0, 0],
// contoursRaw: [],
// contours: [],
annotations: {}
};
[angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c2 = config3.face.detector) == null ? void 0 : _c2.rotation, box, input, ((_d2 = config3.face.mesh) == null ? void 0 : _d2.enabled) ? inputSize6 : size());
if (config3.filter.equalization) {
const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0;
Ot(face4.tensor);
if (equilized)
face4.tensor = equilized;
}
face4.boxScore = Math.round(100 * box.confidence) / 100;
if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled) || !(model7 == null ? void 0 : model7["executor"])) {
face4.box = clampBox(box, input);
face4.boxRaw = getRawBox(box, input);
face4.score = face4.boxScore;
face4.size = box.size;
face4.mesh = box.landmarks;
face4.meshRaw = face4.mesh.map((pt2) => [pt2[0] / (input.shape[2] || 0), pt2[1] / (input.shape[1] || 0), (pt2[2] || 0) / size2]);
for (const key of Object.keys(blazeFaceLandmarks))
face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
} else if (!model7) {
if (config3.debug)
log("face mesh detection requested, but model is not loaded");
} else {
if (((_f2 = config3.face.attention) == null ? void 0 : _f2.enabled) && !env.kernels.includes("atan2")) {
config3.face.attention.enabled = false;
Ot(face4.tensor);
return faces;
}
const results = model7.execute(face4.tensor);
const confidenceT = results.find((t10) => t10.shape[t10.shape.length - 1] === 1);
const faceConfidence = await confidenceT.data();
face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
if (face4.faceScore < (((_g2 = config3.face.detector) == null ? void 0 : _g2.minConfidence) || 1)) {
box.confidence = face4.faceScore;
if (config3.face.mesh["keepInvalid"]) {
face4.box = clampBox(box, input);
face4.boxRaw = getRawBox(box, input);
face4.score = face4.boxScore;
face4.mesh = box.landmarks;
face4.meshRaw = face4.mesh.map((pt2) => [pt2[0] / (input.shape[2] || 1), pt2[1] / (input.shape[1] || 1), (pt2[2] || 0) / size2]);
for (const key of Object.keys(blazeFaceLandmarks)) {
face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
}
}
} else {
const meshT = results.find((t10) => t10.shape[t10.shape.length - 1] === 1404);
const coordsReshaped = W(meshT, [-1, 3]);
let rawCoords = await coordsReshaped.array();
Ot(coordsReshaped);
if ((_h2 = config3.face.attention) == null ? void 0 : _h2.enabled) {
rawCoords = await augment(rawCoords, results);
} else if ((_i2 = config3.face.iris) == null ? void 0 : _i2.enabled) {
rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6);
}
face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6);
face4.meshRaw = face4.mesh.map((pt2) => [pt2[0] / (input.shape[2] || 0), pt2[1] / (input.shape[1] || 0), (pt2[2] || 0) / size2]);
for (const key of Object.keys(meshAnnotations))
face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]);
face4.score = face4.faceScore;
const calculatedBox = {
...calculateFaceBox(face4.mesh, box),
confidence: box.confidence,
landmarks: box.landmarks,
size: box.size
};
face4.box = clampBox(calculatedBox, input);
face4.boxRaw = getRawBox(calculatedBox, input);
newCache.push(calculatedBox);
}
Ot(results);
}
if (face4.score > (((_j2 = config3.face.detector) == null ? void 0 : _j2.minConfidence) || 1))
faces.push(face4);
else
Ot(face4.tensor);
}
cache3.boxes = newCache;
return faces;
}
async function load5(config3) {
var _a2, _b, _c2, _d2, _e, _f2;
if (env.initial)
model7 = null;
if (((_a2 = config3.face.attention) == null ? void 0 : _a2.enabled) && (model7 == null ? void 0 : model7["signature"])) {
if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6)
model7 = null;
}
if (!model7) {
if ((_c2 = config3.face.attention) == null ? void 0 : _c2.enabled)
model7 = await loadModel(config3.face.attention.modelPath);
else
model7 = await loadModel((_d2 = config3.face.mesh) == null ? void 0 : _d2.modelPath);
} else if (config3.debug) {
log("cached model:", model7["modelUrl"]);
}
inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f2 = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f2[0].shape[2] : 256;
return model7;
}
var triangulation = TRI468;
var uvmap = UV468;
// src/gear/emotion.ts
var annotations = [];
var model8;
var last3 = [];
var lastCount = 0;
var lastTime4 = 0;
var skipped4 = Number.MAX_SAFE_INTEGER;
var rgb = false;
async function load6(config3) {
var _a2, _b, _c2;
if (env.initial)
model8 = null;
if (!model8) {
model8 = await loadModel((_a2 = config3.face.emotion) == null ? void 0 : _a2.modelPath);
rgb = ((_c2 = (_b = model8 == null ? void 0 : model8.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c2[3]) === 3;
if (!rgb)
annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
else
annotations = ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"];
} else if (config3.debug) {
log("cached model:", model8["modelUrl"]);
}
return model8;
}
async function predict5(image, config3, idx, count2) {
var _a2, _b;
if (!model8)
return [];
const skipFrame = skipped4 < (((_a2 = config3.face.emotion) == null ? void 0 : _a2.skipFrames) || 0);
const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4;
if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) {
skipped4++;
return last3[idx];
}
skipped4 = 0;
return new Promise(async (resolve) => {
var _a3, _b2, _c2;
const obj = [];
if ((_a3 = config3.face.emotion) == null ? void 0 : _a3.enabled) {
const t10 = {};
const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0;
if (((_b2 = config3.face.emotion) == null ? void 0 : _b2["crop"]) > 0) {
const crop = (_c2 = config3.face.emotion) == null ? void 0 : _c2["crop"];
const box = [[crop, crop, 1 - crop, 1 - crop]];
t10.resize = uj.cropAndResize(image, box, [0], [inputSize10, inputSize10]);
} else {
t10.resize = uj.resizeBilinear(image, [inputSize10, inputSize10], false);
}
if (rgb) {
t10.mul = se(t10.resize, 255);
t10.normalize = Te(t10.mul, [103.939, 116.779, 123.68]);
t10.emotion = model8 == null ? void 0 : model8.execute(t10.normalize);
} else {
t10.channels = se(t10.resize, constants.rgb);
t10.grayscale = ot(t10.channels, 3, true);
t10.grayscaleSub = Te(t10.grayscale, constants.tf05);
t10.grayscaleMul = se(t10.grayscaleSub, constants.tf2);
t10.emotion = model8 == null ? void 0 : model8.execute(t10.grayscaleMul);
}
lastTime4 = now();
const data = await t10.emotion.data();
for (let i = 0; i < data.length; i++) {
if (data[i] > (config3.face.emotion.minConfidence || 0))
obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
}
obj.sort((a, b) => b.score - a.score);
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
}
last3[idx] = obj;
lastCount = count2;
resolve(obj);
});
}
// src/face/faceres.ts
var model9;
var last4 = [];
var lastTime5 = 0;
var lastCount2 = 0;
var skipped5 = Number.MAX_SAFE_INTEGER;
async function load7(config3) {
var _a2;
if (env.initial)
model9 = null;
if (!model9)
model9 = await loadModel((_a2 = config3.face.description) == null ? void 0 : _a2.modelPath);
else if (config3.debug)
log("cached model:", model9["modelUrl"]);
return model9;
}
function enhance(input, config3) {
var _a2, _b;
const tensor = input.image || input.tensor || input;
if (!(model9 == null ? void 0 : model9.inputs[0].shape))
return tensor;
let crop;
if (((_a2 = config3.face.description) == null ? void 0 : _a2["crop"]) > 0) {
const cropval = (_b = config3.face.description) == null ? void 0 : _b["crop"];
const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];
crop = uj.cropAndResize(tensor, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]);
} else {
crop = uj.resizeBilinear(tensor, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
}
const norm = se(crop, constants.tf255);
Ot(crop);
return norm;
}
async function predict6(image, config3, idx, count2) {
var _a2, _b, _c2, _d2;
const obj = {
age: 0,
gender: "unknown",
genderScore: 0,
descriptor: []
};
if (!(model9 == null ? void 0 : model9["executor"]))
return obj;
const skipFrame = skipped5 < (((_a2 = config3.face.description) == null ? void 0 : _a2.skipFrames) || 0);
const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c2 = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c2.age) > 0 && ((_d2 = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d2.genderScore) > 0) {
skipped5++;
return last4[idx];
}
skipped5 = 0;
return new Promise(async (resolve) => {
var _a3;
if ((_a3 = config3.face.description) == null ? void 0 : _a3.enabled) {
const enhanced = enhance(image, config3);
const resT = model9 == null ? void 0 : model9.execute(enhanced);
lastTime5 = now();
Ot(enhanced);
const genderT = resT.find((t10) => t10.shape[1] === 1);
const gender2 = await genderT.data();
const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100;
if (confidence > (config3.face.description.minConfidence || 0)) {
obj.gender = gender2[0] <= 0.5 ? "female" : "male";
obj.genderScore = Math.min(0.99, confidence);
}
const argmax = mk(resT.find((t10) => t10.shape[1] === 100), 1);
const ageIdx = (await argmax.data())[0];
Ot(argmax);
const ageT = resT.find((t10) => t10.shape[1] === 100);
const all2 = await ageT.data();
obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10;
if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0]))
log("faceres error:", { model: model9, result: resT });
const desc = resT.find((t10) => t10.shape[1] === 1024);
const descriptor = desc ? await desc.data() : [];
obj.descriptor = Array.from(descriptor);
resT.forEach((t10) => Ot(t10));
}
last4[idx] = obj;
lastCount2 = count2;
resolve(obj);
});
}
// src/face/mask.ts
var expandFact = 0.1;
var alpha = 0.5;
function insidePoly(x, y5, polygon) {
let inside = false;
let j = polygon.length - 1;
for (let i = 0; i < polygon.length; j = i++) {
if (polygon[i].y > y5 !== polygon[j].y > y5 && x < (polygon[j].x - polygon[i].x) * (y5 - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)
inside = !inside;
}
return inside;
}
async function mask(face4) {
if (!face4.tensor)
return face4.tensor;
if (!face4.mesh || face4.mesh.length < 100)
return face4.tensor;
const width = face4.tensor.shape[2] || 0;
const height = face4.tensor.shape[1] || 0;
const buffer = await face4.tensor.buffer();
let silhouette = [];
for (const pt2 of meshAnnotations.silhouette)
silhouette.push({ x: (face4.mesh[pt2][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt2][1] - face4.box[1]) / face4.box[3] });
if (expandFact && expandFact > 0)
silhouette = silhouette.map((pt2) => ({ x: pt2.x > 0.5 ? pt2.x + expandFact : pt2.x - expandFact, y: pt2.y > 0.5 ? pt2.y + expandFact : pt2.y - expandFact }));
for (let x = 0; x < width; x++) {
for (let y5 = 0; y5 < height; y5++) {
const inside = insidePoly(x / width, y5 / width, silhouette);
if (!inside) {
buffer.set(alpha * buffer.get(0, y5, x, 0), 0, y5, x, 0);
buffer.set(alpha * buffer.get(0, y5, x, 1), 0, y5, x, 1);
buffer.set(alpha * buffer.get(0, y5, x, 2), 0, y5, x, 2);
}
}
}
const output = buffer.toTensor();
return output;
}
// src/face/antispoof.ts
var model10;
var cached = [];
var skipped6 = Number.MAX_SAFE_INTEGER;
var lastCount3 = 0;
var lastTime6 = 0;
async function load8(config3) {
var _a2;
if (env.initial)
model10 = null;
if (!model10)
model10 = await loadModel((_a2 = config3.face.antispoof) == null ? void 0 : _a2.modelPath);
else if (config3.debug)
log("cached model:", model10["modelUrl"]);
return model10;
}
async function predict7(image, config3, idx, count2) {
var _a2, _b;
if (!(model10 == null ? void 0 : model10["executor"]))
return 0;
const skipTime = (((_a2 = config3.face.antispoof) == null ? void 0 : _a2.skipTime) || 0) > now() - lastTime6;
const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) {
skipped6++;
return cached[idx];
}
skipped6 = 0;
return new Promise(async (resolve) => {
const resize = uj.resizeBilinear(image, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false);
const res = model10 == null ? void 0 : model10.execute(resize);
const num = (await res.data())[0];
cached[idx] = Math.round(100 * num) / 100;
lastCount3 = count2;
lastTime6 = now();
Ot([resize, res]);
resolve(cached[idx]);
});
}
// src/face/liveness.ts
var model11;
var cached2 = [];
var skipped7 = Number.MAX_SAFE_INTEGER;
var lastCount4 = 0;
var lastTime7 = 0;
async function load9(config3) {
var _a2;
if (env.initial)
model11 = null;
if (!model11)
model11 = await loadModel((_a2 = config3.face.liveness) == null ? void 0 : _a2.modelPath);
else if (config3.debug)
log("cached model:", model11["modelUrl"]);
return model11;
}
async function predict8(image, config3, idx, count2) {
var _a2, _b;
if (!(model11 == null ? void 0 : model11["executor"]))
return 0;
const skipTime = (((_a2 = config3.face.liveness) == null ? void 0 : _a2.skipTime) || 0) > now() - lastTime7;
const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) {
skipped7++;
return cached2[idx];
}
skipped7 = 0;
return new Promise(async (resolve) => {
const resize = uj.resizeBilinear(image, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false);
const res = model11 == null ? void 0 : model11.execute(resize);
const num = (await res.data())[0];
cached2[idx] = Math.round(100 * num) / 100;
lastCount4 = count2;
lastTime7 = now();
Ot([resize, res]);
resolve(cached2[idx]);
});
}
// src/gear/gear.ts
var model12;
var last5 = [];
var raceNames = ["white", "black", "asian", "indian", "other"];
var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
var lastCount5 = 0;
var lastTime8 = 0;
var skipped8 = Number.MAX_SAFE_INTEGER;
async function load10(config3) {
var _a2;
if (env.initial)
model12 = null;
if (!model12)
model12 = await loadModel((_a2 = config3.face.gear) == null ? void 0 : _a2.modelPath);
else if (config3.debug)
log("cached model:", model12["modelUrl"]);
return model12;
}
async function predict9(image, config3, idx, count2) {
var _a2, _b;
if (!model12)
return { age: 0, gender: "unknown", genderScore: 0, race: [] };
const skipFrame = skipped8 < (((_a2 = config3.face.gear) == null ? void 0 : _a2.skipFrames) || 0);
const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8;
if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) {
skipped8++;
return last5[idx];
}
skipped8 = 0;
return new Promise(async (resolve) => {
var _a3, _b2, _c2, _d2;
if (!(model12 == null ? void 0 : model12.inputs[0].shape))
return;
const t10 = {};
let box = [[0, 0.1, 0.9, 0.9]];
if (((_a3 = config3.face.gear) == null ? void 0 : _a3["crop"]) > 0) {
const crop = (_b2 = config3.face.gear) == null ? void 0 : _b2["crop"];
box = [[crop, crop, 1 - crop, 1 - crop]];
}
t10.resize = uj.cropAndResize(image, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]);
const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
if ((_c2 = config3.face.gear) == null ? void 0 : _c2.enabled)
[t10.age, t10.gender, t10.race] = model12.execute(t10.resize, ["age_output", "gender_output", "race_output"]);
const gender2 = await t10.gender.data();
obj.gender = gender2[0] > gender2[1] ? "male" : "female";
obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
const race = await t10.race.data();
for (let i = 0; i < race.length; i++) {
if (race[i] > (((_d2 = config3.face.gear) == null ? void 0 : _d2.minConfidence) || 0.2))
obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
}
obj.race.sort((a, b) => b.score - a.score);
const ageDistribution = Array.from(await t10.age.data());
const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);
let age2 = ageSorted[0][0];
for (let i = 1; i < ageSorted.length; i++)
age2 += ageSorted[i][1] * (ageSorted[i][0] - age2);
obj.age = Math.round(10 * age2) / 10;
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
last5[idx] = obj;
lastCount5 = count2;
lastTime8 = now();
resolve(obj);
});
}
// src/gear/ssrnet-age.ts
var model13;
var last6 = [];
var lastCount6 = 0;
var lastTime9 = 0;
var skipped9 = Number.MAX_SAFE_INTEGER;
async function load11(config3) {
if (env.initial)
model13 = null;
if (!model13)
model13 = await loadModel(config3.face["ssrnet"].modelPathAge);
else if (config3.debug)
log("cached model:", model13["modelUrl"]);
return model13;
}
async function predict10(image, config3, idx, count2) {
var _a2, _b, _c2, _d2;
if (!model13)
return { age: 0 };
const skipFrame = skipped9 < (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.skipFrames) || 0);
const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9;
if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c2 = last6[idx]) == null ? void 0 : _c2.age) && ((_d2 = last6[idx]) == null ? void 0 : _d2.age) > 0) {
skipped9++;
return last6[idx];
}
skipped9 = 0;
return new Promise(async (resolve) => {
var _a3, _b2, _c3;
if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape)
return;
const t10 = {};
if (((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3["crop"]) > 0) {
const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"];
const box = [[crop, crop, 1 - crop, 1 - crop]];
t10.resize = uj.cropAndResize(image, box, [0], [model13.inputs[0].shape[2], model13.inputs[0].shape[1]]);
} else {
t10.resize = uj.resizeBilinear(image, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
}
t10.enhance = se(t10.resize, constants.tf255);
const obj = { age: 0 };
if ((_c3 = config3.face["ssrnet"]) == null ? void 0 : _c3.enabled)
t10.age = model13.execute(t10.enhance);
if (t10.age) {
const data = await t10.age.data();
obj.age = Math.trunc(10 * data[0]) / 10;
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
last6[idx] = obj;
lastCount6 = count2;
lastTime9 = now();
resolve(obj);
});
}
// src/gear/ssrnet-gender.ts
var model14;
var last7 = [];
var lastCount7 = 0;
var lastTime10 = 0;
var skipped10 = Number.MAX_SAFE_INTEGER;
var rgb2 = [0.2989, 0.587, 0.114];
async function load12(config3) {
var _a2;
if (env.initial)
model14 = null;
if (!model14)
model14 = await loadModel((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.modelPathGender);
else if (config3.debug)
log("cached model:", model14["modelUrl"]);
return model14;
}
async function predict11(image, config3, idx, count2) {
var _a2, _b, _c2, _d2;
if (!model14)
return { gender: "unknown", genderScore: 0 };
const skipFrame = skipped10 < (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.skipFrames) || 0);
const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10;
if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c2 = last7[idx]) == null ? void 0 : _c2.gender) && ((_d2 = last7[idx]) == null ? void 0 : _d2.genderScore) > 0) {
skipped10++;
return last7[idx];
}
skipped10 = 0;
return new Promise(async (resolve) => {
var _a3, _b2, _c3;
if (!(model14 == null ? void 0 : model14.inputs[0].shape))
return;
const t10 = {};
if (((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3["crop"]) > 0) {
const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"];
const box = [[crop, crop, 1 - crop, 1 - crop]];
t10.resize = uj.cropAndResize(image, box, [0], [model14.inputs[0].shape[2], model14.inputs[0].shape[1]]);
} else {
t10.resize = uj.resizeBilinear(image, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false);
}
t10.enhance = De(() => {
var _a4, _b3;
let normalize2;
if (((_b3 = (_a4 = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a4[0].shape) == null ? void 0 : _b3[3]) === 1) {
const [red, green, blue] = ai(t10.resize, 3, 3);
const redNorm = se(red, rgb2[0]);
const greenNorm = se(green, rgb2[1]);
const blueNorm = se(blue, rgb2[2]);
const grayscale = pk([redNorm, greenNorm, blueNorm]);
normalize2 = se(Te(grayscale, constants.tf05), 2);
} else {
normalize2 = se(Te(t10.resize, constants.tf05), 2);
}
return normalize2;
});
const obj = { gender: "unknown", genderScore: 0 };
if ((_c3 = config3.face["ssrnet"]) == null ? void 0 : _c3.enabled)
t10.gender = model14.execute(t10.enhance);
const data = await t10.gender.data();
obj.gender = data[0] > data[1] ? "female" : "male";
obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
last7[idx] = obj;
lastCount7 = count2;
lastTime10 = now();
resolve(obj);
});
}
// src/face/mobilefacenet.ts
var model15;
var last8 = [];
var lastCount8 = 0;
var lastTime11 = 0;
var skipped11 = Number.MAX_SAFE_INTEGER;
async function load13(config3) {
var _a2;
if (env.initial)
model15 = null;
if (!model15)
model15 = await loadModel((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.modelPath);
else if (config3.debug)
log("cached model:", model15["modelUrl"]);
return model15;
}
async function predict12(input, config3, idx, count2) {
var _a2, _b;
if (!(model15 == null ? void 0 : model15["executor"]))
return [];
const skipFrame = skipped11 < (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.skipFrames) || 0);
const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11;
if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) {
skipped11++;
return last8[idx];
}
return new Promise(async (resolve) => {
var _a3;
let data = [];
if (((_a3 = config3.face["mobilefacenet"]) == null ? void 0 : _a3.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) {
const t10 = {};
t10.crop = uj.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false);
t10.data = model15.execute(t10.crop);
const output = await t10.data.data();
data = Array.from(output);
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
}
last8[idx] = data;
lastCount8 = count2;
lastTime11 = now();
resolve(data);
});
}
// src/face/insightface.ts
var model16;
var last9 = [];
var lastCount9 = 0;
var lastTime12 = 0;
var skipped12 = Number.MAX_SAFE_INTEGER;
async function load14(config3) {
if (env.initial)
model16 = null;
if (!model16)
model16 = await loadModel(config3.face["insightface"].modelPath);
else if (config3.debug)
log("cached model:", model16["modelUrl"]);
return model16;
}
async function predict13(input, config3, idx, count2) {
var _a2, _b;
if (!(model16 == null ? void 0 : model16["executor"]))
return [];
const skipFrame = skipped12 < (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.skipFrames) || 0);
const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12;
if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) {
skipped12++;
return last9[idx];
}
return new Promise(async (resolve) => {
var _a3;
let data = [];
if (((_a3 = config3.face["insightface"]) == null ? void 0 : _a3.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) {
const t10 = {};
t10.crop = uj.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false);
t10.data = model16.execute(t10.crop);
const output = await t10.data.data();
data = Array.from(output);
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
}
last9[idx] = data;
lastCount9 = count2;
lastTime12 = now();
resolve(data);
});
}
// src/face/angles.ts
var calculateGaze = (face4) => {
const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris)
return { bearing: 0, strength: 0 };
const offsetIris = [0, -0.1];
const eyeRatio = 1;
const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0);
const irisCenter = left ? face4.mesh[473] : face4.mesh[468];
const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2];
const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]];
const eyeDiff = [
// x distance between extreme point and center point normalized with eye size
(eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],
eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1]
];
let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]);
strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2);
const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI;
return { bearing, strength };
};
var calculateFaceAngle = (face4, imageSize) => {
const normalize2 = (v5) => {
const length = Math.sqrt(v5[0] * v5[0] + v5[1] * v5[1] + v5[2] * v5[2]);
v5[0] /= length;
v5[1] /= length;
v5[2] /= length;
return v5;
};
const subVectors = (a, b) => {
const x = a[0] - b[0];
const y5 = a[1] - b[1];
const z = a[2] - b[2];
return [x, y5, z];
};
const crossVectors = (a, b) => {
const x = a[1] * b[2] - a[2] * b[1];
const y5 = a[2] * b[0] - a[0] * b[2];
const z = a[0] * b[1] - a[1] * b[0];
return [x, y5, z];
};
const rotationMatrixToEulerAngle = (r) => {
const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
let thetaX;
let thetaY;
let thetaZ;
if (r10 < 1) {
if (r10 > -1) {
thetaZ = Math.asin(r10);
thetaY = Math.atan2(-r20, r00);
thetaX = Math.atan2(-r12, r11);
} else {
thetaZ = -Math.PI / 2;
thetaY = -Math.atan2(r21, r22);
thetaX = 0;
}
} else {
thetaZ = Math.PI / 2;
thetaY = Math.atan2(r21, r22);
thetaX = 0;
}
if (Number.isNaN(thetaX))
thetaX = 0;
if (Number.isNaN(thetaY))
thetaY = 0;
if (Number.isNaN(thetaZ))
thetaZ = 0;
return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };
};
const mesh = face4.meshRaw;
if (!mesh || mesh.length < 300)
return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };
const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5;
const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt2) => [pt2[0] * imageSize[0] / size2, pt2[1] * imageSize[1] / size2, pt2[2]]);
const yAxis = normalize2(subVectors(pts[1], pts[0]));
let xAxis = normalize2(subVectors(pts[3], pts[2]));
const zAxis = normalize2(crossVectors(xAxis, yAxis));
xAxis = crossVectors(yAxis, zAxis);
const matrix = [
xAxis[0],
xAxis[1],
xAxis[2],
yAxis[0],
yAxis[1],
yAxis[2],
zAxis[0],
zAxis[1],
zAxis[2]
];
const angle = rotationMatrixToEulerAngle(matrix);
const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 };
return { angle, matrix, gaze };
};
// src/face/anthropometry.ts
function calculateCameraDistance(face4, width) {
const f = face4 == null ? void 0 : face4.annotations;
if (!(f == null ? void 0 : f.leftEyeIris) || !(f == null ? void 0 : f.rightEyeIris))
return 0;
const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;
const cameraDistance = Math.round(1.17 / irisSize) / 100;
return cameraDistance;
}
// src/face/face.ts
var detectFace = async (instance, input) => {
var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w;
let timeStamp = now();
let ageRes;
let gearRes;
let genderRes;
let emotionRes;
let mobilefacenetRes;
let insightfaceRes;
let antispoofRes;
let livenessRes;
let descRes;
const faceRes = [];
instance.state = "run:face";
const faces = await predict4(input, instance.config);
instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
if (!input.shape || input.shape.length !== 4)
return [];
if (!faces)
return [];
for (let i = 0; i < faces.length; i++) {
instance.analyze("Get Face");
if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {
log("Face object is disposed:", faces[i].tensor);
continue;
}
if ((_a2 = instance.config.face.detector) == null ? void 0 : _a2.mask) {
const masked = await mask(faces[i]);
Ot(faces[i].tensor);
if (masked)
faces[i].tensor = masked;
}
const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;
instance.analyze("Start Emotion:");
if (instance.config.async) {
emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || ir([]), instance.config, i, faces.length) : [];
} else {
instance.state = "run:emotion";
timeStamp = now();
emotionRes = ((_c2 = instance.config.face.emotion) == null ? void 0 : _c2.enabled) ? await predict5(faces[i].tensor || ir([]), instance.config, i, faces.length) : [];
instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
instance.analyze("End Emotion:");
instance.analyze("Start AntiSpoof:");
if (instance.config.async) {
antispoofRes = ((_d2 = instance.config.face.antispoof) == null ? void 0 : _d2.enabled) ? predict7(faces[i].tensor || ir([]), instance.config, i, faces.length) : 0;
} else {
instance.state = "run:antispoof";
timeStamp = now();
antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || ir([]), instance.config, i, faces.length) : 0;
instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
instance.analyze("End AntiSpoof:");
instance.analyze("Start Liveness:");
if (instance.config.async) {
livenessRes = ((_f2 = instance.config.face.liveness) == null ? void 0 : _f2.enabled) ? predict8(faces[i].tensor || ir([]), instance.config, i, faces.length) : 0;
} else {
instance.state = "run:liveness";
timeStamp = now();
livenessRes = ((_g2 = instance.config.face.liveness) == null ? void 0 : _g2.enabled) ? await predict8(faces[i].tensor || ir([]), instance.config, i, faces.length) : 0;
instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
instance.analyze("End Liveness:");
instance.analyze("Start GEAR:");
if (instance.config.async) {
gearRes = ((_h2 = instance.config.face.gear) == null ? void 0 : _h2.enabled) ? predict9(faces[i].tensor || ir([]), instance.config, i, faces.length) : null;
} else {
instance.state = "run:gear";
timeStamp = now();
gearRes = ((_i2 = instance.config.face.gear) == null ? void 0 : _i2.enabled) ? await predict9(faces[i].tensor || ir([]), instance.config, i, faces.length) : null;
instance.performance.gear = Math.trunc(now() - timeStamp);
}
instance.analyze("End GEAR:");
instance.analyze("Start SSRNet:");
if (instance.config.async) {
ageRes = ((_j2 = instance.config.face["ssrnet"]) == null ? void 0 : _j2.enabled) ? predict10(faces[i].tensor || ir([]), instance.config, i, faces.length) : null;
genderRes = ((_k2 = instance.config.face["ssrnet"]) == null ? void 0 : _k2.enabled) ? predict11(faces[i].tensor || ir([]), instance.config, i, faces.length) : null;
} else {
instance.state = "run:ssrnet";
timeStamp = now();
ageRes = ((_l2 = instance.config.face["ssrnet"]) == null ? void 0 : _l2.enabled) ? await predict10(faces[i].tensor || ir([]), instance.config, i, faces.length) : null;
genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || ir([]), instance.config, i, faces.length) : null;
instance.performance.ssrnet = Math.trunc(now() - timeStamp);
}
instance.analyze("End SSRNet:");
instance.analyze("Start MobileFaceNet:");
if (instance.config.async) {
mobilefacenetRes = ((_n2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _n2.enabled) ? predict12(faces[i].tensor || ir([]), instance.config, i, faces.length) : null;
} else {
instance.state = "run:mobilefacenet";
timeStamp = now();
mobilefacenetRes = ((_o2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _o2.enabled) ? await predict12(faces[i].tensor || ir([]), instance.config, i, faces.length) : null;
instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);
}
instance.analyze("End MobileFaceNet:");
instance.analyze("Start InsightFace:");
if (instance.config.async) {
insightfaceRes = ((_p2 = instance.config.face["insightface"]) == null ? void 0 : _p2.enabled) ? predict13(faces[i].tensor || ir([]), instance.config, i, faces.length) : null;
} else {
instance.state = "run:mobilefacenet";
timeStamp = now();
insightfaceRes = ((_q2 = instance.config.face["insightface"]) == null ? void 0 : _q2.enabled) ? await predict13(faces[i].tensor || ir([]), instance.config, i, faces.length) : null;
instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);
}
instance.analyze("End InsightFace:");
instance.analyze("Start Description:");
if (instance.config.async) {
descRes = predict6(faces[i].tensor || ir([]), instance.config, i, faces.length);
} else {
instance.state = "run:description";
timeStamp = now();
descRes = await predict6(faces[i].tensor || ir([]), instance.config, i, faces.length);
instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
instance.analyze("End Description:");
if (instance.config.async) {
[ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);
}
instance.analyze("Finish Face:");
if (((_r2 = instance.config.face["ssrnet"]) == null ? void 0 : _r2.enabled) && ageRes && genderRes) {
descRes = {
...descRes,
age: ageRes.age,
gender: genderRes.gender,
genderScore: genderRes.genderScore
};
}
if (((_s2 = instance.config.face.gear) == null ? void 0 : _s2.enabled) && gearRes) {
descRes = {
...descRes,
age: gearRes.age,
gender: gearRes.gender,
genderScore: gearRes.genderScore,
race: gearRes.race
};
}
if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) {
descRes.descriptor = mobilefacenetRes;
}
if (((_u2 = instance.config.face["insightface"]) == null ? void 0 : _u2.enabled) && insightfaceRes) {
descRes.descriptor = insightfaceRes;
}
const irisSize = ((_v2 = instance.config.face.iris) == null ? void 0 : _v2.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0;
const tensor = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? lc(faces[i].tensor) : null;
Ot(faces[i].tensor);
if (faces[i].tensor)
delete faces[i].tensor;
const res = {
...faces[i],
id: i
};
if (descRes.age)
res.age = descRes.age;
if (descRes.gender)
res.gender = descRes.gender;
if (descRes.genderScore)
res.genderScore = descRes.genderScore;
if (descRes.descriptor)
res.embedding = descRes.descriptor;
if (descRes.race)
res.race = descRes.race;
if (emotionRes)
res.emotion = emotionRes;
if (antispoofRes)
res.real = antispoofRes;
if (livenessRes)
res.live = livenessRes;
if (irisSize > 0)
res.distance = irisSize;
if (rotation)
res.rotation = rotation;
if (tensor)
res.tensor = tensor;
faceRes.push(res);
instance.analyze("End Face");
}
instance.analyze("End FaceMesh:");
if (instance.config.async) {
if (instance.performance.face)
delete instance.performance.face;
if (instance.performance.age)
delete instance.performance.age;
if (instance.performance.gender)
delete instance.performance.gender;
if (instance.performance.emotion)
delete instance.performance.emotion;
}
return faceRes;
};
// src/hand/fingerdef.ts
var Finger = {
thumb: 0,
index: 1,
middle: 2,
ring: 3,
pinky: 4,
all: [0, 1, 2, 3, 4],
// just for convenience
nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" },
// Describes mapping of joints based on the 21 points returned by handpose.
// [0] Palm
// [1-4] Thumb
// [5-8] Index
// [9-12] Middle
// [13-16] Ring
// [17-20] Pinky
pointsMapping: {
0: [[0, 1], [1, 2], [2, 3], [3, 4]],
1: [[0, 5], [5, 6], [6, 7], [7, 8]],
2: [[0, 9], [9, 10], [10, 11], [11, 12]],
3: [[0, 13], [13, 14], [14, 15], [15, 16]],
4: [[0, 17], [17, 18], [18, 19], [19, 20]]
},
getName: (value) => Finger.nameMapping[value],
getPoints: (value) => Finger.pointsMapping[value]
};
var FingerCurl = {
none: 0,
half: 1,
full: 2,
nameMapping: { 0: "none", 1: "half", 2: "full" },
getName: (value) => FingerCurl.nameMapping[value]
};
var FingerDirection = {
verticalUp: 0,
verticalDown: 1,
horizontalLeft: 2,
horizontalRight: 3,
diagonalUpRight: 4,
diagonalUpLeft: 5,
diagonalDownRight: 6,
diagonalDownLeft: 7,
nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" },
getName: (value) => FingerDirection.nameMapping[value]
};
var FingerGesture = class {
constructor(name) {
__publicField(this, "name");
__publicField(this, "curls");
__publicField(this, "directions");
__publicField(this, "weights");
__publicField(this, "weightsRelative");
this.name = name;
this.curls = {};
this.directions = {};
this.weights = [1, 1, 1, 1, 1];
this.weightsRelative = [1, 1, 1, 1, 1];
}
curl(finger, curl, confidence) {
if (typeof this.curls[finger] === "undefined")
this.curls[finger] = [];
this.curls[finger].push([curl, confidence]);
}
direction(finger, position, confidence) {
if (!this.directions[finger])
this.directions[finger] = [];
this.directions[finger].push([position, confidence]);
}
weight(finger, weight) {
this.weights[finger] = weight;
const total = this.weights.reduce((a, b) => a + b, 0);
this.weightsRelative = this.weights.map((el) => el * 5 / total);
}
matchAgainst(detectedCurls, detectedDirections) {
let confidence = 0;
for (const fingerIdx in detectedCurls) {
const detectedCurl = detectedCurls[fingerIdx];
const expectedCurls = this.curls[fingerIdx];
if (typeof expectedCurls === "undefined") {
confidence += this.weightsRelative[fingerIdx];
continue;
}
for (const [expectedCurl, score] of expectedCurls) {
if (detectedCurl === expectedCurl) {
confidence += score * this.weightsRelative[fingerIdx];
break;
}
}
}
for (const fingerIdx in detectedDirections) {
const detectedDirection = detectedDirections[fingerIdx];
const expectedDirections = this.directions[fingerIdx];
if (typeof expectedDirections === "undefined") {
confidence += this.weightsRelative[fingerIdx];
continue;
}
for (const [expectedDirection, score] of expectedDirections) {
if (detectedDirection === expectedDirection) {
confidence += score * this.weightsRelative[fingerIdx];
break;
}
}
}
return confidence / 10;
}
};
// src/hand/fingergesture.ts
var { thumb, index, middle, ring, pinky } = Finger;
var { none, half, full } = FingerCurl;
var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;
var ThumbsUp = new FingerGesture("thumbs up");
ThumbsUp.curl(thumb, none, 1);
ThumbsUp.direction(thumb, verticalUp, 1);
ThumbsUp.direction(thumb, diagonalUpLeft, 0.25);
ThumbsUp.direction(thumb, diagonalUpRight, 0.25);
for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {
ThumbsUp.curl(finger, full, 1);
ThumbsUp.direction(finger, horizontalLeft, 1);
ThumbsUp.direction(finger, horizontalRight, 1);
}
var Victory = new FingerGesture("victory");
Victory.curl(thumb, half, 0.5);
Victory.curl(thumb, none, 0.5);
Victory.direction(thumb, verticalUp, 1);
Victory.direction(thumb, diagonalUpLeft, 1);
Victory.curl(index, none, 1);
Victory.direction(index, verticalUp, 0.75);
Victory.direction(index, diagonalUpLeft, 1);
Victory.curl(middle, none, 1);
Victory.direction(middle, verticalUp, 1);
Victory.direction(middle, diagonalUpLeft, 0.75);
Victory.curl(ring, full, 1);
Victory.direction(ring, verticalUp, 0.2);
Victory.direction(ring, diagonalUpLeft, 1);
Victory.direction(ring, horizontalLeft, 0.2);
Victory.curl(pinky, full, 1);
Victory.direction(pinky, verticalUp, 0.2);
Victory.direction(pinky, diagonalUpLeft, 1);
Victory.direction(pinky, horizontalLeft, 0.2);
Victory.weight(index, 2);
Victory.weight(middle, 2);
var Point = new FingerGesture("point");
Point.curl(thumb, full, 1);
Point.curl(index, none, 0.5);
Point.curl(middle, full, 0.5);
Point.curl(ring, full, 0.5);
Point.curl(pinky, full, 0.5);
Point.weight(index, 2);
Point.weight(middle, 2);
var MiddleFinger = new FingerGesture("middle finger");
MiddleFinger.curl(thumb, none, 1);
MiddleFinger.curl(index, full, 0.5);
MiddleFinger.curl(middle, full, 0.5);
MiddleFinger.curl(ring, full, 0.5);
MiddleFinger.curl(pinky, full, 0.5);
MiddleFinger.weight(index, 2);
MiddleFinger.weight(middle, 2);
var OpenPalm = new FingerGesture("open palm");
OpenPalm.curl(thumb, none, 0.75);
OpenPalm.curl(index, none, 0.75);
OpenPalm.curl(middle, none, 0.75);
OpenPalm.curl(ring, none, 0.75);
OpenPalm.curl(pinky, none, 0.75);
var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];
// src/hand/fingerpose.ts
var minConfidence = 0.7;
var options3 = {
// curl estimation
HALF_CURL_START_LIMIT: 60,
NO_CURL_START_LIMIT: 130,
// direction estimation
DISTANCE_VOTE_POWER: 1.1,
SINGLE_ANGLE_VOTE_POWER: 0.9,
TOTAL_ANGLE_VOTE_POWER: 1.6
};
function calculateSlope(point1x, point1y, point2x, point2y) {
const value = (point1y - point2y) / (point1x - point2x);
let slope = Math.atan(value) * 180 / Math.PI;
if (slope <= 0)
slope = -slope;
else if (slope > 0)
slope = 180 - slope;
return slope;
}
function getSlopes(point1, point2) {
if (!point1 || !point2)
return [0, 0];
const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);
if (point1.length === 2)
return slopeXY;
const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);
return [slopeXY, slopeYZ];
}
function angleOrientationAt(angle, weightageAt = 1) {
let isVertical = 0;
let isDiagonal = 0;
let isHorizontal = 0;
if (angle >= 75 && angle <= 105)
isVertical = 1 * weightageAt;
else if (angle >= 25 && angle <= 155)
isDiagonal = 1 * weightageAt;
else
isHorizontal = 1 * weightageAt;
return [isVertical, isDiagonal, isHorizontal];
}
function estimateFingerCurl(startPoint, midPoint, endPoint) {
const start_mid_x_dist = startPoint[0] - midPoint[0];
const start_end_x_dist = startPoint[0] - endPoint[0];
const mid_end_x_dist = midPoint[0] - endPoint[0];
const start_mid_y_dist = startPoint[1] - midPoint[1];
const start_end_y_dist = startPoint[1] - endPoint[1];
const mid_end_y_dist = midPoint[1] - endPoint[1];
const start_mid_z_dist = startPoint[2] - midPoint[2];
const start_end_z_dist = startPoint[2] - endPoint[2];
const mid_end_z_dist = midPoint[2] - endPoint[2];
const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);
const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);
const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);
let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);
if (cos_in > 1)
cos_in = 1;
else if (cos_in < -1)
cos_in = -1;
let angleOfCurve = Math.acos(cos_in);
angleOfCurve = 57.2958 * angleOfCurve % 180;
let fingerCurl;
if (angleOfCurve > options3.NO_CURL_START_LIMIT)
fingerCurl = FingerCurl.none;
else if (angleOfCurve > options3.HALF_CURL_START_LIMIT)
fingerCurl = FingerCurl.half;
else
fingerCurl = FingerCurl.full;
return fingerCurl;
}
function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
let estimatedDirection;
if (max_dist_x === Math.abs(start_end_x_dist)) {
if (start_end_x_dist > 0)
estimatedDirection = FingerDirection.horizontalLeft;
else
estimatedDirection = FingerDirection.horizontalRight;
} else if (max_dist_x === Math.abs(start_mid_x_dist)) {
if (start_mid_x_dist > 0)
estimatedDirection = FingerDirection.horizontalLeft;
else
estimatedDirection = FingerDirection.horizontalRight;
} else {
if (mid_end_x_dist > 0)
estimatedDirection = FingerDirection.horizontalLeft;
else
estimatedDirection = FingerDirection.horizontalRight;
}
return estimatedDirection;
}
function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {
let estimatedDirection;
if (max_dist_y === Math.abs(start_end_y_dist)) {
if (start_end_y_dist < 0)
estimatedDirection = FingerDirection.verticalDown;
else
estimatedDirection = FingerDirection.verticalUp;
} else if (max_dist_y === Math.abs(start_mid_y_dist)) {
if (start_mid_y_dist < 0)
estimatedDirection = FingerDirection.verticalDown;
else
estimatedDirection = FingerDirection.verticalUp;
} else {
if (mid_end_y_dist < 0)
estimatedDirection = FingerDirection.verticalDown;
else
estimatedDirection = FingerDirection.verticalUp;
}
return estimatedDirection;
}
function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
let estimatedDirection;
const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
if (reqd_vertical_direction === FingerDirection.verticalUp) {
if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
estimatedDirection = FingerDirection.diagonalUpLeft;
else
estimatedDirection = FingerDirection.diagonalUpRight;
} else {
if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
estimatedDirection = FingerDirection.diagonalDownLeft;
else
estimatedDirection = FingerDirection.diagonalDownRight;
}
return estimatedDirection;
}
function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {
const start_mid_x_dist = startPoint[0] - midPoint[0];
const start_end_x_dist = startPoint[0] - endPoint[0];
const mid_end_x_dist = midPoint[0] - endPoint[0];
const start_mid_y_dist = startPoint[1] - midPoint[1];
const start_end_y_dist = startPoint[1] - endPoint[1];
const mid_end_y_dist = midPoint[1] - endPoint[1];
const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));
const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));
let voteVertical = 0;
let voteDiagonal = 0;
let voteHorizontal = 0;
const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5);
if (start_end_x_y_dist_ratio > 1.5)
voteVertical += options3.DISTANCE_VOTE_POWER;
else if (start_end_x_y_dist_ratio > 0.66)
voteDiagonal += options3.DISTANCE_VOTE_POWER;
else
voteHorizontal += options3.DISTANCE_VOTE_POWER;
const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);
const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);
const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);
const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);
let calc_start_point_x = startPoint[0];
let calc_start_point_y = startPoint[1];
let calc_end_point_x = endPoint[0];
let calc_end_point_y = endPoint[1];
if (max_dist === start_mid_dist) {
calc_end_point_x = endPoint[0];
calc_end_point_y = endPoint[1];
} else if (max_dist === mid_end_dist) {
calc_start_point_x = midPoint[0];
calc_start_point_y = midPoint[1];
}
const calcStartPoint = [calc_start_point_x, calc_start_point_y];
const calcEndPoint = [calc_end_point_x, calc_end_point_y];
const totalAngle = getSlopes(calcStartPoint, calcEndPoint);
const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER);
voteVertical += votes[0];
voteDiagonal += votes[1];
voteHorizontal += votes[2];
for (const fingerSlope of fingerSlopes) {
const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER);
voteVertical += fingerVotes[0];
voteDiagonal += fingerVotes[1];
voteHorizontal += fingerVotes[2];
}
let estimatedDirection;
if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {
estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
} else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {
estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
} else {
estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
}
return estimatedDirection;
}
function estimate(landmarks) {
const slopesXY = [];
const slopesYZ = [];
const fingerCurls = [];
const fingerDirections = [];
if (!landmarks)
return { curls: fingerCurls, directions: fingerDirections };
for (const finger of Finger.all) {
const points = Finger.getPoints(finger);
const slopeAtXY = [];
const slopeAtYZ = [];
for (const point2 of points) {
const point1 = landmarks[point2[0]];
const point22 = landmarks[point2[1]];
const slopes = getSlopes(point1, point22);
const slopeXY = slopes[0];
const slopeYZ = slopes[1];
slopeAtXY.push(slopeXY);
slopeAtYZ.push(slopeYZ);
}
slopesXY.push(slopeAtXY);
slopesYZ.push(slopeAtYZ);
}
for (const finger of Finger.all) {
const pointIndexAt = finger === Finger.thumb ? 1 : 0;
const fingerPointsAt = Finger.getPoints(finger);
const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];
const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];
const endPoint = landmarks[fingerPointsAt[3][1]];
const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);
const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));
fingerCurls[finger] = fingerCurled;
fingerDirections[finger] = fingerPosition;
}
return { curls: fingerCurls, directions: fingerDirections };
}
function analyze(keypoints) {
if (!keypoints || keypoints.length === 0)
return null;
const estimatorRes = estimate(keypoints);
const landmarks = {};
for (const fingerIdx of Finger.all) {
landmarks[Finger.getName(fingerIdx)] = {
curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),
direction: FingerDirection.getName(estimatorRes.directions[fingerIdx])
};
}
return landmarks;
}
function match(keypoints) {
const poses = [];
if (!keypoints || keypoints.length === 0)
return poses;
const estimatorRes = estimate(keypoints);
for (const gesture2 of fingergesture_default) {
const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions);
if (confidence >= minConfidence)
poses.push({ name: gesture2.name, confidence });
}
return poses;
}
// src/gesture/gesture.ts
var body2 = (res) => {
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist");
const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist");
const nose = res[i].keypoints.find((a) => a.part === "nose");
if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1])
gestures.push({ body: i, gesture: "i give up" });
else if (nose && leftWrist && leftWrist.position[1] < nose.position[1])
gestures.push({ body: i, gesture: "raise left hand" });
else if (nose && rightWrist && rightWrist.position[1] < nose.position[1])
gestures.push({ body: i, gesture: "raise right hand" });
const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
}
}
return gestures;
};
var face2 = (res) => {
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
if (res[i].mesh && res[i].mesh.length > 450) {
const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
if (Math.abs(zDiff / xDiff) <= 0.15)
gestures.push({ face: i, gesture: "facing center" });
else
gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` });
const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]);
if (openLeft < 0.2)
gestures.push({ face: i, gesture: "blink left eye" });
const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]);
if (openRight < 0.2)
gestures.push({ face: i, gesture: "blink right eye" });
const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
if (mouthOpen > 10)
gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
const chinDepth = res[i].mesh[152][2] || 0;
if (Math.abs(chinDepth) > 10)
gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
}
}
return gestures;
};
var iris2 = (res) => {
var _a2, _b, _c2, _d2;
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
if (!((_b = (_a2 = res[i].annotations) == null ? void 0 : _a2.leftEyeIris) == null ? void 0 : _b[0]) || !((_d2 = (_c2 = res[i].annotations) == null ? void 0 : _c2.rightEyeIris) == null ? void 0 : _d2[0]))
continue;
const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];
const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];
const areaLeft = Math.abs(sizeXLeft * sizeYLeft);
const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];
const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];
const areaRight = Math.abs(sizeXRight * sizeYRight);
let center = false;
const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);
if (difference < 0.25) {
center = true;
gestures.push({ iris: i, gesture: "facing center" });
}
const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];
const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];
if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06)
center = false;
if (leftIrisCenterX > rightIrisCenterX) {
if (leftIrisCenterX > 0.05)
gestures.push({ iris: i, gesture: "looking right" });
} else {
if (rightIrisCenterX > 0.05)
gestures.push({ iris: i, gesture: "looking left" });
}
const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];
const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];
if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
center = false;
if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01)
gestures.push({ iris: i, gesture: "looking down" });
if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
gestures.push({ iris: i, gesture: "looking up" });
if (center)
gestures.push({ iris: i, gesture: "looking center" });
}
return gestures;
};
var hand2 = (res) => {
if (!res)
return [];
const gestures = [];
for (let i = 0; i < res.length; i++) {
const fingers = [];
if (res[i].annotations) {
for (const [finger, pos] of Object.entries(res[i].annotations)) {
if (finger !== "palmBase" && Array.isArray(pos) && pos[0])
fingers.push({ name: finger.toLowerCase(), position: pos[0] });
}
}
if (fingers && fingers.length > 0) {
const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
gestures.push({ hand: i, gesture: `${closest.name} forward` });
const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
gestures.push({ hand: i, gesture: `${highest.name} up` });
}
if (res[i].keypoints) {
const poses = match(res[i].keypoints);
for (const pose of poses)
gestures.push({ hand: i, gesture: pose.name });
}
}
return gestures;
};
// src/hand/handposeutil.ts
function getBoxSize2(box) {
return [
Math.abs(box.endPoint[0] - box.startPoint[0]),
Math.abs(box.endPoint[1] - box.startPoint[1])
];
}
function getBoxCenter2(box) {
return [
box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
];
}
function cutBoxFromImageAndResize(box, image, cropSize) {
const h = image.shape[1];
const w = image.shape[2];
const boxes = [[
box.startPoint[1] / h,
box.startPoint[0] / w,
box.endPoint[1] / h,
box.endPoint[0] / w
]];
return uj.cropAndResize(image, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box, factor) {
const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
const palmLandmarks = box.palmLandmarks.map((coord) => {
const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
return scaledCoord;
});
return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };
}
function enlargeBox2(box, factor = 1.5) {
const center = getBoxCenter2(box);
const size2 = getBoxSize2(box);
const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
function squarifyBox2(box) {
const centers = getBoxCenter2(box);
const size2 = getBoxSize2(box);
const maxEdge = Math.max(...size2);
const halfSize = maxEdge / 2;
const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
function normalizeRadians2(angle) {
return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
}
function computeRotation2(point1, point2) {
const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
return normalizeRadians2(radians);
}
var buildTranslationMatrix2 = (x, y5) => [[1, 0, x], [0, 1, y5], [0, 0, 1]];
function dot2(v12, v22) {
let product = 0;
for (let i = 0; i < v12.length; i++) {
product += v12[i] * v22[i];
}
return product;
}
function getColumnFrom2DArr2(arr, columnIndex) {
const column = [];
for (let i = 0; i < arr.length; i++) {
column.push(arr[i][columnIndex]);
}
return column;
}
function multiplyTransformMatrices2(mat1, mat2) {
const product = [];
const size2 = mat1.length;
for (let row = 0; row < size2; row++) {
product.push([]);
for (let col = 0; col < size2; col++) {
product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col)));
}
}
return product;
}
function buildRotationMatrix2(rotation, center) {
const cosA = Math.cos(rotation);
const sinA = Math.sin(rotation);
const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
const translationMatrix = buildTranslationMatrix2(center[0], center[1]);
const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix);
const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]);
return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix);
}
function invertTransformMatrix2(matrix) {
const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
const translationComponent = [matrix[0][2], matrix[1][2]];
const invertedTranslation = [
-dot2(rotationComponent[0], translationComponent),
-dot2(rotationComponent[1], translationComponent)
];
return [
rotationComponent[0].concat(invertedTranslation[0]),
rotationComponent[1].concat(invertedTranslation[1]),
[0, 0, 1]
];
}
function rotatePoint2(homogeneousCoordinate, rotationMatrix) {
return [
dot2(homogeneousCoordinate, rotationMatrix[0]),
dot2(homogeneousCoordinate, rotationMatrix[1])
];
}
// src/hand/handposeanchors.ts
var anchors2 = [
{ x: 0.015625, y: 0.015625 },
{ x: 0.015625, y: 0.015625 },
{ x: 0.046875, y: 0.015625 },
{ x: 0.046875, y: 0.015625 },
{ x: 0.078125, y: 0.015625 },
{ x: 0.078125, y: 0.015625 },
{ x: 0.109375, y: 0.015625 },
{ x: 0.109375, y: 0.015625 },
{ x: 0.140625, y: 0.015625 },
{ x: 0.140625, y: 0.015625 },
{ x: 0.171875, y: 0.015625 },
{ x: 0.171875, y: 0.015625 },
{ x: 0.203125, y: 0.015625 },
{ x: 0.203125, y: 0.015625 },
{ x: 0.234375, y: 0.015625 },
{ x: 0.234375, y: 0.015625 },
{ x: 0.265625, y: 0.015625 },
{ x: 0.265625, y: 0.015625 },
{ x: 0.296875, y: 0.015625 },
{ x: 0.296875, y: 0.015625 },
{ x: 0.328125, y: 0.015625 },
{ x: 0.328125, y: 0.015625 },
{ x: 0.359375, y: 0.015625 },
{ x: 0.359375, y: 0.015625 },
{ x: 0.390625, y: 0.015625 },
{ x: 0.390625, y: 0.015625 },
{ x: 0.421875, y: 0.015625 },
{ x: 0.421875, y: 0.015625 },
{ x: 0.453125, y: 0.015625 },
{ x: 0.453125, y: 0.015625 },
{ x: 0.484375, y: 0.015625 },
{ x: 0.484375, y: 0.015625 },
{ x: 0.515625, y: 0.015625 },
{ x: 0.515625, y: 0.015625 },
{ x: 0.546875, y: 0.015625 },
{ x: 0.546875, y: 0.015625 },
{ x: 0.578125, y: 0.015625 },
{ x: 0.578125, y: 0.015625 },
{ x: 0.609375, y: 0.015625 },
{ x: 0.609375, y: 0.015625 },
{ x: 0.640625, y: 0.015625 },
{ x: 0.640625, y: 0.015625 },
{ x: 0.671875, y: 0.015625 },
{ x: 0.671875, y: 0.015625 },
{ x: 0.703125, y: 0.015625 },
{ x: 0.703125, y: 0.015625 },
{ x: 0.734375, y: 0.015625 },
{ x: 0.734375, y: 0.015625 },
{ x: 0.765625, y: 0.015625 },
{ x: 0.765625, y: 0.015625 },
{ x: 0.796875, y: 0.015625 },
{ x: 0.796875, y: 0.015625 },
{ x: 0.828125, y: 0.015625 },
{ x: 0.828125, y: 0.015625 },
{ x: 0.859375, y: 0.015625 },
{ x: 0.859375, y: 0.015625 },
{ x: 0.890625, y: 0.015625 },
{ x: 0.890625, y: 0.015625 },
{ x: 0.921875, y: 0.015625 },
{ x: 0.921875, y: 0.015625 },
{ x: 0.953125, y: 0.015625 },
{ x: 0.953125, y: 0.015625 },
{ x: 0.984375, y: 0.015625 },
{ x: 0.984375, y: 0.015625 },
{ x: 0.015625, y: 0.046875 },
{ x: 0.015625, y: 0.046875 },
{ x: 0.046875, y: 0.046875 },
{ x: 0.046875, y: 0.046875 },
{ x: 0.078125, y: 0.046875 },
{ x: 0.078125, y: 0.046875 },
{ x: 0.109375, y: 0.046875 },
{ x: 0.109375, y: 0.046875 },
{ x: 0.140625, y: 0.046875 },
{ x: 0.140625, y: 0.046875 },
{ x: 0.171875, y: 0.046875 },
{ x: 0.171875, y: 0.046875 },
{ x: 0.203125, y: 0.046875 },
{ x: 0.203125, y: 0.046875 },
{ x: 0.234375, y: 0.046875 },
{ x: 0.234375, y: 0.046875 },
{ x: 0.265625, y: 0.046875 },
{ x: 0.265625, y: 0.046875 },
{ x: 0.296875, y: 0.046875 },
{ x: 0.296875, y: 0.046875 },
{ x: 0.328125, y: 0.046875 },
{ x: 0.328125, y: 0.046875 },
{ x: 0.359375, y: 0.046875 },
{ x: 0.359375, y: 0.046875 },
{ x: 0.390625, y: 0.046875 },
{ x: 0.390625, y: 0.046875 },
{ x: 0.421875, y: 0.046875 },
{ x: 0.421875, y: 0.046875 },
{ x: 0.453125, y: 0.046875 },
{ x: 0.453125, y: 0.046875 },
{ x: 0.484375, y: 0.046875 },
{ x: 0.484375, y: 0.046875 },
{ x: 0.515625, y: 0.046875 },
{ x: 0.515625, y: 0.046875 },
{ x: 0.546875, y: 0.046875 },
{ x: 0.546875, y: 0.046875 },
{ x: 0.578125, y: 0.046875 },
{ x: 0.578125, y: 0.046875 },
{ x: 0.609375, y: 0.046875 },
{ x: 0.609375, y: 0.046875 },
{ x: 0.640625, y: 0.046875 },
{ x: 0.640625, y: 0.046875 },
{ x: 0.671875, y: 0.046875 },
{ x: 0.671875, y: 0.046875 },
{ x: 0.703125, y: 0.046875 },
{ x: 0.703125, y: 0.046875 },
{ x: 0.734375, y: 0.046875 },
{ x: 0.734375, y: 0.046875 },
{ x: 0.765625, y: 0.046875 },
{ x: 0.765625, y: 0.046875 },
{ x: 0.796875, y: 0.046875 },
{ x: 0.796875, y: 0.046875 },
{ x: 0.828125, y: 0.046875 },
{ x: 0.828125, y: 0.046875 },
{ x: 0.859375, y: 0.046875 },
{ x: 0.859375, y: 0.046875 },
{ x: 0.890625, y: 0.046875 },
{ x: 0.890625, y: 0.046875 },
{ x: 0.921875, y: 0.046875 },
{ x: 0.921875, y: 0.046875 },
{ x: 0.953125, y: 0.046875 },
{ x: 0.953125, y: 0.046875 },
{ x: 0.984375, y: 0.046875 },
{ x: 0.984375, y: 0.046875 },
{ x: 0.015625, y: 0.078125 },
{ x: 0.015625, y: 0.078125 },
{ x: 0.046875, y: 0.078125 },
{ x: 0.046875, y: 0.078125 },
{ x: 0.078125, y: 0.078125 },
{ x: 0.078125, y: 0.078125 },
{ x: 0.109375, y: 0.078125 },
{ x: 0.109375, y: 0.078125 },
{ x: 0.140625, y: 0.078125 },
{ x: 0.140625, y: 0.078125 },
{ x: 0.171875, y: 0.078125 },
{ x: 0.171875, y: 0.078125 },
{ x: 0.203125, y: 0.078125 },
{ x: 0.203125, y: 0.078125 },
{ x: 0.234375, y: 0.078125 },
{ x: 0.234375, y: 0.078125 },
{ x: 0.265625, y: 0.078125 },
{ x: 0.265625, y: 0.078125 },
{ x: 0.296875, y: 0.078125 },
{ x: 0.296875, y: 0.078125 },
{ x: 0.328125, y: 0.078125 },
{ x: 0.328125, y: 0.078125 },
{ x: 0.359375, y: 0.078125 },
{ x: 0.359375, y: 0.078125 },
{ x: 0.390625, y: 0.078125 },
{ x: 0.390625, y: 0.078125 },
{ x: 0.421875, y: 0.078125 },
{ x: 0.421875, y: 0.078125 },
{ x: 0.453125, y: 0.078125 },
{ x: 0.453125, y: 0.078125 },
{ x: 0.484375, y: 0.078125 },
{ x: 0.484375, y: 0.078125 },
{ x: 0.515625, y: 0.078125 },
{ x: 0.515625, y: 0.078125 },
{ x: 0.546875, y: 0.078125 },
{ x: 0.546875, y: 0.078125 },
{ x: 0.578125, y: 0.078125 },
{ x: 0.578125, y: 0.078125 },
{ x: 0.609375, y: 0.078125 },
{ x: 0.609375, y: 0.078125 },
{ x: 0.640625, y: 0.078125 },
{ x: 0.640625, y: 0.078125 },
{ x: 0.671875, y: 0.078125 },
{ x: 0.671875, y: 0.078125 },
{ x: 0.703125, y: 0.078125 },
{ x: 0.703125, y: 0.078125 },
{ x: 0.734375, y: 0.078125 },
{ x: 0.734375, y: 0.078125 },
{ x: 0.765625, y: 0.078125 },
{ x: 0.765625, y: 0.078125 },
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{ x: 0.4375, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ x: 0.4375, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.5625, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.6875, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.8125, y: 0.9375 },
{ x: 0.9375, y: 0.9375 },
{ x: 0.9375, y: 0.9375 },
{ x: 0.9375, y: 0.9375 },
{ x: 0.9375, y: 0.9375 },
{ x: 0.9375, y: 0.9375 },
{ x: 0.9375, y: 0.9375 }
];
// src/hand/handposedetector.ts
var HandDetector = class {
constructor(model23) {
__publicField(this, "model");
__publicField(this, "anchors");
__publicField(this, "anchorsTensor");
__publicField(this, "inputSize");
__publicField(this, "inputSizeTensor");
__publicField(this, "doubleInputSizeTensor");
var _a2, _b, _c2, _d2;
this.model = model23;
this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
this.anchorsTensor = uu(this.anchors);
this.inputSize = ((_d2 = (_c2 = (_b = (_a2 = this == null ? void 0 : this.model) == null ? void 0 : _a2.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c2.shape) == null ? void 0 : _d2[2]) || 0;
this.inputSizeTensor = xr([this.inputSize, this.inputSize]);
this.doubleInputSizeTensor = xr([this.inputSize * 2, this.inputSize * 2]);
}
normalizeBoxes(boxes) {
const t10 = {};
t10.boxOffsets = qe(boxes, [0, 0], [-1, 2]);
t10.boxSizes = qe(boxes, [0, 2], [-1, 2]);
t10.div = Ke(t10.boxOffsets, this.inputSizeTensor);
t10.boxCenterPoints = be(t10.div, this.anchorsTensor);
t10.halfBoxSizes = Ke(t10.boxSizes, this.doubleInputSizeTensor);
t10.sub = Te(t10.boxCenterPoints, t10.halfBoxSizes);
t10.startPoints = se(t10.sub, this.inputSizeTensor);
t10.add = be(t10.boxCenterPoints, t10.halfBoxSizes);
t10.endPoints = se(t10.add, this.inputSizeTensor);
const res = Dk([t10.startPoints, t10.endPoints], 1);
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return res;
}
normalizeLandmarks(rawPalmLandmarks, index2) {
const t10 = {};
t10.reshape = W(rawPalmLandmarks, [-1, 7, 2]);
t10.div = Ke(t10.reshape, this.inputSizeTensor);
t10.landmarks = be(t10.div, this.anchors[index2] ? this.anchors[index2] : 0);
const res = se(t10.landmarks, this.inputSizeTensor);
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return res;
}
async predict(input, config3) {
var _a2;
const t10 = {};
t10.resize = uj.resizeBilinear(input, [this.inputSize, this.inputSize]);
t10.div = Ke(t10.resize, constants.tf127);
t10.image = Te(t10.div, constants.tf1);
t10.batched = this.model.execute(t10.image);
t10.predictions = lc(t10.batched);
t10.slice = qe(t10.predictions, [0, 0], [-1, 1]);
t10.sigmoid = wa(t10.slice);
t10.scores = lc(t10.sigmoid);
const scores = await t10.scores.data();
t10.boxes = qe(t10.predictions, [0, 1], [-1, 4]);
t10.norm = this.normalizeBoxes(t10.boxes);
t10.nms = await uj.nonMaxSuppressionAsync(t10.norm, t10.scores, 3 * (((_a2 = config3.hand) == null ? void 0 : _a2.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence);
const nms = await t10.nms.array();
const hands = [];
for (const index2 of nms) {
const p = {};
p.box = qe(t10.norm, [index2, 0], [1, -1]);
p.slice = qe(t10.predictions, [index2, 5], [1, 14]);
p.norm = this.normalizeLandmarks(p.slice, index2);
p.palmLandmarks = W(p.norm, [-1, 2]);
const box = await p.box.data();
const startPoint = box.slice(0, 2);
const endPoint = box.slice(2, 4);
const palmLandmarks = await p.palmLandmarks.array();
const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] };
const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);
hands.push(scaled);
Object.keys(p).forEach((tensor) => Ot(p[tensor]));
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return hands;
}
};
// src/hand/handposepipeline.ts
var palmBoxEnlargeFactor = 5;
var handBoxEnlargeFactor = 1.65;
var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
var palmLandmarksPalmBase = 0;
var palmLandmarksMiddleFingerBase = 2;
var lastTime13 = 0;
var HandPipeline = class {
constructor(handDetector, handPoseModel2) {
__publicField(this, "handDetector");
__publicField(this, "handPoseModel");
__publicField(this, "inputSize");
__publicField(this, "storedBoxes");
__publicField(this, "skipped");
__publicField(this, "detectedHands");
var _a2, _b, _c2;
this.handDetector = handDetector;
this.handPoseModel = handPoseModel2;
this.inputSize = ((_c2 = (_b = (_a2 = this.handPoseModel) == null ? void 0 : _a2.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c2[2]) || 0;
this.storedBoxes = [];
this.skipped = Number.MAX_SAFE_INTEGER;
this.detectedHands = 0;
}
calculateLandmarksBoundingBox(landmarks) {
const xs2 = landmarks.map((d) => d[0]);
const ys2 = landmarks.map((d) => d[1]);
const startPoint = [Math.min(...xs2), Math.min(...ys2)];
const endPoint = [Math.max(...xs2), Math.max(...ys2)];
return { startPoint, endPoint };
}
getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix));
const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor);
}
getBoxForHandLandmarks(landmarks) {
const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor);
boxAroundHand.palmLandmarks = [];
for (let i = 0; i < palmLandmarkIds.length; i++) {
boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));
}
return boxAroundHand;
}
transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
const boxSize = getBoxSize2(box2);
const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];
const coordsScaled = rawCoords.map((coord) => [
scaleFactor[0] * (coord[0] - this.inputSize / 2),
scaleFactor[1] * (coord[1] - this.inputSize / 2),
scaleFactor[2] * coord[2]
]);
const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]);
const coordsRotated = coordsScaled.map((coord) => {
const rotated = rotatePoint2(coord, coordsRotationMatrix);
return [...rotated, coord[2]];
});
const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix);
const boxCenter = [...getBoxCenter2(box2), 1];
const originalBoxCenter = [
dot2(boxCenter, inverseRotationMatrix[0]),
dot2(boxCenter, inverseRotationMatrix[1])
];
return coordsRotated.map((coord) => [
Math.trunc(coord[0] + originalBoxCenter[0]),
Math.trunc(coord[1] + originalBoxCenter[1]),
Math.trunc(coord[2])
]);
}
async estimateHands(image, config3) {
let useFreshBox = false;
let boxes;
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13;
const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame) {
boxes = await this.handDetector.predict(image, config3);
this.skipped = 0;
}
if (config3.skipAllowed)
this.skipped++;
if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) {
this.detectedHands = 0;
this.storedBoxes = [...boxes];
if (this.storedBoxes.length > 0)
useFreshBox = true;
}
const hands = [];
for (let i = 0; i < this.storedBoxes.length; i++) {
const currentBox = this.storedBoxes[i];
if (!currentBox)
continue;
if (config3.hand.landmarks) {
const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
const palmCenter = getBoxCenter2(currentBox);
const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];
const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? uj.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();
const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
const handImage = Ke(croppedInput, constants.tf255);
Ot(croppedInput);
Ot(rotatedImage);
const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
lastTime13 = now();
Ot(handImage);
const confidence = (await confidenceT.data())[0];
Ot(confidenceT);
if (confidence >= config3.hand.minConfidence / 4) {
const keypointsReshaped = W(keypoints, [-1, 3]);
const rawCoords = await keypointsReshaped.array();
Ot(keypoints);
Ot(keypointsReshaped);
const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
const nextBoundingBox = this.getBoxForHandLandmarks(coords);
this.storedBoxes[i] = { ...nextBoundingBox, confidence };
const result = {
landmarks: coords,
confidence,
boxConfidence: currentBox.confidence,
fingerConfidence: confidence,
box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint }
};
hands.push(result);
} else {
this.storedBoxes[i] = null;
}
Ot(keypoints);
} else {
const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor);
const result = {
confidence: currentBox.confidence,
boxConfidence: currentBox.confidence,
fingerConfidence: 0,
box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
landmarks: []
};
hands.push(result);
}
}
this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
this.detectedHands = hands.length;
if (hands.length > config3.hand.maxDetected)
hands.length = config3.hand.maxDetected;
return hands;
}
};
// src/hand/handpose.ts
var meshAnnotations2 = {
thumb: [1, 2, 3, 4],
index: [5, 6, 7, 8],
middle: [9, 10, 11, 12],
ring: [13, 14, 15, 16],
pinky: [17, 18, 19, 20],
palm: [0]
};
var handDetectorModel;
var handPoseModel;
var handPipeline;
async function predict14(input, config3) {
const predictions = await handPipeline.estimateHands(input, config3);
if (!predictions)
return [];
const hands = [];
for (let i = 0; i < predictions.length; i++) {
const annotations2 = {};
if (predictions[i].landmarks) {
for (const key of Object.keys(meshAnnotations2)) {
annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]);
}
}
const keypoints = predictions[i].landmarks;
let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
let boxRaw = [0, 0, 0, 0];
if (keypoints && keypoints.length > 0) {
for (const pt2 of keypoints) {
if (pt2[0] < box[0])
box[0] = pt2[0];
if (pt2[1] < box[1])
box[1] = pt2[1];
if (pt2[0] > box[2])
box[2] = pt2[0];
if (pt2[1] > box[3])
box[3] = pt2[1];
}
box[2] -= box[0];
box[3] -= box[1];
boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];
} else {
box = predictions[i].box ? [
Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
] : [0, 0, 0, 0];
boxRaw = [
predictions[i].box.topLeft[0] / (input.shape[2] || 0),
predictions[i].box.topLeft[1] / (input.shape[1] || 0),
(predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
];
}
const landmarks = analyze(keypoints);
hands.push({
id: i,
score: Math.round(100 * predictions[i].confidence) / 100,
boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,
fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,
label: "hand",
box,
boxRaw,
keypoints,
annotations: annotations2,
landmarks
});
}
return hands;
}
async function load15(config3) {
var _a2, _b;
if (env.initial) {
handDetectorModel = null;
handPoseModel = null;
}
if (!handDetectorModel || !handPoseModel) {
[handDetectorModel, handPoseModel] = await Promise.all([
config3.hand.enabled ? loadModel((_a2 = config3.hand.detector) == null ? void 0 : _a2.modelPath) : null,
config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null
]);
} else {
if (config3.debug)
log("cached model:", handDetectorModel["modelUrl"]);
if (config3.debug)
log("cached model:", handPoseModel["modelUrl"]);
}
const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0;
if (handDetector && handPoseModel)
handPipeline = new HandPipeline(handDetector, handPoseModel);
return [handDetectorModel, handPoseModel];
}
// src/hand/handtrack.ts
var models2 = [null, null];
var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
var inputSize7 = [[0, 0], [0, 0]];
var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
var faceIndex = 4;
var boxExpandFact = 1.6;
var maxDetectorResolution = 512;
var detectorExpandFact = 1.4;
var skipped13 = Number.MAX_SAFE_INTEGER;
var lastTime14 = 0;
var outputSize = [0, 0];
var cache4 = {
boxes: [],
hands: []
};
var fingerMap = {
/*
thumb: [0, 1, 2, 3, 4],
index: [0, 5, 6, 7, 8],
middle: [0, 9, 10, 11, 12],
ring: [0, 13, 14, 15, 16],
pinky: [0, 17, 18, 19, 20],
palm: [0],
*/
thumb: [1, 2, 3, 4],
index: [5, 6, 7, 8],
middle: [9, 10, 11, 12],
ring: [13, 14, 15, 16],
pinky: [17, 18, 19, 20],
base: [0],
palm: [0, 17, 13, 9, 5, 1, 0]
};
async function loadDetect2(config3) {
var _a2;
if (env.initial)
models2[0] = null;
if (!models2[0]) {
fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
models2[0] = await loadModel((_a2 = config3.hand.detector) == null ? void 0 : _a2.modelPath);
const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0;
inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
} else if (config3.debug)
log("cached model:", models2[0]["modelUrl"]);
return models2[0];
}
async function loadSkeleton(config3) {
var _a2;
if (env.initial)
models2[1] = null;
if (!models2[1]) {
models2[1] = await loadModel((_a2 = config3.hand.skeleton) == null ? void 0 : _a2.modelPath);
const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0;
inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
} else if (config3.debug)
log("cached model:", models2[1]["modelUrl"]);
return models2[1];
}
async function detectHands(input, config3) {
const hands = [];
if (!input || !models2[0])
return hands;
const t10 = {};
const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1);
const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution);
const width = Math.round(height * ratio2 / 8) * 8;
t10.resize = uj.resizeBilinear(input, [height, width]);
t10.cast = Ye(t10.resize, "int32");
[t10.rawScores, t10.rawBoxes] = await models2[0].executeAsync(t10.cast, modelOutputNodes);
t10.boxes = lc(t10.rawBoxes, [0, 2]);
t10.scores = lc(t10.rawScores, [0]);
const classScores = po(t10.scores, 1);
Ot(classScores[faceIndex]);
classScores.splice(faceIndex, 1);
t10.filtered = vr(classScores, 1);
Ot(classScores);
t10.max = Ia(t10.filtered, 1);
t10.argmax = mk(t10.filtered, 1);
let id2 = 0;
t10.nms = await uj.nonMaxSuppressionAsync(t10.boxes, t10.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
const nms = await t10.nms.data();
const scores = await t10.max.data();
const classNum = await t10.argmax.data();
for (const nmsIndex of Array.from(nms)) {
const boxSlice = qe(t10.boxes, nmsIndex, 1);
const boxYX = await boxSlice.data();
Ot(boxSlice);
const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
const boxRaw = scale(boxData, detectorExpandFact);
const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
const score = scores[nmsIndex];
const label = classes[classNum[nmsIndex]];
const hand3 = { id: id2++, score, box: boxFull, boxRaw, label };
hands.push(hand3);
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
hands.sort((a, b) => b.score - a.score);
if (hands.length > (config3.hand.maxDetected || 1))
hands.length = config3.hand.maxDetected || 1;
return hands;
}
async function detectFingers(input, h, config3) {
const hand3 = {
// initial values inherited from hand detect
id: h.id,
score: Math.round(100 * h.score) / 100,
boxScore: Math.round(100 * h.score) / 100,
fingerScore: 0,
box: h.box,
boxRaw: h.boxRaw,
label: h.label,
keypoints: [],
landmarks: {},
annotations: {}
};
if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
const t10 = {};
const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
t10.crop = uj.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
t10.div = Ke(t10.crop, constants.tf255);
[t10.score, t10.keypoints] = models2[1].execute(t10.div, ["Identity_1", "Identity"]);
const rawScore = (await t10.score.data())[0];
const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
if (score >= (config3.hand.minConfidence || 0)) {
hand3.fingerScore = score;
t10.reshaped = W(t10.keypoints, [-1, 3]);
const coordsData = await t10.reshaped.array();
const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
hand3.landmarks = analyze(hand3.keypoints);
for (const key of Object.keys(fingerMap)) {
hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
}
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
}
return hand3;
}
async function predict15(input, config3) {
var _a2, _b;
if (!((_a2 = models2[0]) == null ? void 0 : _a2["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape)
return [];
outputSize = [input.shape[2] || 0, input.shape[1] || 0];
skipped13++;
const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14;
const skipFrame = skipped13 < (config3.hand.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame) {
return cache4.hands;
}
return new Promise(async (resolve) => {
const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14;
const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0);
if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
} else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
} else {
cache4.boxes = await detectHands(input, config3);
lastTime14 = now();
cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
skipped13 = 0;
}
const oldCache = [...cache4.boxes];
cache4.boxes.length = 0;
if (config3.cacheSensitivity > 0) {
for (let i = 0; i < cache4.hands.length; i++) {
const boxKpt = square(cache4.hands[i].keypoints, outputSize);
if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) {
const boxScale = scale(boxKpt.box, boxExpandFact);
const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact);
cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });
}
}
}
for (let i = 0; i < cache4.hands.length; i++) {
const bbox = calc(cache4.hands[i].keypoints, outputSize);
cache4.hands[i].box = bbox.box;
cache4.hands[i].boxRaw = bbox.boxRaw;
}
resolve(cache4.hands);
});
}
// src/result.ts
var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });
// src/body/movenetcoords.ts
var movenetcoords_exports = {};
__export(movenetcoords_exports, {
connected: () => connected3,
horizontal: () => horizontal,
kpt: () => kpt3,
relative: () => relative,
vertical: () => vertical
});
var kpt3 = [
// used to create part labels
"nose",
"leftEye",
"rightEye",
"leftEar",
"rightEar",
"leftShoulder",
"rightShoulder",
"leftElbow",
"rightElbow",
"leftWrist",
"rightWrist",
"leftHip",
"rightHip",
"leftKnee",
"rightKnee",
"leftAnkle",
"rightAnkle"
];
var horizontal = [
// used to fix left vs right
["leftEye", "rightEye"],
["leftEar", "rightEar"],
["leftShoulder", "rightShoulder"],
["leftElbow", "rightElbow"],
["leftWrist", "rightWrist"],
["leftHip", "rightHip"],
["leftKnee", "rightKnee"],
["leftAnkle", "rightAnkle"]
];
var vertical = [
// used to remove unlikely keypoint positions
["leftKnee", "leftShoulder"],
["rightKnee", "rightShoulder"],
["leftAnkle", "leftKnee"],
["rightAnkle", "rightKnee"]
];
var relative = [
// used to match relative body parts
[["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
[["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
];
var connected3 = {
// used to create body outline in annotations
leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
head: []
};
// src/util/interpolate.ts
var bufferedResult = empty();
var interpolateTime = 0;
function calc2(newResult, config3) {
var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w, _x2, _y, _z2;
const t0 = now();
if (!newResult)
return empty();
const elapsed = Date.now() - newResult.timestamp;
const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1;
if (newResult.canvas)
bufferedResult.canvas = newResult.canvas;
if (newResult.error)
bufferedResult.error = newResult.error;
if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
} else {
for (let i = 0; i < newResult.body.length; i++) {
const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor);
const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor);
const keypoints = newResult.body[i].keypoints.map((newKpt, j) => {
var _a3, _b2, _c3, _d3, _e2, _f3, _g3, _h3, _i3;
return {
score: newKpt.score,
part: newKpt.part,
position: [
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
],
positionRaw: [
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
],
distance: [
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a3[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c3 = newKpt.distance) == null ? void 0 : _c3[0],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d3[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f3 = newKpt.distance) == null ? void 0 : _f3[1],
bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g3[2]) || 0) + (((_h3 = newKpt.distance) == null ? void 0 : _h3[2]) || 0)) / bufferedFactor : (_i3 = newKpt.distance) == null ? void 0 : _i3[2]
]
};
});
const annotations2 = {};
let coords = { connected: {} };
if ((_a2 = config3.body.modelPath) == null ? void 0 : _a2.includes("efficientpose"))
coords = efficientposecoords_exports;
else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
coords = blazeposecoords_exports;
else if ((_c2 = config3.body.modelPath) == null ? void 0 : _c2.includes("movenet"))
coords = movenetcoords_exports;
for (const [name, indexes] of Object.entries(coords.connected)) {
const pt2 = [];
for (let j = 0; j < indexes.length - 1; j++) {
const pt0 = keypoints.find((kp2) => kp2.part === indexes[j]);
const pt1 = keypoints.find((kp2) => kp2.part === indexes[j + 1]);
if (pt0 && pt1)
pt2.push([pt0.position, pt1.position]);
}
annotations2[name] = pt2;
}
bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 };
}
}
if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) {
bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand));
} else {
for (let i = 0; i < newResult.hand.length; i++) {
const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor);
const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor);
if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length)
bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints;
const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
let annotations2 = {};
if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {
bufferedResult.hand[i].annotations = newResult.hand[i].annotations;
annotations2 = bufferedResult.hand[i].annotations;
} else if (newResult.hand[i].annotations) {
for (const key of Object.keys(newResult.hand[i].annotations)) {
annotations2[key] = ((_f2 = (_e = (_d2 = newResult.hand[i]) == null ? void 0 : _d2.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f2[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null;
}
}
bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 };
}
}
if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) {
bufferedResult.face = JSON.parse(JSON.stringify(newResult.face));
} else {
for (let i = 0; i < newResult.face.length; i++) {
const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
let annotations2 = newResult.face[i].annotations;
if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {
bufferedResult.face[i].annotations = newResult.face[i].annotations;
annotations2 = bufferedResult.face[i].annotations;
} else if (newResult.face[i].annotations) {
for (const key of Object.keys(newResult.face[i].annotations)) {
annotations2[key] = ((_i2 = (_h2 = (_g2 = newResult.face[i]) == null ? void 0 : _g2.annotations) == null ? void 0 : _h2[key]) == null ? void 0 : _i2[0]) ? newResult.face[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor)) : null;
}
}
if (newResult.face[i].rotation) {
const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
rotation.matrix = (_j2 = newResult.face[i].rotation) == null ? void 0 : _j2.matrix;
rotation.angle = {
roll: ((bufferedFactor - 1) * (((_l2 = (_k2 = bufferedResult.face[i].rotation) == null ? void 0 : _k2.angle) == null ? void 0 : _l2.roll) || 0) + (((_n2 = (_m = newResult.face[i].rotation) == null ? void 0 : _m.angle) == null ? void 0 : _n2.roll) || 0)) / bufferedFactor,
yaw: ((bufferedFactor - 1) * (((_p2 = (_o2 = bufferedResult.face[i].rotation) == null ? void 0 : _o2.angle) == null ? void 0 : _p2.yaw) || 0) + (((_r2 = (_q2 = newResult.face[i].rotation) == null ? void 0 : _q2.angle) == null ? void 0 : _r2.yaw) || 0)) / bufferedFactor,
pitch: ((bufferedFactor - 1) * (((_t = (_s2 = bufferedResult.face[i].rotation) == null ? void 0 : _s2.angle) == null ? void 0 : _t.pitch) || 0) + (((_v2 = (_u2 = newResult.face[i].rotation) == null ? void 0 : _u2.angle) == null ? void 0 : _v2.pitch) || 0)) / bufferedFactor
};
rotation.gaze = {
// not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive
bearing: ((bufferedFactor - 1) * (((_w = bufferedResult.face[i].rotation) == null ? void 0 : _w.gaze.bearing) || 0) + (((_x2 = newResult.face[i].rotation) == null ? void 0 : _x2.gaze.bearing) || 0)) / bufferedFactor,
strength: ((bufferedFactor - 1) * (((_y = bufferedResult.face[i].rotation) == null ? void 0 : _y.gaze.strength) || 0) + (((_z2 = newResult.face[i].rotation) == null ? void 0 : _z2.gaze.strength) || 0)) / bufferedFactor
};
bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations: annotations2 };
} else {
bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations: annotations2 };
}
}
}
if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) {
bufferedResult.object = JSON.parse(JSON.stringify(newResult.object));
} else {
for (let i = 0; i < newResult.object.length; i++) {
const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor);
const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor);
bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw };
}
}
if (newResult.persons) {
const newPersons = newResult.persons;
if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) {
bufferedResult.persons = JSON.parse(JSON.stringify(newPersons));
} else {
for (let i = 0; i < newPersons.length; i++) {
bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor);
}
}
}
if (newResult.gesture)
bufferedResult.gesture = newResult.gesture;
bufferedResult.width = newResult.width;
bufferedResult.height = newResult.height;
const t12 = now();
interpolateTime = env.perfadd ? interpolateTime + Math.round(t12 - t0) : Math.round(t12 - t0);
if (newResult.performance)
bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };
return bufferedResult;
}
// src/segmentation/meet.ts
var model17;
async function load16(config3) {
if (!model17 || env.initial)
model17 = await loadModel(config3.segmentation.modelPath);
else if (config3.debug)
log("cached model:", model17["modelUrl"]);
return model17;
}
async function predict16(input, config3) {
var _a2;
if (!model17)
model17 = await load16(config3);
if (!(model17 == null ? void 0 : model17["executor"]) || !((_a2 = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a2[0].shape))
return null;
const t10 = {};
t10.resize = uj.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false);
t10.norm = Ke(t10.resize, constants.tf255);
t10.res = model17.execute(t10.norm);
t10.squeeze = lc(t10.res, [0]);
[t10.bgRaw, t10.fgRaw] = po(t10.squeeze, 2);
t10.fg = y1(t10.fgRaw);
t10.mul = se(t10.fg, constants.tf255);
t10.expand = oi(t10.mul, 2);
t10.output = uj.resizeBilinear(t10.expand, [input.shape[1] || 0, input.shape[2] || 0]);
let rgba;
switch (config3.segmentation.mode || "default") {
case "default":
t10.input = lc(input);
t10.concat = yt([t10.input, t10.output], -1);
rgba = Ye(t10.concat, "int32");
break;
case "alpha":
rgba = Ye(t10.output, "int32");
break;
default:
rgba = ir(0);
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return rgba;
}
// src/face/match.ts
var match_exports = {};
__export(match_exports, {
distance: () => distance,
find: () => find,
similarity: () => similarity
});
function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) {
if (!descriptor1 || !descriptor1)
return Number.MAX_SAFE_INTEGER;
let sum = 0;
for (let i = 0; i < descriptor1.length; i++) {
const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]);
sum += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order;
}
return (options4.multiplier || 20) * sum;
}
var normalizeDistance = (dist, order, min, max) => {
if (dist === 0)
return 1;
const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order);
const norm = (1 - root / 100 - min) / (max - min);
const clamp2 = Math.max(Math.min(norm, 1), 0);
return clamp2;
};
function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {
const dist = distance(descriptor1, descriptor2, options4);
return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1);
}
function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {
if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) {
return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };
}
let lowestDistance = Number.MAX_SAFE_INTEGER;
let index2 = -1;
for (let i = 0; i < descriptors.length; i++) {
const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER;
if (res < lowestDistance) {
lowestDistance = res;
index2 = i;
}
if (lowestDistance < (options4.threshold || 0))
break;
}
const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1);
return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity };
}
// src/models.ts
var models_exports2 = {};
__export(models_exports2, {
Models: () => Models,
validateModel: () => validateModel
});
// src/body/movenetfix.ts
var maxJitter = 5e-3;
var cache5 = {
keypoints: [],
padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
};
function bodyParts(body4) {
for (const pair of horizontal) {
const left = body4.keypoints.findIndex((kp2) => kp2.part === pair[0]);
const right = body4.keypoints.findIndex((kp2) => kp2.part === pair[1]);
if (body4.keypoints[left] && body4.keypoints[right]) {
if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
const tmp = body4.keypoints[left];
body4.keypoints[left] = body4.keypoints[right];
body4.keypoints[right] = tmp;
}
}
}
for (const pair of vertical) {
const lower = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[0]);
const higher = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[1]);
if (body4.keypoints[lower] && body4.keypoints[higher]) {
if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
body4.keypoints.splice(lower, 1);
}
}
}
for (const [pair, compare2] of relative) {
const left = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[0]);
const right = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[1]);
const leftTo = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === compare2[0]);
const rightTo = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === compare2[1]);
if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
continue;
const distanceLeft = body4.keypoints[left] ? [
Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
] : [0, 0];
const distanceRight = body4.keypoints[right] ? [
Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
] : [0, 0];
if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
const tmp = body4.keypoints[left];
body4.keypoints[left] = body4.keypoints[right];
body4.keypoints[right] = tmp;
}
}
}
function jitter(keypoints) {
for (let i = 0; i < keypoints.length; i++) {
if (keypoints[i] && cache5.keypoints[i]) {
const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])];
if (diff[0] < maxJitter && diff[1] < maxJitter) {
keypoints[i] = cache5.keypoints[i];
} else {
cache5.keypoints[i] = keypoints[i];
}
} else {
cache5.keypoints[i] = keypoints[i];
}
}
return keypoints;
}
function padInput(input, inputSize10) {
var _a2, _b;
const t10 = {};
if (!((_a2 = input == null ? void 0 : input.shape) == null ? void 0 : _a2[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
return input;
cache5.padding = [
[0, 0],
// dont touch batch
[input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0],
// height before&after
[input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0],
// width before&after
[0, 0]
// dont touch rbg
];
t10.pad = ka(input, cache5.padding);
t10.resize = uj.resizeBilinear(t10.pad, [inputSize10, inputSize10]);
const final = Ye(t10.resize, "int32");
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return final;
}
function rescaleBody(body4, outputSize2) {
body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position);
for (const kpt4 of body4.keypoints) {
kpt4.position = [
kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
];
kpt4.positionRaw = [
kpt4.position[0] / outputSize2[0],
kpt4.position[1] / outputSize2[1]
];
}
const rescaledBoxes = calc(body4.keypoints.map((pt2) => pt2.position), outputSize2);
body4.box = rescaledBoxes.box;
body4.boxRaw = rescaledBoxes.boxRaw;
return body4;
}
// src/body/movenet.ts
var model18;
var inputSize8 = 0;
var skipped14 = Number.MAX_SAFE_INTEGER;
var cache6 = {
boxes: [],
bodies: [],
last: 0
};
async function load17(config3) {
var _a2;
if (env.initial)
model18 = null;
if (!model18) {
fakeOps(["size"], config3);
model18 = await loadModel(config3.body.modelPath);
} else if (config3.debug)
log("cached model:", model18["modelUrl"]);
inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a2 = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a2[0].shape) ? model18.inputs[0].shape[2] : 0;
if (inputSize8 < 64)
inputSize8 = 256;
if (P().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
P().set("WEBGL_USE_SHAPES_UNIFORMS", false);
return model18;
}
function parseSinglePose(res, config3, image) {
const kpt4 = res[0][0];
const keypoints = [];
let score = 0;
for (let id2 = 0; id2 < kpt4.length; id2++) {
score = kpt4[id2][2];
if (score > config3.body.minConfidence) {
const positionRaw = [kpt4[id2][1], kpt4[id2][0]];
keypoints.push({
score: Math.round(100 * score) / 100,
part: kpt3[id2],
positionRaw,
position: [
// normalized to input image size
Math.round((image.shape[2] || 0) * positionRaw[0]),
Math.round((image.shape[1] || 0) * positionRaw[1])
]
});
}
}
score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
const bodies = [];
const newBox = calc(keypoints.map((pt2) => pt2.position), [image.shape[2], image.shape[1]]);
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt2 = [];
for (let i = 0; i < indexes.length - 1; i++) {
const pt0 = keypoints.find((kp2) => kp2.part === indexes[i]);
const pt1 = keypoints.find((kp2) => kp2.part === indexes[i + 1]);
if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
pt2.push([pt0.position, pt1.position]);
}
annotations2[name] = pt2;
}
const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
bodyParts(body4);
bodies.push(body4);
return bodies;
}
function parseMultiPose(res, config3, image) {
const bodies = [];
for (let id2 = 0; id2 < res[0].length; id2++) {
const kpt4 = res[0][id2];
const boxScore = Math.round(100 * kpt4[51 + 4]) / 100;
if (boxScore > config3.body.minConfidence) {
const keypoints = [];
for (let i = 0; i < 17; i++) {
const score = kpt4[3 * i + 2];
if (score > config3.body.minConfidence) {
const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]];
keypoints.push({
part: kpt3[i],
score: Math.round(100 * score) / 100,
positionRaw,
position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])]
});
}
}
const boxRaw = [kpt4[51 + 1], kpt4[51 + 0], kpt4[51 + 3] - kpt4[51 + 1], kpt4[51 + 2] - kpt4[51 + 0]];
const boxNorm = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];
const annotations2 = {};
for (const [name, indexes] of Object.entries(connected3)) {
const pt2 = [];
for (let i = 0; i < indexes.length - 1; i++) {
const pt0 = keypoints.find((kp2) => kp2.part === indexes[i]);
const pt1 = keypoints.find((kp2) => kp2.part === indexes[i + 1]);
if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
pt2.push([pt0.position, pt1.position]);
}
annotations2[name] = pt2;
}
const body4 = { id: id2, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations: annotations2 };
bodyParts(body4);
bodies.push(body4);
}
}
bodies.sort((a, b) => b.score - a.score);
if (bodies.length > config3.body.maxDetected)
bodies.length = config3.body.maxDetected;
return bodies;
}
async function predict17(input, config3) {
var _a2;
if (!(model18 == null ? void 0 : model18["executor"]) || !((_a2 = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a2[0].shape))
return [];
if (!config3.skipAllowed)
cache6.boxes.length = 0;
skipped14++;
const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
const skipFrame = skipped14 < (config3.body.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame) {
return cache6.bodies;
}
return new Promise(async (resolve) => {
const t10 = {};
skipped14 = 0;
t10.input = padInput(input, inputSize8);
t10.res = model18 == null ? void 0 : model18.execute(t10.input);
cache6.last = now();
const res = await t10.res.array();
cache6.bodies = t10.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input);
for (const body4 of cache6.bodies) {
rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]);
jitter(body4.keypoints);
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
resolve(cache6.bodies);
});
}
// src/object/nanodet.ts
var model19;
var last10 = [];
var lastTime15 = 0;
var skipped15 = Number.MAX_SAFE_INTEGER;
var inputSize9 = 0;
var scaleBox = 2.5;
async function load18(config3) {
if (!model19 || env.initial) {
model19 = await loadModel(config3.object.modelPath);
const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0;
inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;
} else if (config3.debug)
log("cached model:", model19["modelUrl"]);
return model19;
}
async function process4(res, outputShape, config3) {
var _a2, _b;
let id2 = 0;
let results = [];
const size2 = inputSize9;
for (const strideSize of [1, 2, 4]) {
const baseSize = strideSize * 13;
const scoresT = lc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length));
const scores = await scoresT.array();
const featuresT = lc(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length));
const boxesMaxT = W(featuresT, [-1, 4, (((_a2 = featuresT.shape) == null ? void 0 : _a2[1]) || 0) / 4]);
const boxIdxT = mk(boxesMaxT, 2);
const boxIdx = await boxIdxT.array();
for (let i = 0; i < scoresT.shape[0]; i++) {
for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) {
const score = scores[i][j];
if (score > (config3.object.minConfidence || 0) && j !== 61) {
const cx2 = (0.5 + Math.trunc(i % baseSize)) / baseSize;
const cy2 = (0.5 + Math.trunc(i / baseSize)) / baseSize;
const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2));
const [x, y5] = [
cx2 - scaleBox / strideSize * boxOffset[0],
cy2 - scaleBox / strideSize * boxOffset[1]
];
const [w, h] = [
cx2 + scaleBox / strideSize * boxOffset[2] - x,
cy2 + scaleBox / strideSize * boxOffset[3] - y5
];
let boxRaw = [x, y5, w, h];
boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1)));
const box = [
// results normalized to input image pixels
boxRaw[0] * outputShape[0],
boxRaw[1] * outputShape[1],
boxRaw[2] * outputShape[0],
boxRaw[3] * outputShape[1]
];
const result = {
id: id2++,
// strideSize,
score: Math.round(100 * score) / 100,
class: j + 1,
label: labels2[j].label,
// center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],
// centerRaw: [cx, cy],
box: box.map((a) => Math.trunc(a)),
boxRaw
};
results.push(result);
}
}
}
Ot([scoresT, featuresT, boxesMaxT, boxIdxT]);
}
const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
const nmsScores = results.map((a) => a.score);
let nmsIdx = [];
if (nmsBoxes && nmsBoxes.length > 0) {
const nms = await uj.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence);
nmsIdx = Array.from(await nms.data());
Ot(nms);
}
results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
return results;
}
async function predict18(image, config3) {
if (!(model19 == null ? void 0 : model19["executor"]))
return [];
const skipTime = (config3.object.skipTime || 0) > now() - lastTime15;
const skipFrame = skipped15 < (config3.object.skipFrames || 0);
if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) {
skipped15++;
return last10;
}
skipped15 = 0;
if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
return last10;
return new Promise(async (resolve) => {
const outputSize2 = [image.shape[2] || 0, image.shape[1] || 0];
const resizeT = uj.resizeBilinear(image, [inputSize9, inputSize9], false);
const normT = Ke(resizeT, constants.tf255);
const transposeT = dc(normT, [0, 3, 1, 2]);
let objectT;
if (config3.object.enabled)
objectT = model19.execute(transposeT);
lastTime15 = now();
const obj = await process4(objectT, outputSize2, config3);
last10 = obj;
Ot([resizeT, normT, transposeT, ...objectT]);
resolve(obj);
});
}
// src/body/posenetutils.ts
var partNames = [
"nose",
"leftEye",
"rightEye",
"leftEar",
"rightEar",
"leftShoulder",
"rightShoulder",
"leftElbow",
"rightElbow",
"leftWrist",
"rightWrist",
"leftHip",
"rightHip",
"leftKnee",
"rightKnee",
"leftAnkle",
"rightAnkle"
];
var count = partNames.length;
var partIds = partNames.reduce((result, jointName, i) => {
result[jointName] = i;
return result;
}, {});
var connectedPartNames = [
["leftHip", "leftShoulder"],
["leftElbow", "leftShoulder"],
["leftElbow", "leftWrist"],
["leftHip", "leftKnee"],
["leftKnee", "leftAnkle"],
["rightHip", "rightShoulder"],
["rightElbow", "rightShoulder"],
["rightElbow", "rightWrist"],
["rightHip", "rightKnee"],
["rightKnee", "rightAnkle"],
["leftShoulder", "rightShoulder"],
["leftHip", "rightHip"]
];
var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
var poseChain = [
["nose", "leftEye"],
["leftEye", "leftEar"],
["nose", "rightEye"],
["rightEye", "rightEar"],
["nose", "leftShoulder"],
["leftShoulder", "leftElbow"],
["leftElbow", "leftWrist"],
["leftShoulder", "leftHip"],
["leftHip", "leftKnee"],
["leftKnee", "leftAnkle"],
["nose", "rightShoulder"],
["rightShoulder", "rightElbow"],
["rightElbow", "rightWrist"],
["rightShoulder", "rightHip"],
["rightHip", "rightKnee"],
["rightKnee", "rightAnkle"]
];
function getBoundingBox(keypoints) {
const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y: y5 } }) => ({
maxX: Math.max(maxX, x),
maxY: Math.max(maxY, y5),
minX: Math.min(minX, x),
minY: Math.min(minY, y5)
}), {
maxX: Number.NEGATIVE_INFINITY,
maxY: Number.NEGATIVE_INFINITY,
minX: Number.POSITIVE_INFINITY,
minY: Number.POSITIVE_INFINITY
});
return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
}
function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
const scaleY = height / inputResolutionHeight;
const scaleX = width / inputResolutionWidth;
const scalePose = (pose, i) => ({
id: i,
score: pose.score,
boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
keypoints: pose.keypoints.map(({ score, part, position }) => ({
score,
part,
position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
})),
annotations: {}
});
const scaledPoses = poses.map((pose, i) => scalePose(pose, i));
return scaledPoses;
}
var MaxHeap = class {
// function call
constructor(maxSize2, getElementValue) {
__publicField(this, "priorityQueue");
// don't touch
__publicField(this, "numberOfElements");
__publicField(this, "getElementValue");
this.priorityQueue = new Array(maxSize2);
this.numberOfElements = -1;
this.getElementValue = getElementValue;
}
enqueue(x) {
this.priorityQueue[++this.numberOfElements] = x;
this.swim(this.numberOfElements);
}
dequeue() {
const max = this.priorityQueue[0];
this.exchange(0, this.numberOfElements--);
this.sink(0);
this.priorityQueue[this.numberOfElements + 1] = null;
return max;
}
empty() {
return this.numberOfElements === -1;
}
size() {
return this.numberOfElements + 1;
}
all() {
return this.priorityQueue.slice(0, this.numberOfElements + 1);
}
max() {
return this.priorityQueue[0];
}
swim(k) {
while (k > 0 && this.less(Math.floor(k / 2), k)) {
this.exchange(k, Math.floor(k / 2));
k = Math.floor(k / 2);
}
}
sink(k) {
while (2 * k <= this.numberOfElements) {
let j = 2 * k;
if (j < this.numberOfElements && this.less(j, j + 1))
j++;
if (!this.less(k, j))
break;
this.exchange(k, j);
k = j;
}
}
getValueAt(i) {
return this.getElementValue(this.priorityQueue[i]);
}
less(i, j) {
return this.getValueAt(i) < this.getValueAt(j);
}
exchange(i, j) {
const t10 = this.priorityQueue[i];
this.priorityQueue[i] = this.priorityQueue[j];
this.priorityQueue[j] = t10;
}
};
function getOffsetPoint(y5, x, keypoint, offsets) {
return {
y: offsets.get(y5, x, keypoint),
x: offsets.get(y5, x, keypoint + count)
};
}
function getImageCoords(part, outputStride2, offsets) {
const { heatmapY, heatmapX, id: keypoint } = part;
const { y: y5, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
return {
x: part.heatmapX * outputStride2 + x,
y: part.heatmapY * outputStride2 + y5
};
}
function clamp(a, min, max) {
if (a < min)
return min;
if (a > max)
return max;
return a;
}
function squaredDistance(y12, x12, y22, x22) {
const dy = y22 - y12;
const dx2 = x22 - x12;
return dy * dy + dx2 * dx2;
}
function addVectors(a, b) {
return { x: a.x + b.x, y: a.y + b.y };
}
// src/body/posenet.ts
var model20;
var poseNetOutputs = [
"MobilenetV1/offset_2/BiasAdd",
"MobilenetV1/heatmap_2/BiasAdd",
"MobilenetV1/displacement_fwd_2/BiasAdd",
"MobilenetV1/displacement_bwd_2/BiasAdd"
/* displacementBwd */
];
var localMaximumRadius = 1;
var outputStride = 16;
var squaredNmsRadius = 50 ** 2;
function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {
const getDisplacement = (point2) => ({
y: displacements.get(point2.y, point2.x, edgeId),
x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId)
});
const getStridedIndexNearPoint = (point2, height2, width2) => ({
y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1),
x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1)
});
const [height, width] = scores.shape;
const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);
const displacement = getDisplacement(sourceKeypointIndices);
const displacedPoint = addVectors(sourceKeypoint.position, displacement);
let targetKeypoint = displacedPoint;
for (let i = 0; i < offsetRefineStep; i++) {
const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);
targetKeypoint = addVectors(
{ x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },
{ x: offsetPoint.x, y: offsetPoint.y }
);
}
const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
return { position: targetKeypoint, part: partNames[targetId], score };
}
function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {
const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]);
const edgesFwd = tuples.map(([, childJointId]) => childJointId);
const edgesBwd = tuples.map(([parentJointId]) => parentJointId);
const numParts = scores.shape[2];
const numEdges = edgesFwd.length;
const keypoints = new Array(numParts);
const rootPoint = getImageCoords(root.part, outputStride, offsets);
keypoints[root.part.id] = {
score: root.score,
part: partNames[root.part.id],
position: rootPoint
};
for (let edge = numEdges - 1; edge >= 0; --edge) {
const sourceId = edgesFwd[edge];
const targetId = edgesBwd[edge];
if (keypoints[sourceId] && !keypoints[targetId]) {
keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);
}
}
for (let edge = 0; edge < numEdges; ++edge) {
const sourceId = edgesBwd[edge];
const targetId = edgesFwd[edge];
if (keypoints[sourceId] && !keypoints[targetId]) {
keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);
}
}
return keypoints;
}
function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {
const [height, width] = scores.shape;
let localMaximum = true;
const yStart = Math.max(heatmapY - localMaximumRadius, 0);
const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
const xStart = Math.max(heatmapX - localMaximumRadius, 0);
const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
if (scores.get(yCurrent, xCurrent, keypointId) > score) {
localMaximum = false;
break;
}
}
if (!localMaximum)
break;
}
return localMaximum;
}
function buildPartWithScoreQueue(minConfidence2, scores) {
const [height, width, numKeypoints] = scores.shape;
const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score);
for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
const score = scores.get(heatmapY, heatmapX, keypointId);
if (score < minConfidence2)
continue;
if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores))
queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });
}
}
}
return queue;
}
function withinRadius(poses, { x, y: y5 }, keypointId) {
return poses.some(({ keypoints }) => {
var _a2;
const correspondingKeypoint = (_a2 = keypoints[keypointId]) == null ? void 0 : _a2.position;
if (!correspondingKeypoint)
return false;
return squaredDistance(y5, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
});
}
function getInstanceScore(existingPoses, keypoints) {
const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
if (!withinRadius(existingPoses, position, keypointId))
result += score;
return result;
}, 0);
return notOverlappedKeypointScores / keypoints.length;
}
function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
const poses = [];
const queue = buildPartWithScoreQueue(minConfidence2, scores);
while (poses.length < maxDetected && !queue.empty()) {
const root = queue.dequeue();
const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
if (withinRadius(poses, rootImageCoords, root.part.id))
continue;
let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
keypoints = keypoints.filter((a) => a.score > minConfidence2);
const score = getInstanceScore(poses, keypoints);
const box = getBoundingBox(keypoints);
if (score > minConfidence2)
poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
}
return poses;
}
async function predict19(input, config3) {
if (!(model20 == null ? void 0 : model20["executor"]))
return [];
const res = De(() => {
if (!model20.inputs[0].shape)
return [];
const resized = uj.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]);
const normalized = Te(Ke(Ye(resized, "float32"), 127.5), 1);
const results = model20.execute(normalized, poseNetOutputs);
const results3d = results.map((y5) => lc(y5, [0]));
results3d[1] = wa(results3d[1]);
return results3d;
});
const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));
for (const t10 of res)
Ot(t10);
const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
if (!model20.inputs[0].shape)
return [];
const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]);
return scaled;
}
async function load19(config3) {
if (!model20 || env.initial)
model20 = await loadModel(config3.body.modelPath);
else if (config3.debug)
log("cached model:", model20["modelUrl"]);
return model20;
}
// src/segmentation/rvm.ts
var model21;
var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"];
var t = {};
var ratio = 0;
function init3(config3) {
Ot([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
t.r1i = ir(0);
t.r2i = ir(0);
t.r3i = ir(0);
t.r4i = ir(0);
ratio = config3.segmentation.ratio || 0.5;
t.downsample_ratio = ir(ratio);
}
async function load20(config3) {
if (!model21 || env.initial)
model21 = await loadModel(config3.segmentation.modelPath);
else if (config3.debug)
log("cached model:", model21["modelUrl"]);
init3(config3);
return model21;
}
var normalize = (r) => De(() => {
const squeeze = lc(r, [0]);
const mul = se(squeeze, constants.tf255);
const cast = Ye(mul, "int32");
return cast;
});
function getRGBA(fgr, pha) {
const rgb3 = fgr ? normalize(fgr) : Sa([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
const a = pha ? normalize(pha) : Sa([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
const rgba = yt([rgb3, a], -1);
Ot([rgb3, a]);
return rgba;
}
function getState(state) {
return De(() => {
const r = {};
r.unstack = po(state, -1);
r.concat = yt(r.unstack, 1);
r.split = ai(r.concat, 4, 1);
r.stack = yt(r.split, 2);
r.squeeze = lc(r.stack, [0]);
r.expand = oi(r.squeeze, -1);
r.add = be(r.expand, 1);
r.mul = se(r.add, 127.5);
r.cast = Ye(r.mul, "int32");
r.tile = nu(r.cast, [1, 1, 3]);
r.alpha = Sa([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
return yt([r.tile, r.alpha], -1);
});
}
async function predict20(input, config3) {
if (!model21)
model21 = await load20(config3);
if (!(model21 == null ? void 0 : model21["executor"]))
return null;
t.src = Ke(input, 255);
if (ratio !== config3.segmentation.ratio)
init3(config3);
const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2);
let rgba;
switch (config3.segmentation.mode || "default") {
case "default":
rgba = getRGBA(fgr, pha);
break;
case "alpha":
rgba = getRGBA(null, pha);
break;
case "foreground":
rgba = getRGBA(fgr, null);
break;
case "state":
rgba = getState(r1o);
break;
default:
rgba = ir(0);
}
Ot([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);
[t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o];
return rgba;
}
// src/segmentation/selfie.ts
var model22;
async function load21(config3) {
if (!model22 || env.initial)
model22 = await loadModel(config3.segmentation.modelPath);
else if (config3.debug)
log("cached model:", model22["modelUrl"]);
return model22;
}
async function predict21(input, config3) {
var _a2;
if (!model22)
model22 = await load21(config3);
if (!(model22 == null ? void 0 : model22["executor"]) || !((_a2 = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a2[0].shape))
return null;
const t10 = {};
t10.resize = uj.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false);
t10.norm = Ke(t10.resize, constants.tf255);
t10.res = model22.execute(t10.norm);
t10.squeeze = lc(t10.res, [0]);
t10.alpha = uj.resizeBilinear(t10.squeeze, [input.shape[1] || 0, input.shape[2] || 0]);
t10.mul = se(t10.alpha, constants.tf255);
let rgba;
switch (config3.segmentation.mode || "default") {
case "default":
t10.input = lc(input);
t10.concat = yt([t10.input, t10.mul], -1);
rgba = Ye(t10.concat, "int32");
break;
case "alpha":
rgba = Ye(t10.mul, "int32");
break;
default:
rgba = ir(0);
}
Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
return rgba;
}
// src/models.ts
function validateModel(instance, model23, name) {
var _a2, _b;
if (!model23)
return null;
if (!((_a2 = instance == null ? void 0 : instance.config) == null ? void 0 : _a2.validateModels))
return null;
const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"];
const ops = [];
const missing = [];
const url = model23["modelUrl"];
const executor = model23["executor"];
if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) {
for (const kernel of Object.values(executor.graph.nodes)) {
const op2 = kernel.op.toLowerCase();
if (!ops.includes(op2))
ops.push(op2);
}
} else {
if (!executor && instance.config.debug) {
log("model not loaded", name);
}
}
for (const op2 of ops) {
if (!simpleOps.includes(op2) && !ignoreOps.includes(op2) && !instance.env.kernels.includes(op2) && !instance.env.kernels.includes(op2.replace("_", "")) && !instance.env.kernels.includes(op2.replace("native", "")) && !instance.env.kernels.includes(op2.replace("v2", ""))) {
missing.push(op2);
}
}
if (instance.config.debug && missing.length > 0)
log("model validation failed:", name, missing);
return missing.length > 0 ? { name, missing, ops, url } : null;
}
var Models = class {
constructor(currentInstance) {
__publicField(this, "instance");
__publicField(this, "models", {});
this.models = {};
this.instance = currentInstance;
}
stats() {
let totalSizeFromManifest = 0;
let totalSizeWeights = 0;
let totalSizeLoading = 0;
for (const m of Object.values(modelStats)) {
totalSizeFromManifest += m.sizeFromManifest;
totalSizeWeights += m.sizeLoadedWeights;
totalSizeLoading += m.sizeDesired;
}
const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
return {
numLoadedModels: Object.values(modelStats).length,
numDefinedModels: Object.keys(this.models).length,
percentageLoaded,
totalSizeFromManifest,
totalSizeWeights,
totalSizeLoading,
modelStats: Object.values(modelStats)
};
}
reset() {
for (const model23 of Object.keys(this.models))
this.models[model23] = null;
}
async load(instance) {
var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w, _x2, _y, _z2, _A2;
if (env.initial)
this.reset();
if (instance)
this.instance = instance;
const m = {};
m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null;
m.antispoof = this.instance.config.face.enabled && ((_a2 = this.instance.config.face.antispoof) == null ? void 0 : _a2.enabled) && !this.models.antispoof ? load8(this.instance.config) : null;
m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null;
m.faceres = this.instance.config.face.enabled && ((_c2 = this.instance.config.face.description) == null ? void 0 : _c2.enabled) && !this.models.faceres ? load7(this.instance.config) : null;
m.emotion = this.instance.config.face.enabled && ((_d2 = this.instance.config.face.emotion) == null ? void 0 : _d2.enabled) && !this.models.emotion ? load6(this.instance.config) : null;
m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f2 = this.instance.config.face.attention) == null ? void 0 : _f2.enabled) && !this.models.iris ? load4(this.instance.config) : null;
m.facemesh = this.instance.config.face.enabled && ((_g2 = this.instance.config.face.mesh) == null ? void 0 : _g2.enabled) && !this.models.facemesh ? load5(this.instance.config) : null;
m.gear = this.instance.config.face.enabled && ((_h2 = this.instance.config.face["gear"]) == null ? void 0 : _h2.enabled) && !this.models.gear ? load10(this.instance.config) : null;
m.ssrnetage = this.instance.config.face.enabled && ((_i2 = this.instance.config.face["ssrnet"]) == null ? void 0 : _i2.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null;
m.ssrnetgender = this.instance.config.face.enabled && ((_j2 = this.instance.config.face["ssrnet"]) == null ? void 0 : _j2.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null;
m.mobilefacenet = this.instance.config.face.enabled && ((_k2 = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k2.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null;
m.insightface = this.instance.config.face.enabled && ((_l2 = this.instance.config.face["insightface"]) == null ? void 0 : _l2.enabled) && !this.models.insightface ? load14(this.instance.config) : null;
m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null;
m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null;
m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n2 = this.instance.config.body.modelPath) == null ? void 0 : _n2.includes("efficientpose")) ? load2(this.instance.config) : null;
m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o2 = this.instance.config.body.modelPath) == null ? void 0 : _o2.includes("movenet")) ? load17(this.instance.config) : null;
m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p2 = this.instance.config.body.modelPath) == null ? void 0 : _p2.includes("posenet")) ? load19(this.instance.config) : null;
m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r2 = (_q2 = this.instance.config.hand.detector) == null ? void 0 : _q2.modelPath) == null ? void 0 : _r2.includes("handtrack")) ? loadDetect2(this.instance.config) : null;
m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s2 = this.instance.config.hand.detector) == null ? void 0 : _s2.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null;
if ((_v2 = (_u2 = this.instance.config.hand.detector) == null ? void 0 : _u2.modelPath) == null ? void 0 : _v2.includes("handdetect"))
[m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null];
m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null;
m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x2 = this.instance.config.object.modelPath) == null ? void 0 : _x2.includes("nanodet")) ? load18(this.instance.config) : null;
m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null;
m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z2 = this.instance.config.segmentation.modelPath) == null ? void 0 : _z2.includes("meet")) ? load16(this.instance.config) : null;
m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A2 = this.instance.config.segmentation.modelPath) == null ? void 0 : _A2.includes("rvm")) ? load20(this.instance.config) : null;
for (const [model23, promise] of Object.entries(m)) {
if (promise == null ? void 0 : promise["then"])
promise["then"]((val) => this.models[model23] = val);
}
await Promise.all(Object.values(m));
}
list() {
const models3 = Object.keys(this.models).map((model23) => {
var _a2;
return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a2 = this.models[model23]) == null ? void 0 : _a2["modelUrl"] : null };
});
for (const m of models3) {
const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));
if (!stats)
continue;
m.size = modelStats[stats].sizeLoadedWeights;
m.url = modelStats[stats].url;
}
return models3;
}
loaded() {
const list = this.list();
const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name);
return loaded;
}
validate() {
const missing = [];
for (const defined of Object.keys(this.models)) {
const model23 = this.models[defined];
if (!model23)
continue;
const res = validateModel(this.instance, model23, defined);
if (res)
missing.push(res);
}
return missing;
}
};
// src/util/persons.ts
function join2(faces, bodies, hands, gestures, shape) {
var _a2, _b, _c2, _d2, _e, _f2;
let id2 = 0;
const persons = [];
for (const face4 of faces) {
const person2 = { id: id2++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };
for (const body4 of bodies) {
if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) {
person2.body = body4;
}
}
if (person2.body) {
for (const hand3 of hands) {
if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
if (person2.hands)
person2.hands.left = hand3;
}
if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
if (person2.hands)
person2.hands.right = hand3;
}
}
}
for (const gesture2 of gestures) {
if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id)
person2.gestures.push(gesture2);
else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id)
person2.gestures.push(gesture2);
else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a2 = person2.body) == null ? void 0 : _a2.id))
person2.gestures.push(gesture2);
else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id))
person2.gestures.push(gesture2);
else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c2 = person2.hands.right) == null ? void 0 : _c2.id))
person2.gestures.push(gesture2);
}
const x = [];
const y5 = [];
const extractXY = (box) => {
if (box && box.length === 4) {
x.push(box[0], box[0] + box[2]);
y5.push(box[1], box[1] + box[3]);
}
};
extractXY(person2.face.box);
extractXY((_d2 = person2.body) == null ? void 0 : _d2.box);
extractXY((_e = person2.hands.left) == null ? void 0 : _e.box);
extractXY((_f2 = person2.hands.right) == null ? void 0 : _f2.box);
const minX = Math.min(...x);
const minY = Math.min(...y5);
person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y5) - minY];
if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2]))
person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]];
persons.push(person2);
}
return persons;
}
// src/sample.ts
var face3 = `
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AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu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AJ1FzRKxDqGii6m3siiQ8F1XGfXI6YNWLfRbiRQMkcZI9fpTDluT2/h6Qy8gDPbtmtG38JeY480Z
5zSLUTZg8M28YwYxjAArXtdPt402qgHbpSaLWhma3o0Uqk7Nx9DWLaaVblgPs6qRyds2M/gRSQp9
zZOni2iWS2hlQ+kjYz9OMGrdjq89vIPPVhj+8M/lQyDq9P1WOYBlMZz1AOD+VdDaTiReOKulK0jO
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q4Em4Gkxk0yRGXrVW6i8yFhkg+tJjRxGsWrxllkUMh9eK5uMz6bcebbnfG33kPcVkay2OntPKuo0
nhXI67c8qa7Lw3c+adjcEDGK1paSRhVV4s6A0or0jyRRQ1AHX0V553hRQBz+vNtt5z3xXzX8Qbdm
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// src/warmup.ts
async function warmupBitmap(instance) {
const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
let blob;
let res;
switch (instance.config.warmup) {
case "face":
blob = await b64toBlob(face3);
break;
case "body":
case "full":
blob = await b64toBlob(body3);
break;
default:
blob = null;
}
if (blob) {
const bitmap = await createImageBitmap(blob);
res = await instance.detect(bitmap, instance.config);
bitmap.close();
}
return res;
}
async function warmupCanvas(instance) {
return new Promise((resolve) => {
let src;
switch (instance.config.warmup) {
case "face":
src = "data:image/jpeg;base64," + face3;
break;
case "full":
case "body":
src = "data:image/jpeg;base64," + body3;
break;
default:
src = "";
}
let img;
if (typeof Image !== "undefined")
img = new Image();
else if (env.Image)
img = new env.Image();
else {
resolve(void 0);
return;
}
img.onload = async () => {
const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
if (!canvas3) {
log("Warmup: Canvas not found");
resolve(void 0);
} else {
const ctx = canvas3.getContext("2d");
if (ctx)
ctx.drawImage(img, 0, 0);
const tensor = await instance.image(canvas3, true);
const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : void 0;
resolve(res);
}
};
if (src)
img.src = src;
else
resolve(void 0);
});
}
async function warmupNode(instance) {
const atob2 = (str) => Buffer.from(str, "base64");
let img;
if (instance.config.warmup === "face")
img = atob2(face3);
else
img = atob2(body3);
let res;
if ("node" in tfjs_esm_exports && Ime() === "tensorflow") {
const data = (void 0).decodeJpeg(img);
const expanded = oi(data, 0);
instance.tf.dispose(data);
res = await instance.detect(expanded, instance.config);
instance.tf.dispose(expanded);
} else {
if (instance.config.debug)
log("Warmup tfjs-node not loaded");
}
return res;
}
async function runInference(instance) {
let res;
if (typeof createImageBitmap === "function")
res = await warmupBitmap(instance);
else if (typeof Image !== "undefined" || env.Canvas !== void 0)
res = await warmupCanvas(instance);
else
res = await warmupNode(instance);
return res;
}
async function runCompile(instance) {
var _a2, _b, _c2, _d2;
if (!P().flagRegistry.ENGINE_COMPILE_ONLY)
return;
const backendType = Ime();
const webGLBackend = Tme();
if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) {
return;
}
P().set("ENGINE_COMPILE_ONLY", true);
const numTensorsStart = ur().state.numTensors;
const compiledModels = [];
for (const [modelName, model23] of Object.entries(instance.models.models)) {
if (!model23)
continue;
const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a2 = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a2[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3];
const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d2 = (_c2 = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c2[0]) == null ? void 0 : _d2.dtype) ? model23.inputs[0].dtype : "float32";
for (let dim = 0; dim < shape.length; dim++) {
if (shape[dim] === -1)
shape[dim] = dim === 0 ? 1 : 64;
}
const tensor = Wr(shape, dtype);
try {
const res = model23.execute(tensor);
compiledModels.push(modelName);
if (Array.isArray(res))
res.forEach((t10) => Ot(t10));
else
Ot(res);
} catch (e) {
if (instance.config.debug)
log("compile fail model:", modelName);
}
Ot(tensor);
}
const kernels = await webGLBackend["checkCompileCompletionAsync"]();
webGLBackend["getUniformLocations"]();
if (instance.config.debug)
log("compile pass:", { models: compiledModels, kernels: kernels.length });
P().set("ENGINE_COMPILE_ONLY", false);
const numTensorsEnd = ur().state.numTensors;
if (numTensorsEnd - numTensorsStart > 0)
log("tensor leak:", numTensorsEnd - numTensorsStart);
}
async function warmup(instance, userConfig) {
await check(instance, false);
const t0 = now();
instance.state = "warmup";
if (userConfig)
instance.config = mergeDeep(instance.config, userConfig);
if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") {
return empty();
}
return new Promise(async (resolve) => {
await instance.models.load();
await runCompile(instance);
const res = await runInference(instance);
const t12 = now();
if (instance.config.debug)
log("warmup", instance.config.warmup, Math.round(t12 - t0), "ms");
instance.emit("warmup");
resolve(res);
});
}
// src/human.ts
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops;
var Human = class {
// definition end
/** Constructor for **Human** library that is futher used for all operations
* @param userConfig - user configuration object {@link Config}
*/
constructor(userConfig) {
/** Current version of Human library in *semver* format */
__publicField(this, "version");
/** Current configuration
* - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
*/
__publicField(this, "config");
/** Last known result of detect run
* - Can be accessed anytime after initial detection
*/
__publicField(this, "result");
/** Current state of Human library
* - Can be polled to determine operations that are currently executed
* - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
*/
__publicField(this, "state");
/** currenty processed image tensor and canvas */
__publicField(this, "process");
/** Instance of TensorFlow/JS used by Human
* - Can be embedded or externally provided
* [TFJS API](https://js.tensorflow.org/api/latest/)
*/
__publicField(this, "tf");
/** Object containing environment information used for diagnostics */
__publicField(this, "env", env);
/** Draw helper classes that can draw detected objects on canvas using specified draw
* - canvas: draws input to canvas
* - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}
* - face, body, hand, gesture, object, person: draws detected results as overlays on canvas
*/
// draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };
__publicField(this, "draw", draw_exports);
/** Face Matching
* - similarity: compare two face descriptors and return similarity index
* - distance: compare two face descriptors and return raw calculated differences
* - find: compare face descriptor to array of face descriptors and return best match
*/
__publicField(this, "match", match_exports);
/** Currently loaded models
* @internal
* {@link models#Models}
*/
__publicField(this, "models");
/** Container for events dispatched by Human
* Possible events:
* - `create`: triggered when Human object is instantiated
* - `load`: triggered when models are loaded (explicitly or on-demand)
* - `image`: triggered when input image is processed
* - `result`: triggered when detection is complete
* - `warmup`: triggered when warmup is complete
* - `error`: triggered on some errors
*/
__publicField(this, "events");
/** Reference face triangualtion array of 468 points, used for triangle references between points */
__publicField(this, "faceTriangulation");
/** Refernce UV map of 468 values, used for 3D mapping of the face mesh */
__publicField(this, "faceUVMap");
/** Performance object that contains values for all recently performed operations */
__publicField(this, "performance");
// perf members are dynamically defined as needed
__privateAdd(this, _numTensors, void 0);
__privateAdd(this, _analyzeMemoryLeaks, void 0);
__privateAdd(this, _checkSanity, void 0);
/** internal function to measure tensor leaks */
__publicField(this, "analyze", (...msg) => {
if (!__privateGet(this, _analyzeMemoryLeaks))
return;
const currentTensors = this.tf.engine().state.numTensors;
const previousTensors = __privateGet(this, _numTensors);
__privateSet(this, _numTensors, currentTensors);
const leaked = currentTensors - previousTensors;
if (leaked !== 0)
log(...msg, leaked);
});
/** internal function for quick sanity check on inputs @hidden */
__privateAdd(this, _sanity, (input) => {
if (!__privateGet(this, _checkSanity))
return null;
if (!input)
return "input is not defined";
if (this.env.node && !(input instanceof pt))
return "input must be a tensor";
try {
this.tf.getBackend();
} catch (e) {
return "backend not loaded";
}
return null;
});
/** WebCam helper methods
*
*/
__publicField(this, "webcam", new WebCam());
/** emit event */
__publicField(this, "emit", (event) => {
var _a2;
if ((_a2 = this.events) == null ? void 0 : _a2.dispatchEvent)
this.events.dispatchEvent(new Event(event));
});
/** internal structure that keeps track of processed videos @hidden */
__privateAdd(this, _loops, {});
const tfVersion = (zpe.tfjs || Vj).replace(/-(.*)/, "");
config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
config.modelBasePath = env.browser ? "../models/" : "file://models/";
this.version = version;
Object.defineProperty(this, "version", { value: version });
this.config = JSON.parse(JSON.stringify(config));
Object.seal(this.config);
this.config.cacheModels = typeof indexedDB !== "undefined";
if (userConfig)
this.config = mergeDeep(this.config, userConfig);
setModelLoadOptions(this.config);
this.tf = tfjs_esm_exports;
this.state = "idle";
__privateSet(this, _numTensors, 0);
__privateSet(this, _analyzeMemoryLeaks, false);
__privateSet(this, _checkSanity, false);
this.performance = {};
this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0;
this.models = new Models(this);
init2();
this.result = empty();
this.process = { tensor: null, canvas: null };
this.faceTriangulation = triangulation;
this.faceUVMap = uvmap;
validateModel(this, null, "");
this.emit("create");
if (this.config.debug || this.env.browser)
log(`version: ${this.version}`);
if (this.config.debug)
log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
const envTemp = JSON.parse(JSON.stringify(this.env));
delete envTemp.kernels;
delete envTemp.initial;
delete envTemp.perfadd;
if (this.config.debug)
log("environment:", envTemp);
}
/** Reset configuration to default values */
reset() {
const currentBackend = this.config.backend;
this.config = JSON.parse(JSON.stringify(config));
this.config.backend = currentBackend;
reset();
env.initial = true;
}
/** Validate current configuration schema */
validate(userConfig) {
const msgs = validate(config, userConfig || this.config);
if (msgs.length === 0)
this.config = mergeDeep(this.config, userConfig);
return msgs;
}
/** Utility wrapper for performance.now() */
now() {
return now();
}
/** Process input as return canvas and tensor
*
* @param input - any input {@link Input}
* @param getTensor - should image processing also return tensor or just canvas
* Returns object with `tensor` and `canvas`
*/
image(input, getTensor = false) {
return process2(input, this.config, getTensor);
}
/** Segmentation method takes any input and returns RGBA tensor
* Note: Segmentation is not triggered as part of detect process
*
* @param input - {@link Input}
* Returns tensor which contains image data in RGBA format
*/
async segmentation(input, userConfig) {
var _a2, _b, _c2;
if (userConfig)
this.config = mergeDeep(this.config, userConfig);
if (!this.config.segmentation.enabled)
return null;
const processed = await process2(input, this.config);
if (!processed.tensor)
return null;
let tensor = null;
if ((_a2 = this.config.segmentation.modelPath) == null ? void 0 : _a2.includes("rvm"))
tensor = await predict20(processed.tensor, this.config);
if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet"))
tensor = await predict16(processed.tensor, this.config);
if ((_c2 = this.config.segmentation.modelPath) == null ? void 0 : _c2.includes("selfie"))
tensor = await predict21(processed.tensor, this.config);
Ot(processed.tensor);
return tensor;
}
/** Compare two input tensors for pixel similarity
* - use `human.image` to process any valid input and get a tensor that can be used for compare
* - when passing manually generated tensors:
* - both input tensors must be in format [1, height, width, 3]
* - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
* - return value is pixel similarity score normalized by input resolution and rgb channels
*/
compare(firstImageTensor, secondImageTensor) {
return compare(this.config, firstImageTensor, secondImageTensor);
}
/** Explicit backend initialization
* - Normally done implicitly during initial load phase
* - Call to explictly register and initialize TFJS backend without any other operations
* - Use when changing backend during runtime
*/
async init() {
await check(this, true);
await this.tf.ready();
reset();
}
/** Load method preloads all configured models on-demand
* - Not explicitly required as any required model is load implicitly on it's first run
*
* @param userConfig - {@link Config}
*/
async load(userConfig) {
this.state = "load";
const timeStamp = now();
const count2 = Object.values(this.models.models).filter((model23) => model23).length;
if (userConfig)
this.config = mergeDeep(this.config, userConfig);
if (this.env.initial) {
if (!await check(this, false))
log("error: backend check failed");
await Sme();
if (this.env.browser) {
if (this.config.debug)
log("configuration:", this.config);
if (this.config.debug)
log("tf flags:", this.tf.ENV.flags);
}
}
await this.models.load(this);
if (this.env.initial && this.config.debug)
log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
this.env.initial = false;
const loaded = Object.values(this.models.models).filter((model23) => model23).length;
if (loaded !== count2) {
this.models.validate();
this.emit("load");
}
const current = Math.trunc(now() - timeStamp);
if (current > (this.performance.loadModels || 0))
this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;
}
/** Runs interpolation using last known result and returns smoothened result
* Interpolation is based on time since last known result so can be called independently
*
* @param result - {@link Result} optional use specific result set to run interpolation on
* @returns result - {@link Result}
*/
next(result = this.result) {
return calc2(result, this.config);
}
/** Warmup method pre-initializes all configured models for faster inference
* - can take significant time on startup
* - only used for `webgl` and `humangl` backends
* @param userConfig - {@link Config}
* @returns result - {@link Result}
*/
async warmup(userConfig) {
const t0 = now();
const res = await warmup(this, userConfig);
const t12 = now();
this.performance.warmup = Math.trunc(t12 - t0);
return res;
}
/** Run detect with tensorflow profiling
* - result object will contain total exeuction time information for top-20 kernels
* - actual detection object can be accessed via `human.result`
*/
async profile(input, userConfig) {
const profile = await this.tf.profile(() => this.detect(input, userConfig));
const kernels = {};
let total = 0;
for (const kernel of profile.kernels) {
const ms2 = Number(kernel.kernelTimeMs) || 0;
if (kernels[kernel.name])
kernels[kernel.name] += ms2;
else
kernels[kernel.name] = ms2;
total += ms2;
}
const kernelArr = [];
Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 }));
for (const kernel of kernelArr) {
kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3;
kernel.time = Math.round(1e3 * kernel.time) / 1e3;
}
kernelArr.sort((a, b) => b.time - a.time);
kernelArr.length = 20;
return kernelArr;
}
/** Main detection method
* - Analyze configuration: {@link Config}
* - Pre-process input: {@link Input}
* - Run inference for all configured models
* - Process and return result: {@link Result}
*
* @param input - {@link Input}
* @param userConfig - {@link Config}
* @returns result - {@link Result}
*/
async detect(input, userConfig) {
this.state = "detect";
return new Promise(async (resolve) => {
var _a2, _b, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2;
this.state = "config";
let timeStamp;
this.config = mergeDeep(this.config, userConfig);
this.state = "check";
const error = __privateGet(this, _sanity).call(this, input);
if (error) {
log(error, input);
this.emit("error");
resolve(empty(error));
}
const timeStart = now();
await this.load();
timeStamp = now();
this.state = "image";
const img = await process2(input, this.config);
this.process = img;
this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
this.analyze("Get Image:");
if (!img.tensor) {
if (this.config.debug)
log("could not convert input to tensor");
this.emit("error");
resolve(empty("could not convert input to tensor"));
return;
}
this.emit("image");
timeStamp = now();
this.config.skipAllowed = await skip(this.config, img.tensor);
this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed;
if (!this.performance.totalFrames)
this.performance.totalFrames = 0;
if (!this.performance.cachedFrames)
this.performance.cachedFrames = 0;
this.performance.totalFrames++;
if (this.config.skipAllowed)
this.performance.cachedFrames++;
this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
this.analyze("Check Changed:");
let faceRes = [];
let bodyRes = [];
let handRes = [];
let objectRes = [];
this.state = "detect:face";
if (this.config.async) {
faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : [];
if (this.performance.face)
delete this.performance.face;
} else {
timeStamp = now();
faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : [];
this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1))
faceRes = await faceRes;
this.analyze("Start Body:");
this.state = "detect:body";
const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if ((_a2 = this.config.body.modelPath) == null ? void 0 : _a2.includes("posenet"))
bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : [];
else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : [];
else if ((_c2 = this.config.body.modelPath) == null ? void 0 : _c2.includes("efficientpose"))
bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : [];
else if ((_d2 = this.config.body.modelPath) == null ? void 0 : _d2.includes("movenet"))
bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : [];
if (this.performance.body)
delete this.performance.body;
} else {
timeStamp = now();
if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : [];
else if ((_f2 = this.config.body.modelPath) == null ? void 0 : _f2.includes("blazepose"))
bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : [];
else if ((_g2 = this.config.body.modelPath) == null ? void 0 : _g2.includes("efficientpose"))
bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : [];
else if ((_h2 = this.config.body.modelPath) == null ? void 0 : _h2.includes("movenet"))
bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
this.analyze("End Body:");
this.analyze("Start Hand:");
this.state = "detect:hand";
const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
if (this.config.async) {
if ((_j2 = (_i2 = this.config.hand.detector) == null ? void 0 : _i2.modelPath) == null ? void 0 : _j2.includes("handdetect"))
handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : [];
else if ((_l2 = (_k2 = this.config.hand.detector) == null ? void 0 : _k2.modelPath) == null ? void 0 : _l2.includes("handtrack"))
handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : [];
if (this.performance.hand)
delete this.performance.hand;
} else {
timeStamp = now();
if ((_n2 = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n2.includes("handdetect"))
handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : [];
else if ((_p2 = (_o2 = this.config.hand.detector) == null ? void 0 : _o2.modelPath) == null ? void 0 : _p2.includes("handtrack"))
handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : [];
this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
this.analyze("End Hand:");
this.analyze("Start Object:");
this.state = "detect:object";
if (this.config.async) {
if ((_q2 = this.config.object.modelPath) == null ? void 0 : _q2.includes("nanodet"))
objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : [];
else if ((_r2 = this.config.object.modelPath) == null ? void 0 : _r2.includes("centernet"))
objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : [];
if (this.performance.object)
delete this.performance.object;
} else {
timeStamp = now();
if ((_s2 = this.config.object.modelPath) == null ? void 0 : _s2.includes("nanodet"))
objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : [];
else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : [];
this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
}
this.analyze("End Object:");
this.state = "detect:await";
if (this.config.async)
[faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);
this.state = "detect:gesture";
let gestureRes = [];
if (this.config.gesture.enabled) {
timeStamp = now();
gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)];
if (!this.config.async)
this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
else if (this.performance.gesture)
delete this.performance.gesture;
}
this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
const shape = ((_u2 = this.process.tensor) == null ? void 0 : _u2.shape) || [0, 0, 0, 0];
this.result = {
face: faceRes,
body: bodyRes,
hand: handRes,
gesture: gestureRes,
object: objectRes,
performance: this.performance,
canvas: this.process.canvas,
timestamp: Date.now(),
error: null,
width: shape[2],
height: shape[1],
get persons() {
return join2(faceRes, bodyRes, handRes, gestureRes, shape);
}
};
Ot(img.tensor);
this.emit("detect");
this.state = "idle";
resolve(this.result);
});
}
/** Helper function
* @param ms - sleep time in miliseconds
*/
async sleep(ms2) {
return new Promise((resolve) => {
setTimeout(resolve, ms2);
});
}
/** Continously detect video frames
* @param element - HTMLVideoElement input
* @param run - boolean run continously or stop if already running, default true
* @param delay - number delay detection between frames for number of miliseconds, default 0
*/
async video(element, run = true, delay = 0) {
if (run) {
if (!__privateGet(this, _loops)[element.id]) {
if (this.config.debug)
log("video start", element.id);
__privateGet(this, _loops)[element.id] = true;
}
if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2)
await this.detect(element);
if (delay > 0)
await this.sleep(delay);
if (__privateGet(this, _loops)[element.id])
requestAnimationFrame(() => this.video(element, run, delay));
} else {
if (this.config.debug)
log("video stop", element.id);
__privateGet(this, _loops)[element.id] = false;
}
}
};
_numTensors = new WeakMap();
_analyzeMemoryLeaks = new WeakMap();
_checkSanity = new WeakMap();
_sanity = new WeakMap();
_loops = new WeakMap();
export {
Env,
Human,
Human as default,
config as defaults,
draw_exports as draw,
empty,
env,
match_exports as match,
models_exports2 as models
};
//# sourceMappingURL=human.esm.js.map
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